Hypotheses are the questions scientists ask as they use the scientific method to understand the world. People use the process of formulating then attempting to disprove a hypothesis in their everyday lives as well. The function of the hypothesis is to give structure to the process of understanding how the world works.
A hypothesis is an educated guess, based on the probability of an outcome. Scientists formulate hypotheses after they understand all the current research on their subject. Hypotheses specify the relationship between at least two variables, and are testable. For a hypothesis to function properly, other scientists must be able to reproduce the results that prove or disprove it. Two types of hypotheses exist: a descriptive hypothesis asks a question, and a directional hypothesis makes a statement.
The scientific method is the process by which hypotheses function. Scientists use the scientific method to, over time, form an accurate picture of the world. The scientific method attempts to remove the scientist's bias from the research. The four parts of the scientific method are observation and description, formulation of a hypothesis, use of the hypothesis for prediction and performance of testing of the hypothesis. Scientists use the scientific method to disprove hypotheses, rather than prove them. It they cannot be disproved, the hypotheses over time become accepted theories.
The most important function hypotheses perform is providing the framework for testing and experimentation. Scientists formulate a hypothesis, or ask a question, about a certain phenomenon and how it relates to other aspects of the world. Then they devise ways to try to disprove their theory as to the answer. For instance, if a scientist made a hypothesis that what goes up must come down, he would test it by throwing many items in the air to see if they do come down. Because scientists cannot test every single possible item for this theory, hypotheses are never proven. However, after many scientists have experimented with the hypothesis, it becomes accepted scientific theory.
Scientists make a hypothesis by comparing the phenomenon being studied to another phenomenon. For instance, in the real world, a person might decide that her house is cold because a window is open. She would test this theory by checking the windows. If the windows are closed, then that hypothesis is proven false, and another is formed when the person decides that her house is probably cold because the furnace isn't working properly. The process of forming and disproving hypotheses continues until a person makes a hypothesis that cannot be disproved.
Shaunta Alburger has been a professional writer for 15 years. She's worked on staff at both major Las Vegas newspapers, as well as a rural Nevada weekly. Her first novel was published in 2014.
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Patricia farrugia.
* Michael G. DeGroote School of Medicine, the
† Division of Orthopaedic Surgery and the
‡ Departments of Surgery and
§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont
There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1
In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.
Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.
Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.
In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4
Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).
Feasible | ||
Interesting | ||
Novel | ||
Ethical | ||
Relevant |
Adapted with permission from Wolters Kluwer Health. 2
Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.
Population (patients) | ||
Intervention (for intervention studies only) | ||
Comparison group | ||
Outcome of interest | ||
Time |
A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.
The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.
The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).
However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.
Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”
The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9
Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.
The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.
From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.
The following is an example from the literature about the relation between the research question, hypothesis and study objectives:
Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.
Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?
Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).
Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.
The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.
FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.
Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.
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.
Following are the characteristics of the hypothesis:
Following are the sources of hypothesis:
There are six forms of hypothesis and they are:
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.
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.
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.
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.
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 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.
Following are the examples of hypotheses based on their types:
Following are the functions performed by the hypothesis:
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:
What is hypothesis.
A hypothesis is an assumption made based on some evidence.
What are the types of hypothesis.
Types of hypothesis are:
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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Conservation, restoration and land management are increasingly implemented at landscape scales 1 , 2 . However, because species interaction data are typically habitat- and/or guild-specific, exactly how those interactions connect habitats and affect the stability and function of communities at landscape scales remains poorly understood. We combine multi-guild species interaction data (plant–pollinator and three plant–herbivore–parasitoid communities, collected from landscapes with one, two or three habitats), a field experiment and a modelling approach to show that multi-habitat landscapes support higher species and interaction evenness, more complementary species interactions and more consistent robustness to species loss. These emergent network properties drive improved pollination success in landscapes with more habitats and are not explained by simply summing component habitat webs. Linking landscape composition, through community structure, to ecosystem function, highlights mechanisms by which several contiguous habitats can support landscape-scale ecosystem services.
Conservation policy and landscape management have moved from the historic protection of species and their habitats to ecosystem and landscape-level approaches 1 , 2 . Habitat heterogeneity 3 , 4 and the number of habitats in a landscape 5 , 6 contribute to species richness and ecosystem functioning, especially in agricultural landscapes 7 , 8 . At present, we lack a mechanistic understanding of how the number of habitats contributes to community structure and function. This understanding is key to the landscape-scale management of ecosystem services that depend on species interactions, such as pollination and pest control and to maintaining functioning ecosystems more generally. Ecological networks of species’ interactions provide a route to understanding functional responses to biodiversity changes 9 , 10 . Although communities host several guilds and transcend different habitats, network datasets encompassing both these characteristics remain scarce, meaning we might be missing important cross-habitat or guild cascades or functional effects. Researchers have recently started linking several networks, of one interaction type across habitats 11 , several interaction types across habitats 12 or several interactions at replicated sites of similar habitat composition 8 . However, a lack of independently derived measures of function has prevented these network changes from being linked mechanistically to functional outcomes.
Both structure and function of local communities can be affected by organisms dispersing between habitats 13 . Immigrating individuals may have a similar role to local species (that is, redundancy) or fill an empty ecological niche (that is, complementarity). If immigrating and local species respond differently to disturbances, this can reduce functional variability by ensuring that overall functionality is maintained 14 , 15 , 16 , 17 . Community impacts of dispersal may differ across trophic groups 18 , making the combination of habitat and guild replication critical. Despite the importance to both pure and applied ecology, it remains unknown whether landscapes are simply the sum of their habitat parts, in terms of both interaction structure and community function, or whether there are emergent properties, such as increased stability or functioning, that cannot be explained by their component habitats alone.
