hypothesis also known as

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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What is a scientific hypothesis?

It's the initial building block in the scientific method.

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

• If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
• If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
• If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

• Read more about writing a hypothesis, from the American Medical Writers Association.
• Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
• Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject.

In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.

In the study of logic, a hypothesis is an if-then proposition, typically written in the form, "If X , then Y ."

In common usage, a hypothesis is simply a proposed explanation or prediction, which may or may not be tested.

Writing a Hypothesis

Most scientific hypotheses are proposed in the if-then format because it's easy to design an experiment to see whether or not a cause and effect relationship exists between the independent variable and the dependent variable . The hypothesis is written as a prediction of the outcome of the experiment.

• Null Hypothesis and Alternative Hypothesis

Statistically, it's easier to show there is no relationship between two variables than to support their connection. So, scientists often propose the null hypothesis . The null hypothesis assumes changing the independent variable will have no effect on the dependent variable.

In contrast, the alternative hypothesis suggests changing the independent variable will have an effect on the dependent variable. Designing an experiment to test this hypothesis can be trickier because there are many ways to state an alternative hypothesis.

For example, consider a possible relationship between getting a good night's sleep and getting good grades. The null hypothesis might be stated: "The number of hours of sleep students get is unrelated to their grades" or "There is no correlation between hours of sleep and grades."

An experiment to test this hypothesis might involve collecting data, recording average hours of sleep for each student and grades. If a student who gets eight hours of sleep generally does better than students who get four hours of sleep or 10 hours of sleep, the hypothesis might be rejected.

But the alternative hypothesis is harder to propose and test. The most general statement would be: "The amount of sleep students get affects their grades." The hypothesis might also be stated as "If you get more sleep, your grades will improve" or "Students who get nine hours of sleep have better grades than those who get more or less sleep."

In an experiment, you can collect the same data, but the statistical analysis is less likely to give you a high confidence limit.

Usually, a scientist starts out with the null hypothesis. From there, it may be possible to propose and test an alternative hypothesis, to narrow down the relationship between the variables.

Example of a Hypothesis

Examples of a hypothesis include:

• If you drop a rock and a feather, (then) they will fall at the same rate.
• Plants need sunlight in order to live. (if sunlight, then life)
• Eating sugar gives you energy. (if sugar, then energy)
• White, Jay D.  Research in Public Administration . Conn., 1998.
• Schick, Theodore, and Lewis Vaughn.  How to Think about Weird Things: Critical Thinking for a New Age . McGraw-Hill Higher Education, 2002.
• Null Hypothesis Definition and Examples
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• Six Steps of the Scientific Method
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• What Are Examples of a Hypothesis?
• Understanding Simple vs Controlled Experiments
• Scientific Method Flow Chart
• Scientific Method Vocabulary Terms
• What Is a Testable Hypothesis?
• Null Hypothesis Examples
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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

• proposition
• supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

• counter - hypothesis
• nebular hypothesis
• null hypothesis
• planetesimal hypothesis
• Whorfian hypothesis

Articles Related to hypothesis

This is the Difference Between a...

This is the Difference Between a Hypothesis and a Theory

In scientific reasoning, they're two completely different things

Dictionary Entries Near hypothesis

hypothermia

hypothesize

Cite this Entry

“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 1 May. 2024.

Kids Definition

Kids definition of hypothesis, medical definition, medical definition of hypothesis, more from merriam-webster on hypothesis.

Nglish: Translation of hypothesis for Spanish Speakers

Britannica English: Translation of hypothesis for Arabic Speakers

Britannica.com: Encyclopedia article about hypothesis

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13 Different Types of Hypothesis

There are 13 different types of hypothesis. These include simple, complex, null, alternative, composite, directional, non-directional, logical, empirical, statistical, associative, exact, and inexact.

A hypothesis can be categorized into one or more of these types. However, some are mutually exclusive and opposites. Simple and complex hypotheses are mutually exclusive, as are direction and non-direction, and null and alternative hypotheses.

Below I explain each hypothesis in simple terms for absolute beginners. These definitions may be too simple for some, but they’re designed to be clear introductions to the terms to help people wrap their heads around the concepts early on in their education about research methods .

Types of Hypothesis

Before you Proceed: Dependent vs Independent Variables

A research study and its hypotheses generally examine the relationships between independent and dependent variables – so you need to know these two concepts:

• The independent variable is the variable that is causing a change.
• The dependent variable is the variable the is affected by the change. This is the variable being tested.

Read my full article on dependent vs independent variables for more examples.

Example: Eating carrots (independent variable) improves eyesight (dependent variable).

1. Simple Hypothesis

A simple hypothesis is a hypothesis that predicts a correlation between two test variables: an independent and a dependent variable.

This is the easiest and most straightforward type of hypothesis. You simply need to state an expected correlation between the dependant variable and the independent variable.

You do not need to predict causation (see: directional hypothesis). All you would need to do is prove that the two variables are linked.

Simple Hypothesis Examples

2. complex hypothesis.

A complex hypothesis is a hypothesis that contains multiple variables, making the hypothesis more specific but also harder to prove.

You can have multiple independent and dependant variables in this hypothesis.

Complex Hypothesis Example

In the above example, we have multiple independent and dependent variables:

• Independent variables: Age and weight.
• Dependent variables: diabetes and heart disease.

Because there are multiple variables, this study is a lot more complex than a simple hypothesis. It quickly gets much more difficult to prove these hypotheses. This is why undergraduate and first-time researchers are usually encouraged to use simple hypotheses.

3. Null Hypothesis

A null hypothesis will predict that there will be no significant relationship between the two test variables.

For example, you can say that “The study will show that there is no correlation between marriage and happiness.”

A good way to think about a null hypothesis is to think of it in the same way as “innocent until proven guilty”[1]. Unless you can come up with evidence otherwise, your null hypothesis will stand.

A null hypothesis may also highlight that a correlation will be inconclusive . This means that you can predict that the study will not be able to confirm your results one way or the other. For example, you can say “It is predicted that the study will be unable to confirm a correlation between the two variables due to foreseeable interference by a third variable .”

Beware that an inconclusive null hypothesis may be questioned by your teacher. Why would you conduct a test that you predict will not provide a clear result? Perhaps you should take a closer look at your methodology and re-examine it. Nevertheless, inconclusive null hypotheses can sometimes have merit.

Null Hypothesis Examples

4. alternative hypothesis.

An alternative hypothesis is a hypothesis that is anything other than the null hypothesis. It will disprove the null hypothesis.

We use the symbol H A or H 1 to denote an alternative hypothesis.

The null and alternative hypotheses are usually used together. We will say the null hypothesis is the case where a relationship between two variables is non-existent. The alternative hypothesis is the case where there is a relationship between those two variables.

The following statement is always true: H 0 ≠ H A .

Let’s take the example of the hypothesis: “Does eating oatmeal before an exam impact test scores?”

We can have two hypotheses here:

• Null hypothesis (H 0 ): “Eating oatmeal before an exam does not impact test scores.”
• Alternative hypothesis (H A ): “Eating oatmeal before an exam does impact test scores.”

For the alternative hypothesis to be true, all we have to do is disprove the null hypothesis for the alternative hypothesis to be true. We do not need an exact prediction of how much oatmeal will impact the test scores or even if the impact is positive or negative. So long as the null hypothesis is proven to be false, then the alternative hypothesis is proven to be true.

5. Composite Hypothesis

A composite hypothesis is a hypothesis that does not predict the exact parameters, distribution, or range of the dependent variable.

Often, we would predict an exact outcome. For example: “23 year old men are on average 189cm tall.” Here, we are giving an exact parameter. So, the hypothesis is not composite.

But, often, we cannot exactly hypothesize something. We assume that something will happen, but we’re not exactly sure what. In these cases, we might say: “23 year old men are not on average 189cm tall.”

We haven’t set a distribution range or exact parameters of the average height of 23 year old men. So, we’ve introduced a composite hypothesis as opposed to an exact hypothesis.

Generally, an alternative hypothesis (discussed above) is composite because it is defined as anything except the null hypothesis. This ‘anything except’ does not define parameters or distribution, and therefore it’s an example of a composite hypothesis.

6. Directional Hypothesis

A directional hypothesis makes a prediction about the positivity or negativity of the effect of an intervention prior to the test being conducted.

Instead of being agnostic about whether the effect will be positive or negative, it nominates the effect’s directionality.

We often call this a one-tailed hypothesis (in contrast to a two-tailed or non-directional hypothesis) because, looking at a distribution graph, we’re hypothesizing that the results will lean toward one particular tail on the graph – either the positive or negative.

Directional Hypothesis Examples

7. non-directional hypothesis.

A non-directional hypothesis does not specify the predicted direction (e.g. positivity or negativity) of the effect of the independent variable on the dependent variable.

These hypotheses predict an effect, but stop short of saying what that effect will be.

A non-directional hypothesis is similar to composite and alternative hypotheses. All three types of hypothesis tend to make predictions without defining a direction. In a composite hypothesis, a specific prediction is not made (although a general direction may be indicated, so the overlap is not complete). For an alternative hypothesis, you often predict that the even will be anything but the null hypothesis, which means it could be more or less than H 0 (or in other words, non-directional).

Let’s turn the above directional hypotheses into non-directional hypotheses.

Non-Directional Hypothesis Examples

8. logical hypothesis.

A logical hypothesis is a hypothesis that cannot be tested, but has some logical basis underpinning our assumptions.

These are most commonly used in philosophy because philosophical questions are often untestable and therefore we must rely on our logic to formulate logical theories.

Usually, we would want to turn a logical hypothesis into an empirical one through testing if we got the chance. Unfortunately, we don’t always have this opportunity because the test is too complex, expensive, or simply unrealistic.

Here are some examples:

• Before the 1980s, it was hypothesized that the Titanic came to its resting place at 41° N and 49° W, based on the time the ship sank and the ship’s presumed path across the Atlantic Ocean. However, due to the depth of the ocean, it was impossible to test. Thus, the hypothesis was simply a logical hypothesis.
• Dinosaurs closely related to Aligators probably had green scales because Aligators have green scales. However, as they are all extinct, we can only rely on logic and not empirical data.

9. Empirical Hypothesis

An empirical hypothesis is the opposite of a logical hypothesis. It is a hypothesis that is currently being tested using scientific analysis. We can also call this a ‘working hypothesis’.

We can to separate research into two types: theoretical and empirical. Theoretical research relies on logic and thought experiments. Empirical research relies on tests that can be verified by observation and measurement.

So, an empirical hypothesis is a hypothesis that can and will be tested.