Here we evaluate how the number of habitats in a given area influences biodiversity, network structure, community stability and function across several interaction types (plant–pollinator and three types of plant–herbivore–parasitoid networks) in replicate landscapes. Using 30 independent field sites in southwest United Kingdom (Fig. 1a ), we test how landscapes with more habitats affect the plant and insect communities in them: specifically, we quantify the effects on species’ abundances, species richness, evenness (both in terms of insect species and the degree to which interactions are uniformly distributed among species) and robustness to species loss. Using a manipulative field experiment, we then test whether the number of habitats affects the ecological function of insect pollination. Finally, we develop a modelling approach to investigate if landscape-scale networks have emergent properties which cannot be explained by their component habitat networks.
a , Map of sites for monads of one 9 ha habitat, dyads of two 4.5 ha habitats and triads of three 3 ha habitats in southwest United Kingdom. b – d , Visualization of the plant–insect interactions as multilayer networks and satellite images of Penhale Sands, a sand dune monad ( b ), Seet Bridge, a woodland and salt marsh dyad ( c ) and Hangman’s Hill, a scrub and heathland and grassland triad ( d ). Each layer corresponds to one habitat with nodes as species and shapes coding for the species type. Interspecific interactions in each habitat (layer) are represented with solid lines. Dashed lines connect nodes between layers representing the same species in different habitats. Map data sources: Office for National Statistics licensed under the Open Government Licence v.3.0; contains OS data © Crown copyright and database right 2021. Google Earth Pro Image © 2024 (CNES/Airbus ( b ) and Landsat/Copernicus ( c and d )).
We standardized field site size at 9 ha and varied the number of constituent habitats from one to three while selecting sites to allow a balanced replication of multi-habitat landscape types: thus, ten sites contained a single habitat (9 ha ‘monads’), ten sites contained two habitats (‘dyads’ with two 4.5 ha habitats) and ten sites contained three habitats (‘triads’ with three 3 ha habitats). Each habitat was selected from a pool of six habitats: grassland, heathland, woodland, salt marsh, sand dune and scrub (Fig. 1b–d and Supplementary Table 3 ) to avoid a habitat identity effect confounding that of the number of habitats, while also avoiding the issue of triads always having the same composition whereas monads and dyads differ. Over 2 years, we collected data on 11,482 interactions among 154 plant species and 954 insect species (5,729 flower–visitor interactions, 2,345 plant–leaf miner interactions, 697 plant–caterpillar interactions, 1,240 plant–seed feeding interactions and 1,471 herbivore–parasitoid interactions; see Fig. 1b–d for example networks, whereby species are depicted as nodes connected by interactions as links).
There was a significant difference in community composition and network structure among monads, dyads and triads. Thus, insect species richness and abundance, plant species richness, floral abundance, insect species evenness and interaction evenness in 9 ha landscapes were higher when there were more habitats (multiple analysis of variance (MANOVA), F 1,28 = 5.366; P = 0.001; Fig. 2 and Supplementary Table 2 ). Specifically, pairwise MANOVAs demonstrated significant increases from monads to triads ( F 1,18 = 5.552; P = 0.005; Fig. 2 ). To better understand the specific aspects driving the MANOVA results, generalized linear models showed that more habitats in the landscape supported non-significant increases of plant species richness (mean ± s.d. for monads, 41.4 ± 20.77; dyads, 43.3 ± 22.5; triads, 59.1 ± 22.99; F 1,28 = 3.244; P = 0.082; Fig. 2b ) and interaction evenness (monads, 0.48 ± 0.07; dyads, 0.52 ± 0.06; triads, 0.52 ± 0.03; F 1,28 = 3.767; P = 0.062; Fig. 2e ) and a significant increase in insect species evenness (monads, 0.71 ± 0.11; dyads, 0.81 ± 0.06; triads, 0.83 ± 0.04; F 1,28 = 14.92; P < 0.001; Fig. 2f ); all other factors showed no significant difference when considered independently (floral abundance, insect species richness and insect abundance all F 1,28 < 0.714; P > 0.405; Fig. 2a,c,d ).
a – f , Differences among monads, dyads and triads in terms of floral abundance ( a ), plant species richness ( b ), insect abundance ( c ), insect species richness ( d ), interaction evenness ( e ) and species evenness ( f ). Circles indicate each site and the habitat combination therein, with a random horizontal jitter to reduce overlap. Data are from 30 independent field sites, 558,386 open floral units (154 plant species) and 11,482 interactions (954 insect species). Boxes represent the 25% (Q1) and 75% (Q3) quartiles around the median line and whiskers are Q1 − 1.5× IQR to Q1 and Q3 to Q3 + 1.5× IQR. See Extended Data Fig. 1 and Supplementary Information section 6 for subwebs.
We measured stability as community robustness, a network-level calculation of the resistance of a community to species loss through secondary extinction, including all interaction types. Although stability has several components, to address the scope of this study, our data sampling design focused on spatial variability across a landscape and did not consider temporal dynamics. Mimicking bottom-up habitat degradation, we simulated removal of plant species across the landscape from least to most abundant at a given site because rare species are more likely to go extinct first 15 . Our robustness analysis ( Methods ) allows for rewiring, whereby species reallocate their interactions following the loss of a resource 16 , 19 and accounts for shared species between interaction types resulting from ontogenetic diet shifts (for example, herbivorous caterpillar to pollinating butterfly); thus measuring the effects of species loss propagating through the multilayer network across different interaction types. There was no difference in mean robustness among monads, dyads and triads ( F 2,27 = 0.183; P = 0.83) but the variability of robustness decreased significantly as habitat number increased (interquartile range (IQR) of 0.105 in monads, 0.064 in dyads and 0.047 in triads; Brown–Forsythe’s test F 2,14997 = 1,272.9; P < 0.001; Fig. 3 ). More flexible rewiring rules make the effect on robustness variability more apparent (Fig. 3 and Extended Data Fig. 2 ). This variability trend is stronger with sequential removal of rare-to-common species than with random removal of species, indicating that secondary extinction trajectories are not purely a result of species loss but rather associated with the distribution of rare species across habitats (Fig. 3 and Extended Data Fig. 3 ). Plant beta-diversity was similar in the monad, dyad and triad sites, indicating that the variability trend in robustness is unlikely to be a consequence of sampling bias because of specific habitat combinations overlapping among triads (Supplementary Information section 1 ).
a , Robustness calculations for each site with 100% dietary flexibility for insects and 50% extinction threshold. Each point is a robustness calculation, colour-coded for each site within landscape type. Boxplots indicate the variation for all sites of a landscape type (monad, dyad or triad). b – d , Brown–Forsythe’s test statistic (two-sided), measuring the equality of group variance, for three extinction thresholds (25% ( b ); 50% ( c ); 75% ( d )) and various levels of dietary flexibility. Dashed lines correspond to the random extinction scenario, stars code for the significance level of the test. For each landscape type (monad, dyad and triad), n = 5,000 (ten sites × 500 replicates). Boxes represent the 25% (Q1) and 75% (Q3) quartiles around the median line and whiskers are Q1 − 1.5× IQR to Q1 and Q3 to Q3 + 1.5× IQR. Asterisks indicate significance (*** P < 0.001; all less than P = 7.46 × 10 −8 ).
We conducted a manipulative field experiment to test how several habitats affected pollination function, defined here as fruit weight and quality. At the centre of each monad and triad, we placed 20 potted wild-type strawberry plants, Fragaria vesca , grown under standardized conditions, as they began flowering (Extended Data Fig. 4 ). Strawberries are an excellent bioassay plant as pollination quality can be easily quantified 8 , 20 with several high-quality insect pollinator visits leading to larger and more symmetrical fruits (Class I fruits versus Class II fruits; Extended Data Fig. 5 ) 21 . Plants were left on site for 14 days to be pollinated and then kept in a pollinator-free greenhouse for 28 days. Ripe strawberries were weighed and graded as being Class I, if perfectly symmetrical, or Class II, if otherwise ( Methods ; Extended Data Fig. 5 ). Strawberries from triads were not heavier ( F 1,16 = 0.091; P = 0.122; Extended Data Fig. 6a ), but they were 30.3% more frequently Class I than those at monad sites ( t 11.3 = 3.263; P = 0.007; Extended Data Fig. 6b ), indicating that pollination was more effective at sites with more habitats. Triad sites were also more consistent in yielding high proportions of Class I fruits (Brown–Forsythe’s test F 1,11 = 10.65; P = 0.007; Extended Data Fig. 6b ).
Pollination is improved by several and varied pollinators 21 , 22 but, although we found overall community differences between monads, dyads and triads (Fig. 2 ), pollinators were not more abundant or more species-rich at triads (Extended Data Fig. 1a,b ). Therefore, to investigate if differences in pollination function could be explained by community interaction differences, we assessed the interaction complementarity of the pollinator community at each site by calculating the dietary dissimilarity of all recorded flower visitors, which has been shown experimentally to improve pollination success 23 . We then used a principal coordinate analysis and calculated the dispersion of the community to evaluate the breadth of dietary dissimilarity across all species at each site; thus sites with more dispersed diets will have higher interaction complementarity ( Methods ). Flower-visiting species at triads differed more in their diets (that is, had greater interaction complementarity) than those at monads ( t 10 = 8.42, P < 0.001; Extended Data Fig. 6c ). Thus, although triads do not support more abundant or rich insect communities, they do host sets of pollinator species with more complementary diets; this observation is robust to the removal of rare or under-sampled species (Supplementary Information section 2 ). Flower–visitor interaction complementarity predicted the proportion of Class I strawberries ( F 3,14 = 3.475; P = 0.045; Extended Data Fig. 6d ), which were higher with greater interaction complementarity but not fruit weight ( F 3,14 = 1.211; P = 0.342) which did not differ across monad and triad sites.