• Raising the wage of restaurant servers increases staff retention.
• Adding 1 lb of corn per day to cows’ diets decreases their lifespan.
• Mushrooms grow faster at 22 degrees Celsius than 27 degrees Celsius.

Each of the above hypotheses can be tested, making them empirical rather than just logical (aka theoretical).

10. Statistical Hypothesis

A statistical hypothesis utilizes representative statistical models to draw conclusions about broader populations.

It requires the use of datasets or carefully selected representative samples so that statistical inference can be drawn across a larger dataset.

This type of research is necessary when it is impossible to assess every single possible case. Imagine, for example, if you wanted to determine if men are taller than women. You would be unable to measure the height of every man and woman on the planet. But, by conducting sufficient random samples, you would be able to predict with high probability that the results of your study would remain stable across the whole population.

You would be right in guessing that almost all quantitative research studies conducted in academic settings today involve statistical hypotheses.

Statistical Hypothesis Examples

• Human Sex Ratio. The most famous statistical hypothesis example is that of John Arbuthnot’s sex at birth case study in 1710. Arbuthnot used birth data to determine with high statistical probability that there are more male births than female births. He called this divine providence, and to this day, his findings remain true: more men are born than women.
• Lady Testing Tea. A 1935 study by Ronald Fisher involved testing a woman who believed she could tell whether milk was added before or after water to a cup of tea. Fisher gave her 4 cups in which one randomly had milk placed before the tea. He repeated the test 8 times. The lady was correct each time. Fisher found that she had a 1 in 70 chance of getting all 8 test correct, which is a statistically significant result.

11. Associative Hypothesis

An associative hypothesis predicts that two variables are linked but does not explore whether one variable directly impacts upon the other variable.

We commonly refer to this as “ correlation does not mean causation ”. Just because there are a lot of sick people in a hospital, it doesn’t mean that the hospital made the people sick. There is something going on there that’s causing the issue (sick people are flocking to the hospital).

So, in an associative hypothesis, you note correlation between an independent and dependent variable but do not make a prediction about how the two interact. You stop short of saying one thing causes another thing.

Associative Hypothesis Examples

• Sick people in hospital. You could conduct a study hypothesizing that hospitals have more sick people in them than other institutions in society. However, you don’t hypothesize that the hospitals caused the sickness.
• Lice make you healthy. In the Middle Ages, it was observed that sick people didn’t tend to have lice in their hair. The inaccurate conclusion was that lice was not only a sign of health, but that they made people healthy. In reality, there was an association here, but not causation. The fact was that lice were sensitive to body temperature and fled bodies that had fevers.

12. Causal Hypothesis

A causal hypothesis predicts that two variables are not only associated, but that changes in one variable will cause changes in another.

A causal hypothesis is harder to prove than an associative hypothesis because the cause needs to be definitively proven. This will often require repeating tests in controlled environments with the researchers making manipulations to the independent variable, or the use of control groups and placebo effects .

If we were to take the above example of lice in the hair of sick people, researchers would have to put lice in sick people’s hair and see if it made those people healthier. Researchers would likely observe that the lice would flee the hair, but the sickness would remain, leading to a finding of association but not causation.

Causal Hypothesis Examples

13. exact vs. inexact hypothesis.

For brevity’s sake, I have paired these two hypotheses into the one point. The reality is that we’ve already seen both of these types of hypotheses at play already.

An exact hypothesis (also known as a point hypothesis) specifies a specific prediction whereas an inexact hypothesis assumes a range of possible values without giving an exact outcome. As Helwig [2] argues:

“An “exact” hypothesis specifies the exact value(s) of the parameter(s) of interest, whereas an “inexact” hypothesis specifies a range of possible values for the parameter(s) of interest.”

Generally, a null hypothesis is an exact hypothesis whereas alternative, composite, directional, and non-directional hypotheses are all inexact.

See Next: 15 Hypothesis Examples

This is introductory information that is basic and indeed quite simplified for absolute beginners. It’s worth doing further independent research to get deeper knowledge of research methods and how to conduct an effective research study. And if you’re in education studies, don’t miss out on my list of the best education studies dissertation ideas .

[1] https://jnnp.bmj.com/content/91/6/571.abstract

[2] http://users.stat.umn.edu/~helwig/notes/SignificanceTesting.pdf

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 5 Top Tips for Succeeding at University
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 50 Durable Goods Examples
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2 thoughts on “13 Different Types of Hypothesis”

Wow! This introductionary materials are very helpful. I teach the begginers in research for the first time in my career. The given tips and materials are very helpful. Chris, thank you so much! Excellent materials!

You’re more than welcome! If you want a pdf version of this article to provide for your students to use as a weekly reading on in-class discussion prompt for seminars, just drop me an email in the Contact form and I’ll get one sent out to you.

When I’ve taught this seminar, I’ve put my students into groups, cut these definitions into strips, and handed them out to the groups. Then I get them to try to come up with hypotheses that fit into each ‘type’. You can either just rotate hypothesis types so they get a chance at creating a hypothesis of each type, or get them to “teach” their hypothesis type and examples to the class at the end of the seminar.

Cheers, Chris

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• Scientific Methods

What is Hypothesis?

We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.

A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

• The hypothesis should be clear and precise to consider it to be reliable.
• If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables.
• The hypothesis must be specific and should have scope for conducting more tests.
• The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance.

Sources of Hypothesis

Following are the sources of hypothesis:

• The resemblance between the phenomenon.
• Observations from past studies, present-day experiences and from the competitors.
• Scientific theories.
• General patterns that influence the thinking process of people.

Types of Hypothesis

There are six forms of hypothesis and they are:

• Simple hypothesis
• Complex hypothesis
• Directional hypothesis
• Non-directional hypothesis
• Null hypothesis
• Associative and casual hypothesis

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Null Hypothesis

It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.

Examples of Hypothesis

Following are the examples of hypotheses based on their types:

• Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis.
• All lilies have the same number of petals is an example of a null hypothesis.
• If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis.

Functions of Hypothesis

Following are the functions performed by the hypothesis:

• Hypothesis helps in making an observation and experiments possible.
• It becomes the start point for the investigation.
• Hypothesis helps in verifying the observations.
• It helps in directing the inquiries in the right direction.

How will Hypothesis help in the Scientific Method?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Formation of question
• Doing background research
• Creation of hypothesis
• Designing an experiment
• Collection of data
• Result analysis
• Summarizing the experiment
• Communicating the results

Frequently Asked Questions – FAQs

What is hypothesis.

A hypothesis is an assumption made based on some evidence.

Give an example of simple hypothesis?

What are the types of hypothesis.

Types of hypothesis are:

• Associative and Casual hypothesis

State true or false: Hypothesis is the initial point of any investigation that translates the research questions into a prediction.

Define complex hypothesis..

A complex hypothesis shows the relationship between two or more dependent variables and two or more independent variables.

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Sapir–Whorf hypothesis (Linguistic Relativity Hypothesis)

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BA with minors in Psychology and Biology, MRes University of Edinburgh

Mia Belle Frothingham is a Harvard University graduate with a Bachelor of Arts in Sciences with minors in biology and psychology

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There are about seven thousand languages heard around the world – they all have different sounds, vocabularies, and structures. As you know, language plays a significant role in our lives.

But one intriguing question is – can it actually affect how we think?

It is widely thought that reality and how one perceives the world is expressed in spoken words and are precisely the same as reality.

That is, perception and expression are understood to be synonymous, and it is assumed that speech is based on thoughts. This idea believes that what one says depends on how the world is encoded and decoded in the mind.

However, many believe the opposite.

In that, what one perceives is dependent on the spoken word. Basically, that thought depends on language, not the other way around.

What Is The Sapir-Whorf Hypothesis?

Twentieth-century linguists Edward Sapir and Benjamin Lee Whorf are known for this very principle and its popularization. Their joint theory, known as the Sapir-Whorf Hypothesis or, more commonly, the Theory of Linguistic Relativity, holds great significance in all scopes of communication theories.

The Sapir-Whorf hypothesis states that the grammatical and verbal structure of a person’s language influences how they perceive the world. It emphasizes that language either determines or influences one’s thoughts.

The Sapir-Whorf hypothesis states that people experience the world based on the structure of their language, and that linguistic categories shape and limit cognitive processes. It proposes that differences in language affect thought, perception, and behavior, so speakers of different languages think and act differently.

For example, different words mean various things in other languages. Not every word in all languages has an exact one-to-one translation in a foreign language.

Because of these small but crucial differences, using the wrong word within a particular language can have significant consequences.

The Sapir-Whorf hypothesis is sometimes called “linguistic relativity” or the “principle of linguistic relativity.” So while they have slightly different names, they refer to the same basic proposal about the relationship between language and thought.

How Language Influences Culture

Culture is defined by the values, norms, and beliefs of a society. Our culture can be considered a lens through which we undergo the world and develop a shared meaning of what occurs around us.

The language that we create and use is in response to the cultural and societal needs that arose. In other words, there is an apparent relationship between how we talk and how we perceive the world.

One crucial question that many intellectuals have asked is how our society’s language influences its culture.

Linguist and anthropologist Edward Sapir and his then-student Benjamin Whorf were interested in answering this question.

Together, they created the Sapir-Whorf hypothesis, which states that our thought processes predominantly determine how we look at the world.

Our language restricts our thought processes – our language shapes our reality. Simply, the language that we use shapes the way we think and how we see the world.

Since the Sapir-Whorf hypothesis theorizes that our language use shapes our perspective of the world, people who speak different languages have different views of the world.

In the 1920s, Benjamin Whorf was a Yale University graduate student studying with linguist Edward Sapir, who was considered the father of American linguistic anthropology.

Sapir was responsible for documenting and recording the cultures and languages of many Native American tribes disappearing at an alarming rate. He and his predecessors were well aware of the close relationship between language and culture.

Anthropologists like Sapir need to learn the language of the culture they are studying to understand the worldview of its speakers truly. Whorf believed that the opposite is also true, that language affects culture by influencing how its speakers think.

His hypothesis proposed that the words and structures of a language influence how its speaker behaves and feels about the world and, ultimately, the culture itself.

Simply put, Whorf believed that you see the world differently from another person who speaks another language due to the specific language you speak.

Human beings do not live in the matter-of-fact world alone, nor solitary in the world of social action as traditionally understood, but are very much at the pardon of the certain language which has become the medium of communication and expression for their society.

To a large extent, the real world is unconsciously built on habits in regard to the language of the group. We hear and see and otherwise experience broadly as we do because the language habits of our community predispose choices of interpretation.

Studies & Examples

The lexicon, or vocabulary, is the inventory of the articles a culture speaks about and has classified to understand the world around them and deal with it effectively.