Lastly, we sought to understand whether the observed patterns in community structure and interaction complementarity of triads were due to an additive effect of several habitats or whether habitat combinations in the landscape present emergent properties. We used a null model and focused on the largest of the component networks: the plant–pollinator network. For each triad site, we created 1,000 null triads from independent observations at the component habitats (monads), while preserving the number of sampled interactions. We then calculated interaction evenness and complementarity for each null triad and compared it to the corresponding empirical triad ( Methods ). Interaction evenness was typically higher in empirical triads than null counterparts (7 of 10 triads; Fig. 4a ) whereas interaction complementarity was lower (7 of 10 triads; Fig. 4b ). These differences weakened but did not reduce to zero when controlling for the number of plant species at the site, indicating that the greater plant species richness of triads (Fig. 2b ) only partially explains these emergent properties (Fig. 4c,d ). It is possible that interaction evenness and complementarity could be due to differences in the plant phylogenetic diversity at a site if distinct phylogenetic groups are associated with different habitats and thus establish distinct sets of interactions. Increased interaction evenness and decreased complementarity at empirical versus null triads could, therefore, be associated with differences in plant phylogenetic diversity, which is indeed positively correlated with interaction complementarity (repeated measures correlation test r 9,969 = 0.158, P < 0.001 and r 9,939 = 0.158, P < 0.001 in Extended Data Fig. 7b,d , respectively) and negatively correlated with interaction evenness ( r 9,969 = −0.238, P < 0.001 and r 9,939 = −0.201, P < 0.001 in Extended Data Fig. 7a,c , respectively). When constraining both models to include equal sampling completeness, the trend is weakened but broadly similar (Supplementary Information section 3 and Extended Data Fig. 8 ).
Two variants of the same null model examining the potential for emergent properties at real triad habitat combinations. a – d , Controlling only for the number of interactions ( a , b ); also controlling for the number of plant species ( c , d ). Boxplots of the null model values of the interaction evenness ( a , c ) and functional dispersion ( b , d ) are plotted against the observed value at each site. Interaction complementarity is calculated as the functional dispersion of species at the real or null triads. If the boxplot overlaps the 1:1, then the observed value falls within the null hypothesis; if it is below, the observed value is less than under the null hypothesis and, if above, more than under the null hypothesis. For each boxplot (one per site), n = 1,000 replicates. Boxes represent the 25% (Q1) and 75% (Q3) quartiles around the median line and whiskers are Q1 − 1.5× IQR to Q1 and Q3 to Q3 + 1.5× IQR.
Landscape-scale effects are various and likely to contain trade-offs. Our results show that landscapes comprising several habitats support higher species and interaction evenness, more functionally diverse communities, with more consistent stability and greater pollination function, probably due to increased environmental heterogeneity at the landscape scale. Indeed, at the habitat-scale, a heterogeneous habitat structure allows for more niches and therefore higher biodiversity 3 , 7 , 24 . Our conclusions are unlikely to be confounded by surrounding patch size (Supplementary Information section 4 ) or sampling completeness differences (Supplementary Information section 5 ). For practicality, many management plans are habitat specific and focus on protecting habitat patches that are large and connected to similar ones (for example, many of the Living Landscapes, United Kingdom 2 ; and Natura 2000 networks, European Union; along with prairie restoration projects, USA 25 ). If multi-habitat landscapes are supporting communities with improved structure and functionality and more consistent robustness to species loss, as shown here, then maintaining diverse connected natural habitats across the wider landscape is likely also to be important. This is key to species conservation, as some species may depend on several habitats for different life stages (for example, different habitat requirements for larval herbivory and adult floral resources), ecological needs (for example, nesting in one habitat but foraging in another) or maximizing of resource availability across seasons (for example, the phenology of flowering plants varies among habitats).
Landscape simplification and habitat loss are significant stressors on biodiversity, community structure and ecosystem function. We found that more natural habitats provide a greater consistency in robustness. The higher variability in single-habitat landscapes means that extremes of robustness are more common, putting communities at individual sites more frequently at risk of cascading effects of species loss. At the landscape scale, the benefits of multi-habitat configurations therefore allow for a buffering effect, even if there is no average loss of robustness in landscapes with fewer habitats; more robust habitats might compensate for lower robustness in contiguous habitats resulting in a landscape-level decrease in robustness variability. To explore the mechanistic path underlying this trend (for example, with path analysis) would require several replications for each habitat combination; however, our results point to potential effects of landscape heterogeneity on community stability being mediated through interaction evenness 26 (Fig. 2e ) and tuned by plant phylogenetic diversity 27 (Extended Data Fig. 7a,c ). Our model indicates that this relationship between several habitats and robustness variability is more pronounced when species have greater flexibility to switch resources. This rewiring flexibility might be an important community response to stressors such as climate change and biodiversity loss 28 , suggesting that multi-habitat landscape configurations could provide even greater protection against environmental change.
Robustness to species loss and the rewiring of interactions are both related to interaction generalism. A greater proportion of generalists in the empirical triads could explain lower interaction complementarity compared to the null triads; yet, empirically, triad interaction complementarity was still higher than in empirical monads. Our field experiment indicates that several habitats supported better pollination with better-quality fruit set, which is explained not by more flower-visiting species or increased visits but rather by this higher interaction complementarity of pollinators at triads. Interaction complementarity, which could reduce the effect of conspecific pollen deposition 29 , positively correlates with the phylogenetic diversity of the plants supporting the food webs, suggesting that multi-habitat landscapes might increase complementarity through an increase in plant phylogenetic (and presumably functional) diversity.
Several habitat landscapes may therefore support both more interaction complementarity (for successful plant reproduction 30 ) than single-habitat landscapes and greater redundancy through generalist species (which is important for robustness 31 ) than expected by compiling the interactions from several independent habitats. Collectively, this link from landscape composition, through the plant–insect community structure, to ecosystem function provides a mechanism through which several habitats across the landscape can support stability and better ecosystem services.
We sampled 30 sites in southwest England and southern Wales, each containing one, two or three of six habitat types: woodland, heathland, grassland, salt marsh, sand dunes and scrub. We sampled at ten single-habitat sites, monads, ten two-habitat sites, dyads and ten three-habitat sites, triads. The site size sampled remained constant, thus monads were a single habitat 9 ha in size, dyads consisted of two adjacent 4.5 ha habitats and triads consisted of three adjacent 3 ha habitats. A 9 ha field site size was selected to capture the diversity of different taxa across monads, dyads and triads, while also allowing effective sampling of all taxa at each site (plants, herbivores, pollinators and parasitoids). All sites were surrounded by the same habitats as those in the field site or water, urban environment or farmland habitats and each field site was visited once in May–September 2014 and three times in April–September 2015. In 2015, all sites were visited before any site was repeated. In each sampling round, sites were visited in ten three-site cycles, each comprising one monad, dyad and triad. The order of visited sites was randomized within both cycle and round.
Potential sites were initially selected with Arc GIS v.10.1 using the 2007 Land Cover Map 32 . Three GIS models selected sites that were (1) single-habitat 9 ha sites with a 500 m buffer that did not include any other habitat of interest (for example, urban, farmland or water were allowed in the buffer); (2) two 4.5 ha contiguous habitats with a 500 m buffer that did not include any other habitat of interest; and (3) three 3 ha contiguous habitats with a 500 m buffer that did not include any other habitat of interest. This created a long list of potential sites that were then verified and narrowed down using satellite images (Google Earth 2013) and finally ground-truthed to confirm habitat types, make final selections and outline appropriate habitat plots. The final site list was based on ease of access, travel time, the need to avoid geographic clustering of any site (Fig. 1a ) or habitat type, along with our ability to secure permission to sample. We selected habitat combinations such that habitats were represented equally across monads, dyads and triads, while accounting for the restrictions of what was available in the southwest United Kingdom. Because some habitat combinations did not exist in accordance with our selection criteria and others consistently occur together (for example, sand dunes and salt marshes often border grasslands), across monads, dyads and triads, we sampled fewer sand dunes and salt marshes and more grassland and scrub. For a full list of sites see Supplementary Table 3 .
At each site, on each visit, we sampled along six 35 m transects arranged as follows: six transects in the one monad habitat, three in each dyad habitat and two in each triad habitat. The transect start location and direction were randomly selected before arrival on site and changed on each of the four visits. Thus, in total we sampled along 24 transects at each site.
We designed our data collection under the assumption that sampling intensity is the main driver of species–area relationships, whereas the influence of patch size on per-unit-area (alpha) diversity is weak or absent 33 , 34 . Thus, larger patches typically have more species in total because they contain a variety of microhabitats. Repeated sampling across these larger patches would capture more microhabitats and therefore show high between-sample (beta) diversity 34 . Therefore, to control for these area effects on richness, the sampling effort was consistent at the site level 33 , 34 and thus we standardized the number of samples per site to avoid a patch size bias. Collection protocol closely followed ref. 12 and is described below.
On each visit, a 0.5 × 0.5 m 2 gridded quadrat was placed on alternating sides of the transect every 10 m, resulting in four quadrats per transect. All plants were identified and given a vegetation abundance score (as in refs. 12 , 35 ). Category 1 species were rare, only present once to a few times (vegetation occupied 1–2% of the quadrat area), category 2 were present in high enough numbers to be seen easily (occupied less than 10% of the quadrat area), category 3 could be seen throughout the quadrat (less than 50% of the area) and category 4 were dominated by the given species (more than 50% of the area). Tree vegetation to a height of 2 m and grasses, the latter collectively pooled, were all classified on this 1–4 scale. All other flowering plant species were identified 36 and floral abundance was further classified with buds, open, wilted and seed-set floral units counted. We calculated these per floral unit for flowers arranged in umbels, heads, capitula and spikes. Vegetation cover for flowering species was determined by the number of times a plant touched one of the 36 cross points formed by the intersecting grids on the quadrat. Any plant species that did not fall within a quadrat but which occurred within 30 m of the transect, were recorded but not included in quantitative analysis.
On each visit, between 09:00 and 17:30 in dry, warm (minimum 15 °C) conditions with little to no wind, flower visitors were sampled by haphazardly walking for 20 min, no more than 30 m from the transect. All insects found on a flower head were collected using a hand net. Visited flowers were identified to species in the field and flower visitors were identified to species by taxonomists (see ‘Acknowledgements’).
On each visit, we collected leaf miners and caterpillars from 1 m 2 quadrats every 10 m on either side of the transect by visual searching of leaves to a height of 2 m. They were collected and stored individually and returned to the laboratory for rearing.
Leaf miners were initially identified from the leaf mine pattern 37 , 38 and caterpillar species were identified at larval stage 39 . Individual larvae from both groups were reared in separate pots and checked every 2–3 days for emergence. Emerged adults, either parasitoid or herbivore, were identified by taxonomists (see Acknowledgements). We used adult identification of surviving individuals to confirm larval identifications, where possible, to ensure accurate identification for herbivores that were either killed by a parasitoid or died during rearing.