For example, our modern life is dictated for many by the need to travel by some vehicle – cars, buses, trucks, SUVs, trains, etc. We, therefore, have thousands of words to talk about and mention, including types of models, vehicles, parts, or brands.

The most influential aspects of each culture are similarly reflected in the dictionary of its language. Among the societies living on the islands in the Pacific, fish have significant economic and cultural importance.

Therefore, this is reflected in the rich vocabulary that describes all aspects of the fish and the environments that islanders depend on for survival.

For example, there are over 1,000 fish species in Palau, and Palauan fishers knew, even long before biologists existed, details about the anatomy, behavior, growth patterns, and habitat of most of them – far more than modern biologists know today.

Whorf’s studies at Yale involved working with many Native American languages, including Hopi. He discovered that the Hopi language is quite different from English in many ways, especially regarding time.

Western cultures and languages view times as a flowing river that carries us continuously through the present, away from the past, and to the future.

Our grammar and system of verbs reflect this concept with particular tenses for past, present, and future.

We perceive this concept of time as universal in that all humans see it in the same way.

Although a speaker of Hopi has very different ideas, their language’s structure both reflects and shapes the way they think about time. Seemingly, the Hopi language has no present, past, or future tense; instead, they divide the world into manifested and unmanifest domains.

The manifested domain consists of the physical universe, including the present, the immediate past, and the future; the unmanifest domain consists of the remote past and the future and the world of dreams, thoughts, desires, and life forces.

Also, there are no words for minutes, minutes, or days of the week. Native Hopi speakers often had great difficulty adapting to life in the English-speaking world when it came to being on time for their job or other affairs.

It is due to the simple fact that this was not how they had been conditioned to behave concerning time in their Hopi world, which followed the phases of the moon and the movements of the sun.

Today, it is widely believed that some aspects of perception are affected by language.

One big problem with the original Sapir-Whorf hypothesis derives from the idea that if a person’s language has no word for a specific concept, then that person would not understand that concept.

Honestly, the idea that a mother tongue can restrict one’s understanding has been largely unaccepted. For example, in German, there is a term that means to take pleasure in another person’s unhappiness.

While there is no translatable equivalent in English, it just would not be accurate to say that English speakers have never experienced or would not be able to comprehend this emotion.

Just because there is no word for this in the English language does not mean English speakers are less equipped to feel or experience the meaning of the word.

Not to mention a “chicken and egg” problem with the theory.

Of course, languages are human creations, very much tools we invented and honed to suit our needs. Merely showing that speakers of diverse languages think differently does not tell us whether it is the language that shapes belief or the other way around.

Supporting Evidence

On the other hand, there is hard evidence that the language-associated habits we acquire play a role in how we view the world. And indeed, this is especially true for languages that attach genders to inanimate objects.

There was a study done that looked at how German and Spanish speakers view different things based on their given gender association in each respective language.

The results demonstrated that in describing things that are referred to as masculine in Spanish, speakers of the language marked them as having more male characteristics like “strong” and “long.” Similarly, these same items, which use feminine phrasings in German, were noted by German speakers as effeminate, like “beautiful” and “elegant.”

The findings imply that speakers of each language have developed preconceived notions of something being feminine or masculine, not due to the objects” characteristics or appearances but because of how they are categorized in their native language.

It is important to remember that the Theory of Linguistic Relativity (Sapir-Whorf Hypothesis) also successfully achieves openness. The theory is shown as a window where we view the cognitive process, not as an absolute.

It is set forth to look at a phenomenon differently than one usually would. Furthermore, the Sapir-Whorf Hypothesis is very simple and logically sound. Understandably, one’s atmosphere and culture will affect decoding.

Likewise, in studies done by the authors of the theory, many Native American tribes do not have a word for particular things because they do not exist in their lives. The logical simplism of this idea of relativism provides parsimony.

Truly, the Sapir-Whorf Hypothesis makes sense. It can be utilized in describing great numerous misunderstandings in everyday life. When a Pennsylvanian says “yuns,” it does not make any sense to a Californian, but when examined, it is just another word for “you all.”

The Linguistic Relativity Theory addresses this and suggests that it is all relative. This concept of relativity passes outside dialect boundaries and delves into the world of language – from different countries and, consequently, from mind to mind.

Is language reality honestly because of thought, or is it thought which occurs because of language? The Sapir-Whorf Hypothesis very transparently presents a view of reality being expressed in language and thus forming in thought.

The principles rehashed in it show a reasonable and even simple idea of how one perceives the world, but the question is still arguable: thought then language or language then thought?

Modern Relevance

Regardless of its age, the Sapir-Whorf hypothesis, or the Linguistic Relativity Theory, has continued to force itself into linguistic conversations, even including pop culture.

The idea was just recently revisited in the movie “Arrival,” – a science fiction film that engagingly explores the ways in which an alien language can affect and alter human thinking.

And even if some of the most drastic claims of the theory have been debunked or argued against, the idea has continued its relevance, and that does say something about its importance.

Hypotheses, thoughts, and intellectual musings do not need to be totally accurate to remain in the public eye as long as they make us think and question the world – and the Sapir-Whorf Hypothesis does precisely that.

The theory does not only make us question linguistic theory and our own language but also our very existence and how our perceptions might shape what exists in this world.

There are generalities that we can expect every person to encounter in their day-to-day life – in relationships, love, work, sadness, and so on. But thinking about the more granular disparities experienced by those in diverse circumstances, linguistic or otherwise, helps us realize that there is more to the story than ours.

And beautifully, at the same time, the Sapir-Whorf Hypothesis reiterates the fact that we are more alike than we are different, regardless of the language we speak.

Isn’t it just amazing that linguistic diversity just reveals to us how ingenious and flexible the human mind is – human minds have invented not one cognitive universe but, indeed, seven thousand!

Kay, P., & Kempton, W. (1984). What is the Sapir‐Whorf hypothesis?. American anthropologist, 86(1), 65-79.

Whorf, B. L. (1952). Language, mind, and reality. ETC: A review of general semantics, 167-188.

Whorf, B. L. (1997). The relation of habitual thought and behavior to language. In Sociolinguistics (pp. 443-463). Palgrave, London.

Whorf, B. L. (2012). Language, thought, and reality: Selected writings of Benjamin Lee Whorf. MIT press.

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The Sapir-Whorf Hypothesis: How Language Influences How We Express Ourselves

Rachael is a New York-based writer and freelance writer for Verywell Mind, where she leverages her decades of personal experience with and research on mental illness—particularly ADHD and depression—to help readers better understand how their mind works and how to manage their mental health.

Thomas Barwick / Getty Images

What to Know About the Sapir-Whorf Hypothesis

Real-world examples of linguistic relativity, linguistic relativity in psychology.

The Sapir-Whorf Hypothesis, also known as linguistic relativity, refers to the idea that the language a person speaks can influence their worldview, thought, and even how they experience and understand the world.

While more extreme versions of the hypothesis have largely been discredited, a growing body of research has demonstrated that language can meaningfully shape how we understand the world around us and even ourselves.

Keep reading to learn more about linguistic relativity, including some real-world examples of how it shapes thoughts, emotions, and behavior.  

The hypothesis is named after anthropologist and linguist Edward Sapir and his student, Benjamin Lee Whorf. While the hypothesis is named after them both, the two never actually formally co-authored a coherent hypothesis together.

This Hypothesis Aims to Figure Out How Language and Culture Are Connected

Sapir was interested in charting the difference in language and cultural worldviews, including how language and culture influence each other. Whorf took this work on how language and culture shape each other a step further to explore how different languages might shape thought and behavior.

Since then, the concept has evolved into multiple variations, some more credible than others.

Linguistic Determinism Is an Extreme Version of the Hypothesis

Linguistic determinism, for example, is a more extreme version suggesting that a person’s perception and thought are limited to the language they speak. An early example of linguistic determinism comes from Whorf himself who argued that the Hopi people in Arizona don’t conjugate verbs into past, present, and future tenses as English speakers do and that their words for units of time (like “day” or “hour”) were verbs rather than nouns.

From this, he concluded that the Hopi don’t view time as a physical object that can be counted out in minutes and hours the way English speakers do. Instead, Whorf argued, the Hopi view time as a formless process.

This was then taken by others to mean that the Hopi don’t have any concept of time—an extreme view that has since been repeatedly disproven.

There is some evidence for a more nuanced version of linguistic relativity, which suggests that the structure and vocabulary of the language you speak can influence how you understand the world around you. To understand this better, it helps to look at real-world examples of the effects language can have on thought and behavior.

Different Languages Express Colors Differently

Color is one of the most common examples of linguistic relativity. Most known languages have somewhere between two and twelve color terms, and the way colors are categorized varies widely. In English, for example, there are distinct categories for blue and green .

Blue and Green

But in Korean, there is one word that encompasses both. This doesn’t mean Korean speakers can’t see blue, it just means blue is understood as a variant of green rather than a distinct color category all its own.

In Russian, meanwhile, the colors that English speakers would lump under the umbrella term of “blue” are further subdivided into two distinct color categories, “siniy” and “goluboy.” They roughly correspond to light blue and dark blue in English. But to Russian speakers, they are as distinct as orange and brown .

In one study comparing English and Russian speakers, participants were shown a color square and then asked to choose which of the two color squares below it was the closest in shade to the first square.

The test specifically focused on varying shades of blue ranging from “siniy” to “goluboy.” Russian speakers were not only faster at selecting the matching color square but were more accurate in their selections.

The Way Location Is Expressed Varies Across Languages

This same variation occurs in other areas of language. For example, in Guugu Ymithirr, a language spoken by Aboriginal Australians, spatial orientation is always described in absolute terms of cardinal directions. While an English speaker would say the laptop is “in front of” you, a Guugu Ymithirr speaker would say it was north, south, west, or east of you.

As a result, Aboriginal Australians have to be constantly attuned to cardinal directions because their language requires it (just as Russian speakers develop a more instinctive ability to discern between shades of what English speakers call blue because their language requires it).

So when you ask a Guugu Ymithirr speaker to tell you which way south is, they can point in the right direction without a moment’s hesitation. Meanwhile, most English speakers would struggle to accurately identify South without the help of a compass or taking a moment to recall grade school lessons about how to find it.

The concept of these cardinal directions exists in English, but English speakers aren’t required to think about or use them on a daily basis so it’s not as intuitive or ingrained in how they orient themselves in space.

Just as with other aspects of thought and perception, the vocabulary and grammatical structure we have for thinking about or talking about what we feel doesn’t create our feelings, but it does shape how we understand them and, to an extent, how we experience them.