Seed herbivores and their parasitoids were collected in seeds in the first and fourth sampling rounds (that is, once in September 2014 and once in August–September 2015). Along each transect, we collected up to 50 seeds from plants expected to host seed feeders 12 , 40 . Seeds were collected from haphazardly sampled plants within 10 m of the transect and, where possible, from different plants, equally spaced along the transect. Each sample of up to 50 seeds was stored collectively in the same pot and checked weekly until adult herbivores and parasitoids emerged (up to 8 months). Each emerged insect, seed feeder or parasitoid, was collected, stored individually and identified by taxonomists. Insects were successfully reared from 23 plant species ( Anthyllis vulneraria, Aster tripolium, Centaurea nigra, Cirsium arvense, Cirsium eriophorum, Cirsium palustre, Cirsium vulgare, Crataegus monogyna, Lathyrus pratensis, Lotus corniculatus, Ornithopus perpusillus, Rhinanthus minor, Rosa rugose, Rubus fruticosus agg., Senecio jacobaea, Succisa pratensis, Trifolium arvense, Trifolium pratense, Trifolium repens, Ulex europaeus, Ulex galii, Viccia sativa and Vicia cracca ).
Between 4 and 14 July 2015, we placed 20 wild strawberry plants ( F. vesca ) in four 15 l buckets in the centre of each monad and triad; dyads were excluded to allow all plants to be placed and retrieved in the flowering time. Each bucket was surrounded by chicken wire to discourage disturbance by wildlife and livestock (Extended Data Fig. 4 ). Plants were at the point of flowering when put in the field, then left for 14 days to allow for natural pollination and then retrieved. It was not possible to collect data on strawberry visitation as the plants could only be left in position for a short period (as seed set occurs relatively quickly) and it was not feasible to simultaneously sample 20 geographically distant field sites (Fig. 1 ) in a meaningful fashion to record pollinator visitation during this period. F. vesca was selected as it grows naturally in the region, is visited by a wide range of pollinators 8 and, although partly wind pollinated, insects are crucial for its successful, uniform pollination 21 , leading to higher seed set and more symmetrical fruits. Moreover, in commercial varieties, better pollination is linked with increased shelf life and market value 13 . At the end of the field experiment, we removed all new flower buds and stored the plants in an insect-free greenhouse, watering daily. Strawberries were picked when ripe, weighed and graded according to commercial symmetry ratings 41 : fruit containing only mild defects in shape (Class I) and those with more severe defects (Class II) (see Extended Data Fig. 5 for example pictures); fruit symmetry significantly affects the market value of commercial strawberries, hence the existence of a grading system. Fruit classes were assigned blindly by an assessor with no knowledge of the field sites, to avoid assessment bias. We stopped fruit collection on greenhouse-stored plants after 28 days.
Data across all visits were summed to create one network per site with edges weighted by interaction frequency. All analyses were performed and graphs created in the R statistical environment (v.3.6.0) 42 . All data and code are available at Zenodo ( https://doi.org/10.5281/zenodo.11184586 ) 43 .
We used a MANOVA to test for overall differences in community and network structure among monads, dyads and triads, based on plant species richness, floral abundance, insect species richness, insect abundance, Pielou’s species evenness and interaction evenness, which are expected to change according to land use 44 . Species evenness was calculated using vegan 45 and interaction evenness using bipartite 46 . To determine the factors contributing to the MANOVA results, we performed pairwise MANOVAs between landscape types and general linear models for each of the six structural aspects (response variables). All residuals were normally distributed and homoscedastic, except for floral abundance, which required a log-transformation: log( x + 1).
To determine the response of ecological communities to species extinction, we evaluated the robustness of plant–insect networks to extinction of plant species from the least to the most common (as in ref. 8 ). We evaluated how common a plant species is by its average proportion in the landscape; the commonness C is of plant species i in site s calculated as:
where H s is the number of habitats in site s , a ij / A j is the proportion of plant species i in habitat j , defining A j as the total abundance of plant species in habitat j and a ij being the abundance of plant species i in habitat j . To calculate this metric for a given plant species, we used its average number of quadrat cross points in each habitat for a given site as a proxy of its local relative abundance.
We modelled a flexible behavioural response of upper trophic levels to their host plant’s extinction. Specifically, the extinction of a plant species can generate cascading loss of species which then allows for rewiring of the network. Herein, we assume that insects are able to reallocate part of their diet on similar resources/hosts, which we determined by identifying the taxa on which species with a similar niche (that is, sharing part of their diet with the focal insect species) feed 16 . Following ref. 19 , we allowed species to reallocate lost interactions to alternative resources/hosts following a primary extinction and probability to interact with a given alternative resource/host was proportional to its abundance (approximated with interaction frequencies). We explored a range of species flexibility, from 0% flexibility (no rewiring allowed) to 100% flexibility (full reallocation of all lost interactions), including intermediate flexibility levels: 25% and 50%. We also explored the range of species’ sensitivity to interaction loss, expressed as a percentage of observed feeding interaction events below which a species is considered extinct: 25%, 50% and 75% of lost interactions (all cases are shown in Extended Data Figs. 2 and 3 and robustness to extinctions with full reallocation and 50% sensitivity level is shown in Fig. 3 ). We extend the approach of ref. 19 to multipartite networks with species interacting at different life stages, by assuming that if one life stage of one species goes extinct (for example, caterpillars), so do the others (for example, the corresponding adult butterfly in the flower–visitor network). Thus, species loss can propagate between two types of networks as a result of species being pollinators, herbivores or even parasitoids during different life stages or ecological requirements. Our approach allows rewiring alternatives and species extinction to be evaluated, respectively, for each interaction type and, for each life stage, when applicable.
We repeated this simulation 100 times, both under this scenario and under random removal. Whereas the former tests the response to plant species loss, the latter provides a control scenario accounting for the contribution of basic properties of the networks (size, number of links and so on) to community robustness.
The effect of habitat numbers (one or three) on fruit weight and the proportion of Class 1 strawberries (a measure of fruit quality that is determined by pollination success) was assessed using a mixed effect model with site as a random effect and the landscape type (monad or triad) as a fixed effect, using the package lme4 (ref. 47 ).
Our field experiment supported the idea that pollination function is higher in sites with three habitats than in sites with a single habitat, even if pollinator richness and abundance were similar in sites with different numbers of habitats. Therefore, we asked whether the pollinator communities of sites with more habitats use floral resources in more complementary ways, compared with single-habitat sites, given that there is evidence that interaction complementarity can be associated with increased function 23 . Our measure of interaction complementarity was adapted from functional diversity analysis methods (for example, refs. 48 , 49 ) which measures, for instance, the breadth in species functional traits in ecological communities. Here we measure the breadth of pollinators’ use of flower resources; under the assumption that pollinator species with more dissimilar patterns of resource use complement the resource use of other pollinators, thereby increasing pollination function of the community 22 .
We started by computing the dissimilarity in resource use of pollinators from the plant–pollinator network data. To: (1) have a more complete picture of how complementary pollinator species interactions were in our communities and (2) create a multidimensional functional space which was comparable across sites, we computed a Bray–Curtis dissimilarity matrix from the complete plant–pollinator interaction network, that is all 30 sites pooled together in one interaction network. To control for a potential sampling completeness bias across pollinator species, we normalized pollinator interaction weights, so that interaction weights for each pollinator species summed to 1. We then performed a principal coordinate analysis (PCoA), to place pairwise dissimilarities into a multidimensional space, in which each species is represented by one point and Euclidean distances between species are proportional to their dissimilarities in resource use (Supplementary Fig. 1 ). We assessed the quality of the functional space using the mean squared deviation index 50 and with no obvious break point, selected the space with ten dimensions. Using the FD package 51 , we then calculated the functional dispersion of each monad and triad site, measured as the sum of the distances of all species in that site to the community centroid. We took this to represent the overall pollinator interaction complementarity at the site level.
Data were normally distributed and had unequal variance, thus we used a Welch’s two sample t -test to determine the difference between the interaction complementarity at monads and triads. Using a linear model, we then tested whether interaction complementarity at each site predicted fruit weight and the proportion of Class 1 strawberries.
Using a null model approach 27 , 52 , we tested whether the network properties we measured on the sites with several habitats are different from those expected if landscape-scale food webs are simply the sum of their habitats (the null hypothesis). To do so, we first created null triads, that is landscape-scale networks constructed from data collected at monad sites randomly assembled to represent the empirical triad landscapes. Then, we quantified interaction evenness and interaction complementarity for null triads and compared these to empirical triads.
Similar to other measures of biodiversity, network properties are expected to be affected by the size of the sampling area 53 as in Supplementary Fig. 2 for three hypothetical habitats: H 1 , H 2 and H 3 . If landscape-scale food webs are simply the sum of their habitats, then the null hypothesis should translate as follows in terms of species richness and number of interspecific interactions: a triad made of { H 1 , H 2 , H 3 } (each of area Δ /3, Δ being the area of the sampled site) should have fewer or an equal number of links \({L}_{\left\{{H}_{1},{H}_{2},{H}_{3}\right\},\left\{\varDelta /3,\varDelta /3,\varDelta /3\right\}}\) than the sum of the links in each habitat of size Δ /3 (that is, \({L}_{{H}_{1},\varDelta /3}+{L}_{{H}_{2},\varDelta /3}+{L}_{{H}_{3},\varDelta /3}\) ). Indeed, a lower number of links would occur if interactions are shared across several habitats. The same rationale applies to the number of species S :
We created two nested null models that generate random plant–insect interaction networks to test emergent properties within triads.
Null model no. 1 . For an observed triad T k made of { H 1 , H 2 , H 3 }, we created three random networks for each habitat H i by subsampling the observed monad networks in the corresponding habitats. Thus, a null triad of woodland, heathland and grassland would be created by subsampling the equivalent number of interactions from woodland, heathland and grassland monads. A random network for the habitat H i in the triad T k was generated by subsampling \({{N}}_{{{T}}_{{k}},{{H}}_{{i}}}\) (the number of interaction events to consider for the {triad T k , habitat H i }-tuple) interactions among those observed in the monad with habitat type H i (hereafter, monad \({{M}}_{{{H}}_{{i}}}\) ). The probability of sampling one interaction was assumed to be proportional to the number of times it had been observed in the monad \({{M}}_{{{H}}_{{i}}}\) . For an interaction between species b and species a in monad \({{M}}_{{{H}}_{{i}}}\) , this probability is:
where \({{N}}_{{ab},{{M}}_{{{H}}_{{i}}}}\) is the number of individuals of species b recorded interacting with species a in monad \({{M}}_{{{H}}_{{i}}}\) and \({{N}}_{{{M}}_{{{H}}_{{i}}}}\) is the total number of insect individuals seen interacting within monad \({{M}}_{{{H}}_{{i}}}\) .