Words Help Us Put a Name to Our Emotions

For example, the ability to detect displeasure from a person’s face is universal. But in a language that has the words “angry” and “sad,” you can further distinguish what kind of displeasure you observe in their facial expression. This doesn’t mean humans never experienced anger or sadness before words for them emerged. But they may have struggled to understand or explain the subtle differences between different dimensions of displeasure.

In one study of English speakers, toddlers were shown a picture of a person with an angry facial expression. Then, they were given a set of pictures of people displaying different expressions including happy, sad, surprised, scared, disgusted, or angry. Researchers asked them to put all the pictures that matched the first angry face picture into a box.

The two-year-olds in the experiment tended to place all faces except happy faces into the box. But four-year-olds were more selective, often leaving out sad or fearful faces as well as happy faces. This suggests that as our vocabulary for talking about emotions expands, so does our ability to understand and distinguish those emotions.

But some research suggests the influence is not limited to just developing a wider vocabulary for categorizing emotions. Language may “also help constitute emotion by cohering sensations into specific perceptions of ‘anger,’ ‘disgust,’ ‘fear,’ etc.,” said Dr. Harold Hong, a board-certified psychiatrist at New Waters Recovery in North Carolina.

As our vocabulary for talking about emotions expands, so does our ability to understand and distinguish those emotions.

Words for emotions, like words for colors, are an attempt to categorize a spectrum of sensations into a handful of distinct categories. And, like color, there’s no objective or hard rule on where the boundaries between emotions should be which can lead to variation across languages in how emotions are categorized.

Emotions Are Categorized Differently in Different Languages

Just as different languages categorize color a little differently, researchers have also found differences in how emotions are categorized. In German, for example, there’s an emotion called “gemütlichkeit.”

While it’s usually translated as “cozy” or “ friendly ” in English, there really isn’t a direct translation. It refers to a particular kind of peace and sense of belonging that a person feels when surrounded by the people they love or feel connected to in a place they feel comfortable and free to be who they are.

Harold Hong, MD, Psychiatrist

The lack of a word for an emotion in a language does not mean that its speakers don't experience that emotion.

You may have felt gemütlichkeit when staying up with your friends to joke and play games at a sleepover. You may feel it when you visit home for the holidays and spend your time eating, laughing, and reminiscing with your family in the house you grew up in.

In Japanese, the word “amae” is just as difficult to translate into English. Usually, it’s translated as "spoiled child" or "presumed indulgence," as in making a request and assuming it will be indulged. But both of those have strong negative connotations in English and amae is a positive emotion .

Instead of being spoiled or coddled, it’s referring to that particular kind of trust and assurance that comes with being nurtured by someone and knowing that you can ask for what you want without worrying whether the other person might feel resentful or burdened by your request.

You might have felt amae when your car broke down and you immediately called your mom to pick you up, without having to worry for even a second whether or not she would drop everything to help you.

Regardless of which languages you speak, though, you’re capable of feeling both of these emotions. “The lack of a word for an emotion in a language does not mean that its speakers don't experience that emotion,” Dr. Hong explained.

What This Means For You

“While having the words to describe emotions can help us better understand and regulate them, it is possible to experience and express those emotions without specific labels for them.” Without the words for these feelings, you can still feel them but you just might not be able to identify them as readily or clearly as someone who does have those words. 

Rhee S. Lexicalization patterns in color naming in Korean . In: Raffaelli I, Katunar D, Kerovec B, eds. Studies in Functional and Structural Linguistics. Vol 78. John Benjamins Publishing Company; 2019:109-128. Doi:10.1075/sfsl.78.06rhe

Winawer J, Witthoft N, Frank MC, Wu L, Wade AR, Boroditsky L. Russian blues reveal effects of language on color discrimination . Proc Natl Acad Sci USA. 2007;104(19):7780-7785.  10.1073/pnas.0701644104

Lindquist KA, MacCormack JK, Shablack H. The role of language in emotion: predictions from psychological constructionism . Front Psychol. 2015;6. Doi:10.3389/fpsyg.2015.00444

By Rachael Green Rachael is a New York-based writer and freelance writer for Verywell Mind, where she leverages her decades of personal experience with and research on mental illness—particularly ADHD and depression—to help readers better understand how their mind works and how to manage their mental health.

Definition : Hypothesis/Also known as

Hypothesis : also known as.

For hypothesis , the word supposition is also found.

The word conjecture is frequently encountered, usually in the context of a statement whose truth value has remained unresolved for a considerable time after the passing of the one who first raised the question.

However, this usage is inconsistent.

The term open question is also encountered, usually in the context in which there is no evidence in either direction as to whether the statement is true or false .

Linguistic Note

The word hypothesis is pronounced hy- po -the-sis , the stress going on the second syllable.

Its plural is hypotheses , which is pronounced hy- po -the-seez .

The word hypothesis comes from the Greek for supposition , literally to put under , that is sub-position .

The idea is that one puts an idea under scrutiny .

The verb hypothesize (British English: hypothesise ) means to make a hypothesis , that is, to suppose .

The adjective hypothetical means having the nature of a hypothesis .

A hypothetical question is a question which relates to a situation that is supposed (or pretended) to be imaginary. One would, for example, announce that a question about to be posed is hypothetical if the questioner wishes to be believed to be at some distance from the possibility of actually being the subject of the question.

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• Published: 17 April 2024

Decline of a distinct coral reef holobiont community under ocean acidification

• Jake Williams 1 , 2 ,
• Nathalie Pettorelli 2 ,
• Aaron C. Hartmann 3 ,
• Robert A. Quinn 4 ,
• Laetitia Plaisance 5 , 6 ,
• Michael O’Mahoney 6 ,
• Chris P. Meyer 6 ,
• Katharina E. Fabricius 7 ,
• Nancy Knowlton 6 &
• Emma Ransome 1  

Microbiome volume  12 , Article number:  75 ( 2024 ) Cite this article

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Microbes play vital roles across coral reefs both in the environment and inside and upon macrobes (holobionts), where they support critical functions such as nutrition and immune system modulation. These roles highlight the potential ecosystem-level importance of microbes, yet most knowledge of microbial functions on reefs is derived from a small set of holobionts such as corals and sponges. Declining seawater pH — an important global coral reef stressor — can cause ecosystem-level change on coral reefs, providing an opportunity to study the role of microbes at this scale. We use an in situ experimental approach to test the hypothesis that under such ocean acidification (OA), known shifts among macrobe trophic and functional groups may drive a general ecosystem-level response extending across macrobes and microbes, leading to reduced distinctness between the benthic holobiont community microbiome and the environmental microbiome.

We test this hypothesis using genetic and chemical data from benthic coral reef community holobionts sampled across a pH gradient from CO 2 seeps in Papua New Guinea. We find support for our hypothesis; under OA, the microbiome and metabolome of the benthic holobiont community become less compositionally distinct from the sediment microbiome and metabolome, suggesting that benthic macrobe communities are colonised by environmental microbes to a higher degree under OA conditions. We also find a simplification and homogenisation of the benthic photosynthetic community, and an increased abundance of fleshy macroalgae, consistent with previously observed reef microbialisation.

Conclusions

We demonstrate a novel structural shift in coral reefs involving macrobes and microbes: that the microbiome of the benthic holobiont community becomes less distinct from the sediment microbiome under OA. Our findings suggest that microbialisation and the disruption of macrobe trophic networks are interwoven general responses to environmental stress, pointing towards a universal, undesirable, and measurable form of ecosystem change.

Video Abstract

Increased atmospheric greenhouse gas concentrations are leading to increased partial pressure of CO 2 (pCO 2 ) and reduced pH in the surface water of the oceans, which have absorbed 25% of all anthropogenic CO 2 to date [ 1 ]. The impacts of this phenomenon, known as ocean acidification (OA) [ 2 ], are predicted to be particularly severe for coral reefs due to declines in net calcification by organisms, which impacts reef structure and diversity [ 3 ]. Severe impacts on coral reefs are concerning as these ecosystems host vast biodiversity and provide significant ecosystem services to humanity (e.g. coastal protection, food security, and new medicines), and other significant terrestrial and pelagic ocean ecosystems are also directly dependent on their functioning [ 4 ]. Being able to predict how coral ecosystems change under OA, and what this means for their ability to continue providing ecosystem services to society, is therefore a global environmental and social priority.

Microbes play important roles on coral reefs, from carrying out nutrient cycling, which contributes to ecosystem productivity in nutrient poor waters [ 5 ], to their roles in immunity and defence for a wide range of reef invertebrates, including cnidarians, sponges, molluscs, and echinoderms [ 6 , 7 , 8 , 9 ]. These interactions between macrobes and microbes are known to shift in response to environmental change, such as OA. Such shifts include changing microbial associations with particular macrobes, such as corals [ 10 , 11 ] and sponges [ 12 ], and changes in the functional profile of specific microbiomes, such as alterations in nitrogen (N 2 ) fixation by coral-associated bacteria [ 13 ] and shifting metabolic activity of free-living bacteria in the water column [ 14 ]. However, the traditional focus of research on individual macrobes and their microbiomes (holobionts) makes it challenging to scale up our understanding to ecosystem-level shifts in macrobe-microbe interactions, which, though often overlooked, can play a significant role in driving ecosystem-level change [ 11 , 15 , 16 ].

Ecosystem-level impacts of OA are expected to result from interaction-mediated changes at the community level, driven by different physiochemical effects of OA at the level of organism metabolism [ 2 , 17 ]. Well-established organism-level effects of OA include benefits to fleshy algae and other photosynthetic organisms from the resource effect of increased pCO 2 [ 2 , 18 ], which can result in enhanced net dissolved organic carbon (DOC) release [ 19 ]. In contrast, calcifiers suffer from increased costs of calcification due to reduced pH [ 2 ]. These differentiated organism-level impacts can shift ecological interactions between taxa [ 2 , 17 ], with cascading effects on energy flows through an ecosystem via altered nutrition and metabolism (i.e. altered trophodynamics [ 20 , 21 ]). Such indirect cascading effects can impact entire trophic networks [ 22 ], including macrobe-microbe trophic interactions [ 23 ]. However, our understanding of the indirect effects of ocean acidification on coral reefs remains limited [ 24 ].

Here, we propose that a previously observed ecosystem-level impact of specific stressors on coral reefs — microbialisation — may be generalisable to OA. Microbialisation refers to an increase in microbial biomass resulting from a reallocation of energy from macrobes to microbes [ 23 , 25 ]. On coral reefs, the proximal causes of microbialisation have been proposed to be overfishing and eutrophication, which facilitate the enhanced growth of fleshy algae and cause an increased release of dissolved organic carbon (DOC) [ 26 ]. Elevated DOC has been proposed to increase microbial biomass and disease (the DDAM (DOC, disease, algae, microbes) positive feedback loop) [ 26 ].

We build on a commonality between proposed organism-level mechanisms of microbialisation, namely that macrobe communities are expected to be more vulnerable to stressors than microbial communities [ 26 , 27 ], an observation both supported in general [ 28 ] and in the case of OA in particular [ 29 ]. This greater vulnerability of macrobes should lead to declines in some benthic taxa, such as calcified algae, soft and hard corals, sponges [ 18 ], and calcified grazers (e.g. gastropods [ 30 ]), already documented with OA, and to the disruption of associated macrobe trophic pathways. Examples of these trophic pathways include calcified grazers consuming algal communities (which controls algal proliferation) [ 31 ], and sponges removing vast quantities of dissolved organic matter (DOM) from the water column and converting it into food for higher trophic levels (e.g. polychaetes and brittle stars) via the sponge loop [ 32 ]. With declines in benthic taxa, some of the free energy cycled through such pathways can become available to less impacted taxa, such as environmental microbes [ 20 , 33 ], and is expected to drive the ecological release of such taxa in both density and niche expansion [ 34 ].

We hypothesise that we will observe a decline in the compositional and functional distinctness between the benthic holobiont community microbiome and the environmental sediment-dwelling microbiome as a result of microbialisation occurring under OA. Almost all macrobes are holobionts with a symbiotic microbiome [ 35 , 36 ], and therefore, microbialisation has the potential to impact the microbiome of the entire holobiont community (recently referred to as the eco-holobiont [ 37 ]). The process of microbialisation should result in decreased compositional and functional distinctness between the benthic holobiont community microbiome and the sediment microbiome through two mechanisms. Firstly, direct impacts on macrobes may alter host metabolism and reduce the resources or habitat available to the holobiont community microbiome, leading to opportunistic environmental microbes displacing holobiont-specialised microbes (e.g. [ 38 ]). Secondly, increased abundances of environmental microbes and increased microbe trophic interactions with macrobes (expected due to the loss of macrobe competitors [ 39 ]) should lead to increased opportunities for colonisation of the holobiome by environmental microbes [ 40 ], including those in the sediment microbiome.

To test whether this decline in the distinctness of the benthic holobiont community microbiome occurs, we generated a unique multiomic dataset from autonomous reef monitoring structures (ARMS) deployed on a natural OA gradient caused by CO 2 seeps. ARMS are three-dimensional, artificial settlement structures designed to mimic the structural complexity of coral reef environments, which are increasingly used to monitor coral reefs across the globe (e.g. [ 41 , 42 , 43 ]). They enable the non-destructive and standardised sampling of a large proportion of reef diversity that is often not studied, including algae and cryptic benthic invertebrates such as sponges, cnidarians, bryozoans, and annelids [ 44 ], alongside their associated microbes, and sediment-dwelling environmental microbes. ARMS allow us to investigate OA using a holistic microbial ecosystem approach that integrates across scales from individual microbes and benthic holobionts, to neighbouring holobionts that, in turn, interact with and influence successively larger and more complex communities. We studied the effects of OA using an in situ experimental approach at a location with naturally occurring CO 2 seeps that produce pH and pCO 2 gradients. These seeps have been intensively studied because they can help predict future ocean conditions under OA [ 45 ].

Multiomics, in this case metabarcoding and metabolomics, provides a powerful toolkit to investigate the effects of stressors across entire communities. Metabarcoding provides data on genetic community composition and diversity (i.e. compositional metrics) [ 46 ], while metabolomics provides comparable data on biochemical composition and diversity of the metabolome (i.e. functional metrics) [ 47 ]. We first confirm that the expected photosynthetic community shifts take place (as previously documented [ 18 ]), consistent with microbialisation. We then analyse the ecosystem-level effects of OA by comparing the distinctness of the sediment microbiome to: (1) the benthic holobiont community microbiome and (2) individual sponge microbiomes. We expected OA to cause a decline in distinctness as a result of ecosystem microbialisation.

Materials and methods

Experimental design and sampling.

This study was carried out at CO 2 seeps and adjacent control sites in Milne Bay Province, Papua New Guinea (Fig.  1 ), located at 9° s latitude in the heart of the Coral Triangle. The two studied seep localities (Upa-Upasina and Dobu) are located along an active tectonic fault where > 99% CO 2 gas has been streaming though the reef substrata at ambient temperature (28.6–29.7 °C) for at least 100 years and probably much longer [ 18 ]. The reefs surrounding the seeps are under low anthropogenic pressure and have been used to study ocean acidification for the last decade (e.g. [ 18 , 48 ]). The six study sites (two localities, each with three pH levels) exhibit similar geomorphology, temperature and salinity, but contrasting pCO 2 and pH [ 18 ]. Water temperature, pH, salinity, and pressure at the study sites have been monitored regularly (2010–2016), making this an ideal location to study the isolated impacts of OA.

Location of the study localities, Dobu and Upa-Upasina, in Milne Bay Province, Papua New Guinea ( A & B )

In April, 2012, eighteen autonomous reef monitoring structures (ARMS; Fig.  2 A) were deployed at 3 m depth adjacent to coral reefs at 3 pH levels at each of the 2 localities (mean pH: control 7.99 & 8.01, medium 7.85 at both localities, and low 7.64 & 7.75; n  = 3 per pH level [ 48 ]). ARMS were collected from the seafloor (Fig.  2 B) after 31 months, in November 2014. A 106-μm nitex-lined crate was placed over each ARMS on the seafloor, and they were together returned to the surface, after which each ARMS was placed in an individual holding tank with 45-μm filtered aerated seawater. ARMS were then transported to shore where they were sampled rapidly to minimise molecular and chemical degradation; transportation time of ARMS was < 20 min, and processing of each sample type was completed within 1.5 h.

Images of Autonomous Reef Monitoring Structures (ARMS) and ARMS plates. Images show an ARMS in situ ( A ) and nestled within the reef after 2.5 years of deployment ( B ). A light-exposed ARMS top plate, from which the benthic photosynthetic community was sampled, can be seen in ( C ). A Tethya sp. sponge (1) and a Halisarca sp. sponge (2), commonly observed on recovered ARMS, can be seen in ( D ). An internal light-limited ARMS plate with crossbars, which create sheltered conditions and mimic the natural reef, can be seen in ( E )

Sample extraction and multiomics

The standard ARMS processing protocol [ 44 ] was modified to test our specific hypothesis. From each ARMS unit, five fractions were collected: the benthic photosynthetic community, the benthic holobiont community, the sediment, Halisarca sp. sponge, and Tethya sp. sponge. To do this, ARMS were removed from their holding tanks, and the 9 plates (17 plate surfaces as there is no accessible bottom surface to the bottom plate) were separated and rinsed to dislodge loosely attached organisms. The water and previously trapped sediment in the holding tank were retained.

First, the benthic photosynthetic community was sampled by randomly subsampling (4 × 1 cm 2 ) the top surface of the top ARMS plate (e.g. Figure  2 C), which resembles the algal community found on exposed rocky substrates (e.g. macroalgae, algal turf, calcified algae, and cyanobacterial mats). Second, the two sponge fractions ( Halisarca sp. and Tethya sp., e.g. Figure  2 D) were generated by sampling morphologically identified sponges from the internal plates. Both are low-microbial abundance sponges [ 49 , 50 ], and Tethya sp. was only found at Upa-Upasina. Thirdly, the benthic holobiont community fraction was generated by scraping, and blending the scrapings from all 17 surfaces, including the remainder of the light-exposed top surface of the top plate. While this fraction will include both photosynthetic and non-photosynthetic organisms, the shaded plate surfaces constitute ~ 16 × the area of the light-exposed top surface of the top plate. The most abundant phyla found in this fraction on other reefs around the world are fairly consistent and include the Porifera, Cnidaria, Bryozoa, Chordata (Ascidiacea), and Annelida [ 42 , 43 , 44 ]. An example of an ARMS plate from which this fraction was collected can be seen in Fig.  2 E. This homogenised bulk sample was then subsampled (50 ml). Finally, the sediment fraction was generated by passing the water and sediment from the holding tank through a 500 μm and then a 100-μm sieve and collecting the material which did not pass through the 100-μm sieve. This drained sediment sample was subsampled (10 g). This fraction was therefore primarily composed of sediment, microbes, including free-living sediment dwelling microbes (e.g. bacteria) and microplankton (e.g. single-celled algae such as diatoms and dinoflagellates).

From each ARMS unit, each fraction was split in two; one-half was snap frozen for metabolomic analysis by being dropped in liquid nitrogen in a dry shipper, and the other was placed in RNA later for metabarcoding. All samples were returned to the Smithsonian National Museum of Natural History (Washington, DC, USA) in a liquid nitrogen dry shipper. Total DNA was extracted from 10 g of the benthic holobiont community samples and 5 g of the sediment samples using a MO-BIO PowerMax Soil DNA Isolation Kit according to the manufacturer’s protocol with the addition of 400 μg/ml proteinase K and an overnight lysis step at 56 °C and 200 rpm. The benthic photosynthetic community and sponge subsamples were extracted with the DNeasy Blood and Tissue Kit (Qiagen), according to the manufacturer’s instructions. All DNA extracts were purified using MO-BIO PowerClean DNA Clean-Up Kits, quantified Qubit dsDNA HS Kit, run on an agarose gel, and DNA quality investigated using ImageJ software. All sample types are known to contain relatively high bacterial biomass, and thus, we do not expect contamination from the lab environment or equipment to be a major issue in DNA libraries. However, extraction and PCR amplification controls were included for all sample types; these were all negative and so were not sequenced. Each sample was analysed with 16S rRNA gene metabarcoding (for the microbe community) and mass spectrometry (for metabolomics). All 16S rRNA gene libraries were prepared for sequencing using the original Earth Microbiome Project protocol using primers 515 F and 806 R, which are designed to amplify prokaryotes (Bacteria and Archaea) [ 51 ]. To investigate the benthic photosynthetic community, 23S rRNA gene libraries were also prepared using the protocol described by Marcelino and Verbruggen [ 52 ] using a two-step PCR procedure that first amplifies the gene fragment followed by ligation of the barcoded Illumina adaptors to the amplicons in a second PCR reaction; this protocol is designed to target both eukaryotic algae and cyanobacteria.

Metabolites were extracted from all fractions in 70% methanol [ 47 ]. The 70% methanol extraction was chosen to select for slightly polar molecules, encompassing a broad range of the chemosphere [ 53 ]. Metabolites were separated and identified via liquid chromatography-tandem mass spectrometry using a Bruker Daltonics Maxis qTOF mass spectrometer equipped with a standard electrospray ionisation source according to the methods of Quinn and colleagues [ 53 ]⁠. Briefly, the mobile phase was pumped through a Kinetex 2.6 μm C18 (30 × 2.10 mm) ultra-performance liquid chromatography (UPLC) column for a 15-min run. The resulting LC–MS/MS data files were processed through the MZmine2 workflow. The subsequent metabolite feature table was then processed through the GNPS feature-based molecular networking workflow with the default parameters, except that a minimum cosine of 0.65 and a minimum matched peaks of 4 were used for network construction.

Bioinformatics

A bioinformatic pipeline was implemented in R for the 16S and 23S rRNA gene libraries. Amplicon sequence variants (ASVs) were generated from raw sequencing data using the Divisive Amplicon Denoising Algorithm (DADA2 v1.24.0 [ 54 ]). Reads were quality filtered to maintain Q30 scores while maintaining at least 50 base pair overlap and removing any base pair below Q2 [ 55 ]. Default maxEE (2) and truncQ (2) parameters were used, 16S rRNA gene sequences were truncated at a length of 150 base pairs on both strands, 23S rRNA gene sequences had the first 20 base pairs (nonbiological primers) trimmed from both strands and were truncated at a length of 249 base pairs on the forward strand and 212 base pairs on the reverse strand (see Table S 1  for numbers of sequences passing denoising steps). Taxonomy was assigned using the DECIPHER v2.24.0 R package [ 56 ] — which has been shown to have higher accuracy than popular classifiers including BLAST and the RDP classifier [ 56 ] — and the GTDB (16S rRNA gene [ 57 ]) and microgreen (23S r RNA gene [ 58 ]) databases. 16S rRNA gene ASVs identified as plastids were subsequently removed. Samples were not rarefied as part of bioinformatic processing [ 59 ] but only when required for specific statistical analyses (see ASV-level Shannon diversity below).

Statistical analyses

PERMANOVAs were performed for each fraction separately, with the 16S rRNA gene sequencing and metabolomic data subdivided into community fractions (benthic photosynthetic community, benthic holobiont community and sediment) and organism fractions ( Halisarca sp. and Tethya sp. sponges), resulting in 10 PERMANOVAs (Table S 2 ). Prior to fitting PERMANOVAs, a multivariate analogue of Levene’s test for homogeneity of variances (betadisper) was applied to ensure PERMANOVA tests could be applied. PERMANOVAs fit locality and ordinal pH as explanatory variables, and locality was treated as a blocking factor, except in the case of the Tethya sp. sponge which was only found at one locality and so was fit with pH as the only explanatory variable. An eleventh PERMANOVA was run on the 23S rRNA gene data, following the same approach. Bonferroni corrections were applied to all p -values obtained from the PERMANOVAs to account for multiple testing. All PERMANOVAs and supporting NMDS visualisations were based on Morisita dissimilarities between samples, as they have been shown to be most reliable in the case of under sampling [ 60 ].⁠

Total ASV richness and phylum level Shannon diversity, each accounting for unobserved ASVs, were estimated for each metabarcoding sample using a breakaway model [ 61 ] and a DivNet model, treating all samples as independent observations, respectively [ 62 ]. The estimated richness and estimated Shannon diversity of all metabarcoding samples were then modelled using a single betta hierarchical mixed model for each metric (including all fractions). This modelling approach was chosen to account for explanatory variables, richness variance, and richness estimation error [ 61 ]. Fraction, ordinal pH, and the interaction of fraction and pH were included as fixed effects and locality as a random effect. Compound richness and Shannon diversity were calculated from untransformed data for metabolomic samples from all fractions. Each metric was modelled with a single linear mixed model. Fraction, pH, and the interaction of fraction and ordinal pH were included as fixed effects and locality as a random effect. In addition, ASV level Shannon diversity was calculated (no statistical estimation procedure was applied) for all samples rarefied to even depth ( n  = 50,000) to test the sensitivity of the results found for phylum level Shannon diversity.

The change in abundance of phyla and metabolites with OA was analysed using DeSeq2 differential abundance analysis with a negative binomial distribution [ 63 ]⁠. Differential abundance and dispersions were calculated for each community fraction (benthic photosynthetic community, benthic holobiont community, and sediment) and multiomic analysis type separately using a DESeq2 design formula with variables of locality and pH. This enabled change within each community fraction to be examined. However, abundance and dispersions were calculated for both sponge fractions together using a design formula with variables of species, locality, and pH. This enabled shared change occurring across sponges under OA to be examined. Wald significance tests were conducted for changes in differential abundance under OA, with a parametric fit of dispersions [ 63 ].

Microbiome/metabolome distinctness was calculated for each ARMS as the proportion of unique sequences found within the benthic holobiont community microbiome/metabolome which were not also found in the sediment microbiome/metabolome. Microbiome/metabolome distinctness was modelled using a linear mixed model with ordinal pH as a fixed effect and locality as a random effect. A likelihood ratio test was used to infer the significance of ordinal pH as a fixed effect. Note that this analysis was not conducted for the benthic photosynthetic community as we are testing whether distinctness is reduced for the general community of macro-organisms, and the benthic holobiont community is a more general sample including both photosynthetic and non-photosynthetic organisms. The same approach was taken to calculate and model microbiome/metabolome distinctness for individual holobionts with the additional random effects of (i) ARMS identity (nested within locality), as multiple individual holobionts were collected from the same ARMS, and (ii) sponge species.

Benthic holobiont community microbiome distinctness from the sediment microbiome was also modelled with a modified mixture Sloan neutral community model (MSNCM [ 64 ]). This additional modelling approach captures the contribution of each of the sediment and benthic holobiont community microbiome metacommunities to the composition of benthic holobiont community microbiomes from individual ARMS, thus providing an alternative abundance-based test of whether benthic holobiont community microbiomes become more distinct from the sediment microbiome under OA. The original Sloan neutral community model describes the frequency of occurrence of ASVs in a community as a function of their abundance in the metacommunity, with a single free parameter (m: migration) which can be interpreted as the probability of neutral dispersal or alternatively inverse dispersal limitation. The MSNCM used here models ASV frequency in sampled benthic holobiont community microbiomes from each pH regime as a function of its abundance in two metacommunities: (1) all benthic holobiont community microbiomes from the same pH regime as the sample and (2) all sediment microbiomes from the same pH regime as the sample. Each metacommunity is fit with its own migration/inverse dispersal limitation parameter (mholo, menv), and a mixture parameter (mix) is fit describing the contribution of each metacommunity. The model is fit to samples from each pH regime separately using non-linear least-squares fitting as detailed in Burns et al. [ 65 ]⁠.

All statistical analyses were conducted in R (version 4.2.1 [ 66 ])⁠; specific packages used were as follows: phyloseq v1.40.0 [ 67 ] for data manipulation, vegan v2.6–4 [ 68 ] for PERMANOVA and betadisper, breakaway v4.8.2 [ 61 ] and DivNet v.0.4.0 [ 62 ] for diversity estimation and modelling, lme4 v1.1–31 [ 69 ] and MuMIn v1.47.1 [ 70 ] for generalised linear mixed models, DESeq2 v1.36.0 for differential abundance analysis [ 63 ], and minpack.lm v1.2–2 [ 71 ] and Hmisc v4.7–1 [ 72 ] for non-linear least-squares modelling.

Genetic diversity and composition

Five fractions were generated for analysis: benthic photosynthetic community, benthic holobiont community, sediment, Tethya sp., and Halisarca sp. Benthic photosynthetic communities were dominated by red algae (Rhodophyta), brown algae (Ochrophyta), and Cyanobacteria. Benthic holobiont communities were visually dominated by Porifera, Chordata, Bryozoa, Annelida, Arthropoda, and Mollusca. Benthic holobiont community microbiomes were dominated by Proteobacteria, unclassified Bacteria, and Cyanobacteria. Benthic photosynthetic community microbiomes were dominated by Proteobacteria and Cyanobacteria, followed by Firmicutes, Bacteroidota, and unclassified Bacteria. Sediment microbiomes were dominated by Proteobacteria, unclassified Bacteria, Bacteroidota, and Planctomycetota. Sponge microbiomes were dominated by Proteobacteria; unclassified Bacteria ASVs were also highly abundant in Tethya sp. samples. See Figure S 1 .

Fifteen 23S rRNA gene metabarcoding libraries were generated across the pH gradient, to confirm the expected effect of OA on the benthic photosynthetic communities. The composition of the benthic photosynthetic community differed significantly by pH ( F  = 5.5, p  < 0.05), with significant declines in phylum Shannon diversity (95% CI [–0.76, –0.55], p  < 0.05) and ASV Shannon diversity (95% CI [–2.27, –0.33], p  < 0.05) at lower pH. Lower pH was associated with significantly increased differential abundance of the dominant phylum Ochrophyta (of which 99.7% of reads were from the class Phaeophyceae, and 71.4% were from the genus Sargassum ; Fig.  3 ; Figure S 1 ).

Heat maps of significant differential abundance of phyla with decreasing pH. Significant change in differential abundance of algal phyla (23S rRNA gene) with decreasing pH are seen in ( A ); algal phyla are shown on the left, and families are shown on the right. Algal taxonomy was assigned using the microgreen database [ 58 ]. Significant change in differential abundance of microbial phyla (16S rRNA gene) with decreasing pH are seen in ( B ); bacterial phyla are shown on the left. Microbial taxonomy was assigned using the GTDB taxonomy [ 57 ]

Ninety-four 16S rRNA metabarcoding libraries were generated across the pH gradient, from 18 ARMS. Each fraction (benthic photosynthetic community microbiome, sediment microbiome, benthic holobiont community microbiome, and sponge [ Tethya sp. and Halisarca sp.] microbiomes) had between 15 and 30 samples (Table 1 ), which in total produced 55,348 ASVs ( n  = 94). Eighty bacterial phyla were identified, with 77.7% of reads identified to the level of phylum.

Forty-seven 16S rRNA gene metabarcoding libraries were generated across the benthic photosynthetic community, benthic holobiont community, and sediment microbiomes ( n  = 15, 17, 15, respectively). All community microbiomes were significantly compositional different at lower pH (control pH compared with medium and low pH as an ordinal variable): benthic photosynthetic community microbiome ( F  = 2.3, p  < 0.05), sediment microbiome ( F  = 3.9, p  < 0.05), and benthic holobiont community microbiome ( F  = 3.0, p  < 0.05; Table S 2 B; Figure S 2 ). There was no significant effect of pH on richness for any community microbiome (Table S 3 ). Phylum and ASV level Shannon diversity was significantly lower in the benthic photosynthetic community microbiome at lower pH (95% CI [− 0.55, − 0.09], p  < 0.05; Table S 4 ; Figure S 3 and 95% CI [− 2.5, − 0.64], p  < 0.05; Table S 5 ; Figure S 3 , respectively). Please see Table S 6 for a summary of all significant 16S rRNA patterns. Decreased pH was associated with significant differences in the abundance of the following phyla: increased WOR-3 and Desulfobacterota alongside decreased Armatimonadota in the sediment microbiome, increased Desulfobacterota in the benthic holobiont community microbiome, and increased Desulfobacterota alongside decreased Poribacteria, Gemmatimonadota, and Firmicutes in the benthic photosynthetic community microbiome (Fig.  3 ).

Forty-seven 16S rRNA gene metabarcoding libraries were generated from the microbiomes of the two sponge species: 17 from Halisarca sp. individuals and 30 from Tethya sp. individuals. Microbiome composition was only significantly different across the pH gradient for Tethya sp. sponges ( F  = 5.9, p  < 0.05; Table S 2 ; Figure S 2 ). While there was no significant effect of pH on ASV richness or ASV level Shannon diversity, reduced pH was associated with significantly lower phylum level Shannon diversity in Tethya sp. sponge microbiomes (95% CI [− 0.44, − 0.12], p  < 0.05), but no such effect was found in Halisarca sp. sponge microbiomes (Table S 4 ; Figure S 3 ; see Table S 6 for a summary of all significant 16S rRNA patterns). At the phyla level, lower pH was associated with significantly higher abundances of Marinisomatota, SAR324 , and Cyanobacteria and significantly lower abundances of Firmicutes, Desulfobacterota, and Bacteroidota (Fig.  3 ) in both sponge species microbiomes.

Biochemical diversity and composition

One-hundred and seven metabolome libraries were analysed from the same 18 ARMS. Each fraction (benthic photosynthetic community metabolome; benthic holobiont community metabolome, sediment metabolome, and sponge metabolomes) had between 18 and 34 samples across the pH gradient (Table 1 ). These samples produced 1211 compounds, of which 4.62% were identified, representing 6% of all molecules.

Fifty-five metabolome libraries were generated from the sediment metabolome, the benthic holobiont community, and the benthic photosynthetic community metabolomes ( n  = 18, 18, 19, respectively). There was no significant compositional difference in these community metabolomes at lower pH (Table S 2 ; Figure S 2 ). There was also no significant effect of pH on compound richness for any community metabolome (Table S 7 ; Figure S 3 ). However, Shannon diversity was significantly lower at lower pH in the benthic holobiont community metabolome (95% CI [− 0.07, − 1.18], p  < 0.05) and significantly higher at lower pH in the sediment metabolome (95% CI [0.02, 0.80], p  < 0.05; Table S 8 ; Figure S 3 ). Decreased pH was associated with significant differences in the abundance of several identified compounds (note that more than 95% of compounds were not identifiable). In the sediment metabolome, glycerophospholipids (lysophosphatidylcholines (LPCs) and phosphocholines) and pheophorbide A (a chlorophyll-derived compound) were less abundant, and beta-carotene was more abundant. In the benthic holobiont community benzene derivatives, chondramide B and mesoporphyrin IX (the latter two both anticarcinogens) were less abundant; a range of glycerophospholipids (again LPCs and phosphocholines), sucrose, and beta-carotene were more abundant. In the benthic photosynthetic community, pheophorbide A was more abundant, and various glycerophospholipids had significantly different abundances, with LPCs mostly having higher abundances and phosphocholines lower abundances (Fig.  4 ).

Heat map of significant differential abundance of metabolites with decreasing pH (community and sponge). Identities of metabolites 1–29 are outlined below: 01 — (1S,2S)-2-(methylamino)-1-phenylpropan-1-ol hydrochloride; 02 — benzalkonium chloride (C12); 03 — diphenhydramine|2-benzhydryloxy-N,N-dimethylethanamine; 04 — N,N-diethyl-3-methylbenzamide; 05 — niranthin; 06 — beta-carotene; 07 — chondramide B; 08 — 15(S)-hydroxy-(5Z,8Z,11Z,13E)-eicosatetraenoic acid; 09 — 17(18)-EpETE; 10 — 1-octadecyl-sn-glycero-3-phosphocholine; 11 — 1-palmitoylphosphatidylcholine; 12 — 1-(1Z-hexadecenyl)-sn-glycero-3-phosphocholine; 13 — 1-(9Z-Octadecenoyl)-sn-glycero-3-phosphocholine; 14 — 1-arachidoyl-2-hydroxy-sn-glycero-3-phosphocholine; 15 — 1-hexadecanoyl-sn-glycero-3-phosphocholine; 16 — 1-O-hexadecyl-2-O-(2E-butenoyl)-sn-glyceryl-3-phosphocholine; 17 — 1-octadecyl-sn-glycero-3-phosphocholine; 18 — 1-stearoyl-2-hydroxy-sn-glycero-3-phosphocholine; 19 — lysophosphatidylcholine (LPC 16:0); 20 — lysophosphatidylcholine (LPC 18:1); 21 — lysophosphatidylcholine (LPC 18:2); 22 — lysophosphatidylcholine (LPC 18:3); 23 — lysophosphatidylcholine (LPC 20:5); 24 — lysophosphatidylcholine (LPC 22:6); 25 — mesoporphyrin IX; 26 — 3-indoleacrylic acid; 27 — sucrose; 28 — (R)-4-((3R,5R,8R,9S,10S,12S,13R,14S,17R)-3,12-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)pent-2-enoic acid; 29 — pheophorbide A

Fifty-two metabolome libraries were generated from the two sponge species, with 18 from Halisarca sp. and 34 from Tethya sp. There was no significant compositional difference in the metabolomes of either sponge at different pH (Table S 2 ; Figure S 2 ). There was also no significant effect of pH on compound richness or Shannon diversity for the sponge metabolomes (Figure S 3 ). Among identified compounds (note that more than 95% of compounds were not identifiable), decreased pH was associated with significantly different abundances in a variety of glycerophospholipids and a significantly increased abundance of a benzene derivate across both sponge species (Fig.  4 ).

Holobiont microbial and chemical distinctness

The proportion of benthic holobiont community microbiome ASVs not found in the sediment microbiome (i.e. holobiont community microbiome distinctness) was lower at lower pH (95% CI [− 6.36, − 1.74], p  < 0.05). The same pattern was observed in the metabolome: the proportion of benthic holobiont community metabolites not found in the sediment (i.e. holobiont community metabolome distinctness) was lower at lower pH (95% CI [− 14.46, − 2.83], p  < 0.05; Fig.  5 ).

Microbiome and metabolome distinctness of benthic holobiont communities as a function of pH. Microbiome ( A ) and metabolome ( B ) of the benthic holobiont community versus the sediment microbiome and microbiome ( C ) and metabolome ( D ) of the two sponge microbiomes versus the sediment microbiome. Distinctness was calculated as percentage of ASVs/metabolites not shared between microbiomes/metabolomes. Horizontal dotted line indicates 50% distinct. ** p -value < 0.01

The dispersal probability from the sediment microbiome into the benthic holobiont community microbiome, as measured by the MSNCM (menv weighted by the mixing parameter), was also higher at lower pH. The frequency of occurrence of ASVs across the benthic holobiont community microbiome samples was well described by their abundance in the benthic holobiont community and sediment metacommunities through the MSNCM. However, the fit of the model was poorer at medium and low pH (control: 0.64, medium: 0.29, low: 0.38). The ratio between mholo and menv (both weighted by the mixing parameter) was also lower at lower pH, reflecting an overall increased contribution of sediment microbial abundance to determining the microbiome composition of benthic holobiont communities under OA (Table 2 ).

Change in microbial and chemical distinctness of individual sponge holobionts ( Tethya sp. and Halisarca sp.) with ocean acidification was also analysed to examine whether community-level patterns were observed in individual holobionts. Individual sponge holobiont microbiome distinctness from sediment was lower at lower pH (95% CI [− 7.87, − 1.75], p  < 0.05; Fig. 5 ). This individual holobiont microbiome distinctness model explained 61% of variation ( R 2 ) compared to 9% of variation ( R 2 ) explained by the equivalent community-level microbiome distinctness model described above. There was no significant effect of pH on sponge metabolome distinctness.

Our results are consistent with the simplification of the algal community seen under OA elsewhere [ 73 ]. Our results further demonstrate that this simplification effect extends across the entire benthic photosynthetic community holobiome as algal-associated microbes also decline in Shannon diversity with OA. While we do not observe an overall compositional shift in the benthic photosynthetic community metabolome, implying that photosynthetic function is largely conserved through these changes, we do see shifts in specific metabolites. Increasing concentration of chlorophyll-derived pheophorbide A suggests increased chlorophyll turnover [ 74 ], consistent with the doubling of macroalgal benthic cover previously observed under OA at these sites [ 18 ]. We also see significant increases in sucrose in the benthic holobiont community under OA, potentially related to sugar-enriched dissolved organic carbon (DOC) released by these algae.

Both findings are consistent with the initial steps in the DDAM mechanism of microbialisation [ 26 ], whereby increased DOC is released from fleshy algae, in this case Sargassum [ 19 ]. We did not, however, test for the increased microbial biomass previously shown to result from increased DOC stimulating the microbial loop and causing a shift in ecosystem trophic structure [ 26 , 75 ]. The main microbial phyla that we observed to increase in abundance in our community samples (i.e. not sponge samples) with low pH (Desulfobacterota and WOR-3 ) differ from those observed by Haas [ 26 ] (Alphaproteobacteria, Gammaproteobacteria, and Bacteroidetes). However, this difference in specific taxonomic patterns in response to different stressors, across different sites, and when sampling different substrates (water vs. sediment) is expected and is a key motivation for seeking to identify more general ecosystem-level effects of stressors, such as microbiome distinctness.

The data presented here provide the first evidence of declining holobiont community distinctness in microbes and metabolites under OA. Our results build on the evidence that OA changes holobiont microbiomes [ 76 ] by demonstrating a systematic decline of a distinct benthic holobiont community microbiome, such that it becomes more compositionally similar to the sediment microbiome. The composition of the sediment microbiome in our study is representative of surface sediments from the region [ 77 ] and global analyses of marine sediments [ 78 ]. Specifically, the results of the MSNCM suggest that this effect may result from the benthic holobiont community being increasingly colonised by sediment microbes as pH decreases. A similar effect has previously been observed for specific sponge [ 79 ] and coral [ 80 , 81 ] holobionts in response to different environmental stressors, but has not previously been observed at the community level. While organism-level studies provide information about the response of key species (e.g. habitat builders) to environmental change, a holistic approach is needed to accurately evaluate and predict impacts on coral reefs. The synthesis of knowledge across scales, from individual microbes and holobionts to ecosystem-wide communities and processes, has recently been called for by multiple authors [ 29 , 75 , 82 ]. Autonomous reef monitoring structures (ARMS) provide a novel tool for taking this “nested ecosystem approach” and conducting in situ experiments.

We explain the decline in the distinctness of the benthic holobiont community from the sediment microbiome as being caused by increased opportunities for colonisation of benthic holobiont communities by environmental microbes due to microbialisation. However, individual macrobes under stress may also become less able to regulate their microbiomes, while colonisation opportunities remain constant. In extreme cases, this inability to regulate the microbiome can result in traumatic dysbiosis [ 80 ], a more heterogeneous microbiome (Anna Karenina principle, [ 83 ], and host death. However, it is unlikely that this process is the major contributor to the observed community-level effect seen here because macrobe community compositions have already shifted under OA conditions at these vent sites, with an increased dominance of taxa that are less impacted by the stressor [ 18 , 84 ]. Therefore, the notion that the majority of the macrobe community is experiencing dysbiosis associated with acute organism-level stress seems unlikely. For example, some sponges are known to thrive under OA, and do not exhibit evidence of organismal stress [ 85 , 86 , 87 ]. In this study, the two sponge holobionts individually analysed ( Tethya sp. and Halisarca sp.) showed reduced distinctness of their microbiomes from the sediment microbiome under OA. However, neither showed evidence of increased compositional heterogeneity of their microbiomes as expected under dysbiosis by the Anna Karenina principle, which predicts organism-level stress to reduce the ability of macrobes to regulate their microbiomes [ 83 ]. In addition, metabolomes for these two sponges do not become significantly less distinct from the sediment under OA, which is consistent with the hypothesis that these sponges were not under stress.

Colonisation of holobionts by environmental microbes may support the resilience of macrobe communities, as it has been shown to allow some hosts to acclimatise to new environmental conditions, for example by allowing the host to make use of changing energy sources, and facilitate greater adaptation than can be afforded by host phenotypic plasticity [ 88 , 89 ]. Different degrees of microbial restructuring observed among different sponge species indicate that horizontal transmission differs between species, and these variations affect the ability of sponges to persist under OA conditions [ 12 , 90 ]. Here, we find significant increases in Cyanobacteria associated with sponges under OA conditions, which can contribute > 50% of a sponge’s carbon demand [ 91 ] and likely provide at least some sponge species with enhanced scope for growth in these seep environments [ 12 ]. We also find significant increases in Desulfobacterota in the benthic photosynthetic community, benthic holobiont community, and sediment; this phylum includes many organisms capable of reducing sulphur compounds [ 92 , 93 ]. Only one of the two ocean vents (Dobu) is known to release hydrogen sulphide [ 18 ], so this does not explain the increase in Desulfobacterota observed across sites. We note that while we do not identify changes in the differential abundance of any metabolites with known roles in sulphur cycling, this is not evidence of their absence due to the low level (less than 5%) of identification of metabolites. While their role here is unknown, coral reefs are important hotspots of marine sulphur and increased sulphate reduction rates of marine microbial communities have been found to occur between a pH of 6 and 7 [ 94 ], suggesting that rates may increase with OA. As the marine sulphur cycle is a quintessential example of algal–bacterial interactions [ 95 ], it will be important for future studies to investigate the impact of algal-derived microbialisation on the marine sulphur cycle, especially as new components and pathways in the sulphur cycle are still being identified [ 96 ].

One would expect the dynamics of microbes and holobionts to be universal to all ecosystems [ 36 , 97 , 98 ], though they may emerge from different organism-level interactions. Therefore, microbialisation, and the observable property of declining holobiont community distinctness under environmental change, could represent a universal ecosystem stress response. Identifying such a general, undesirable response (microbialised ecosystems typically have lower intrinsic and use values [ 99 ]) and a metric of ecosystem change has clear benefits to policy and evaluation. For example, ecosystem change and the associated risk of ecosystem collapse are the underpinning concept leveraged for the IUCN Red List of Ecosystems [ 100 ], but defining collapse for each ecosystem individually is a time-consuming and contentiously value-laden task [ 101 , 102 ]. Furthermore, as microbial communities respond rapidly to environmental change, microbial bioindicators could provide signatures of change with the speed and resolution to allow real-time responses by ecosystem managers. Generating predictions of ecosystem change based on a mechanistic understanding of all organism-level effects of stressors remains unrealistic [ 17 , 103 ]. Therefore, identifying general ecosystem-level changes under stress presents a promising route towards a more efficient predictive ecosystem science, responding to the urgent needs of the biodiversity and climate crisis.

Availability of data and materials

The metabarcoding datasets are available from the NCBI Sequence Read Archive repository. 16S rRNA gene data is available under BioProject ID PRJNA945340 ( http://www.ncbi.nlm.nih.gov/bioproject/945340 ) and 23S rRNA gene data is available under BioProject ID PRJNA945259 ( http://www.ncbi.nlm.nih.gov/bioproject/945259 ). The mass spectrometry data is available at the GNPS MassIVE repository under MassIVE ID: MSV000080572 ( https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=f5c6591769d541a68fdb8bb201532054 ). R scripts used in the bioinformatic pipeline are archived at https://doi.org/10.5281/zenodo.7740559 and available at https://github.com/J-Cos/BioinformaticPipeline . R scripts for the statistical analysis are archived at https://doi.org/10.5281/zenodo.8280507 and available at: https://github.com/J-Cos/Paper_PNG .

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Acknowledgements

We thank Sam Noonan, Sven Uthicke, Craig Humphrey, Amanda Feuerstein, and Obedi Daniel, along with Rob van der Loos and crew members of the M/V Chertan, for their help and support in the field. We also thank the community of Upa Upasina and Dobu Island for permission to deploy ARMS on their reefs and for their generous welcome and assistance during the over 2 years of this experiment. We wish to acknowledge the use of facilities and technical support from the Laboratories of Analytical Biology, National Museum of Natural History, Smithsonian Institution, and the Smithsonian Institution’s DNA Barcode Network in particular Lee Weigt, Amy Driskell, Jeff Hunt, Lowen Wachhaus, Matthew Kweskin, Maggie Halloran, and Janette Madera as well as Mike Trizna and Niamh Redmond. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US government.

JW was supported by the QMEE CDT and funded by NERC grant number NE/R012229/1; CM and ER were supported by the National Science Foundaion (Award Number 1243541); NK and LP were supported by the National Science Foundation (Award Number 1558868), the Sant Chair for Marine Science, and the Smithsonian Institution’s Scholarly Studies Program; AH was supported by the National Science Foundation (Award Number 2022717); KF was supported by the Great Barrier Reef Foundation’s ‘Resilient Coral Reefs Successfully Adapting to Climate Change’ programme.

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Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Buckhurst Road, Ascot, SL5 7PY, UK

Jake Williams & Emma Ransome

Institute of Zoology, Zoological Society of London, Regent’s Park, London, NW1 4RY, UK

Jake Williams & Nathalie Pettorelli

Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA

Aaron C. Hartmann

Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA

Robert A. Quinn

Laboratoire Evolution Et Diversité Biologique, CNRS/UPS, Toulouse, France

Laetitia Plaisance

National Museum of Natural History, Smithsonian Institution, Washington, DC, 20013, USA

Laetitia Plaisance, Michael O’Mahoney, Chris P. Meyer & Nancy Knowlton

Australian Institute of Marine Science, Townsville, Queensland, Australia

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ER & LP designed the experiment; ER & LP collected the data; ER & MO conducted the metabarcoding; RQ conducted the mass spectrometry; RO & AH ran the metabolomics, JW ran the bioinformatics; JW & ER conceived the hypotheses, JW analysed the results, JW wrote the paper. All authors contributed significantly to editing the manuscript.

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Additional file 1:.

  Table S1 . Number of sequences retained at each step of denoising (implemented in DADA2); samples with fewer than 50,000 denoised sequences (ASVs) and duplicate samples were removed prior to analysis. Table S2 . Effect size and significance of factors in PERMANOVAs describing the compositional differences among sites, and along the pH gradient for all multiomic data types; site fit as a blocking factor, except in the case of Halisarca sp. sponge fractions, which were only collected at one site. A p -value less than 0.05 indicates that the factor significantly affects composition. Bonferroni correction was applied to all PERMANOVA. Table S3 . ASV estimated richness betta mixed model: fixed effects listed, random effect of locality. Model Explanatory Power: test statistic = 117.2, p <0.05. A p -value less than 0.05 indicates that the explanatory variable significantly affects ASV richness. All values in the table are reported to two significant figures. Table S4. Phylum Shannon diversity betta mixed model: fixed effects listed, random effect of locality. Model Explanatory Power: test statistic = 3197.94, p <0.05. A p-value less than 0.05 indicates that the explanatory variable significantly affects metabolite richness. All values in the table are reported to two significant figures. Table S5. ASV Shannon diversity betta mixed model: fixed effects listed, random effect of locality. R Squared (conditional)= 88.8%. A p -value less than 0.05 indicates that the explanatory variable significantly affects metabolite richness. All values in the table are reported to two significant figures. Table S6. Summary of tests and results for 16S rRNA gene and metabolomic data. Table S7. Compound richness linear mixed model: fixed effects listed, random effect of locality. R Squared (conditional)= 24.5%. A p -value less than 0.05 indicates that the explanatory variable significantly affects ASV richness. All values in the table are reported to two significant figures. Table S8. Compound Shannon diversity linear mixed model: fixed effects listed, random effect of locality. R Squared (conditional)= 32.0%. A p -value less than 0.05 indicates that the explanatory variable significantly affects ASV richness. All values in the table are reported to two significant figures.  Fig. S1. Visual representation of read abundance from the ARMS 23S rRNA gene and 16S rRNA gene metabarcoding dataset. Data are aggregated across biological replicates to present the average composition of each fraction, at each pH, showing phylum (in white text) and class (in grey text). Fig. S2. NMDS of microbiome and metabolome composition of all fractions across the pH gradient, calculated using Morisita dissimilarity. Fig. S3. Microbial and chemical richness and Shannon diversity boxplots for all fractions across the pH gradient.

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Williams, J., Pettorelli, N., Hartmann, A.C. et al. Decline of a distinct coral reef holobiont community under ocean acidification. Microbiome 12 , 75 (2024). https://doi.org/10.1186/s40168-023-01683-y

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Received : 16 March 2023

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DOI : https://doi.org/10.1186/s40168-023-01683-y

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