Null model no. 2 . In this null model, we also controlled the diversity of the plant community to test whether it contributes to the differences between the observed triads and their random counterparts generated with null model no. 1. To this end, we follow the same steps as for the null model no. 1 but sampled interactions within a subset of monad \({{M}}_{{{H}}_{{i}}}\) involving the same number of plant species as observed interacting in the habitat H i of the triad T k .
Following the construction of each null triad, we calculated the network properties of interest (interaction evenness and interaction complementarity) and compared them to those of the observed triads. The boxplots in the insets of Fig. 4 and Extended Data Fig. 8 show the range of values for a given network value that we can reasonably expect for a given site under the null hypothesis. If the boxplot overlaps the 1:1 line (that is, x = y line), then the observed value falls within the null hypothesis. If it is below that line, the observed value is less than under the null hypothesis and, if above, more than under the null hypothesis.
To account for the effect of plant phylogenetic diversity in the empirical and simulated triads, we constructed plant phylogenetic trees for each community and measured their phylogenetic diversity as the mean tree branch length. To build one tree per community, we cropped the Daphne phylogeny, a comprehensive dated phylogeny of the European flora 54 , to only include plant species available in each of our communities. Our visitation dataset contains 149 plant species, out of which 139 species (93%) can be found in the Daphne phylogenetic tree. We standardized synonyms for species that were separately identified using Global Biodiversity Information Facility (GBIF). The remaining ten plant species that are not recorded in the Daphne phylogeny can be divided into two groups as follows (Supplementary Table 1 ):
Plant species that are the only representatives of their genus in our pollination dataset ( n = 3).
Plant species that are not the only representatives of their genus in the dataset ( n = 7).
These plant species were assigned an alternative replacement species, in their genus, selected from the available species in the phylogeny dataset. The replacements for species in the first group were randomly chosen, as fine-scale phylogenetic distances will probably be less important when a genus is represented by a single species in the data. The replacements for species in the second group were chosen more carefully, as they co-occur with congeneric species, following three steps:
We selected the species from the focal genus available in Daphne phylogeny which were not already part of the interaction dataset.
In GBIF we obtained the coordinates of the occurrences—if any—of these possible species in the United Kingdom.
For each of the seven species in the second group, we ordered their alternative species from the most to least likely species (measured as number of occurrences in our sampling area). We then selected the top most likely species, which had at least twice as many occurrences as the next likely species. Therefore, the selected species had similarly high occurrences, whereas non-selected species had half or fewer occurrences than the selected species with least occurrences.
If a community included species from the second group and for these species more than one alternative was selected, alternative trees for that community were built and their mean phylogenetic diversity calculated.
We calculated repeated measures correlations between plant phylogenetic diversity and both interaction evenness and interaction complementarity using rmcorr 55 .
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
All data and code are available at Zenodo ( https://doi.org/10.5281/zenodo.11184586 ) 43 .
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We thank field and laboratory assistants, K. White, J. Morton, M. Broyles, H. Morse, C. Doran and S. Sanghera; taxonomists, M. Wilson, J. Deeming, B. Levey, A. Polaszek, P. M. Pavett and R. Barnett, who were essential in identifying the insects; and greenhouse manager, T. Pitman, for help with strawberry plant care. A. Scott and G. Rowlands provided GIS support for site selection and habitat patch calculations, respectively. The Wildlife Trust, National Trust and private landowners allowed us access to field sites. K. Baldock and members of Community Ecology Research Oxford group provided valuable feedback and discussions. This research was funded by NERC (NE/K006568/1). J.M.T. was funded by the Marsden Fund (UOC1705).
Authors and affiliations.
School of Biological Sciences, University of Bristol, Bristol, UK
Talya D. Hackett, Alix M. C. Sauve, Kate P. Maia, Daniel Montoya, Nancy Davies, Rose Archer & Jane Memmott
Department of Biology, University of Oxford, Oxford, UK
Talya D. Hackett
Department of Computer Science, University of Bristol, Bristol, UK
Alix M. C. Sauve
University of Bordeaux, Integrative and Theoretical Ecology group, LabEx COTE, Pessac, France
Institute of Biosciences Institute, University of São Paulo, São Paulo, Brazil
Kate P. Maia
Basque Centre for Climate Change (BC3), Parque Científico UPV-EHU, Leioa, Spain
Daniel Montoya
IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading, UK
Simon G. Potts
Bioprotection Aotearoa and Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury, Christchurch, New Zealand
Jason M. Tylianakis
School of Biosciences, Cardiff University, Sir Martin Evans Building, Cardiff, UK
Ian P. Vaughan
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The study was conceived by J.M. and D.M. and designed by T.D.H., J.M., D.M. and A.M.C.S. with input from all authors. Fieldwork was carried out by T.D.H., N.D. and R.A. A.M.C.S. led on development of the robustness model. T.D.H., K.P.M. and I.P.V. carried out the interaction complementarity analysis. T.D.H., A.M.C.S. and K.P.M. developed the null model analyses. All further analyses were completed by T.D.H., A.M.C.S., K.P.M. and I.P.V. with input from all authors. T.D.H. led the writing of the manuscript, all authors contributed to drafts of the manuscript and gave final approval for publication.
Correspondence to Talya D. Hackett or Jane Memmott .
Competing interests.
The authors declare no competing interests.
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Extended data fig. 1 flower visitors structural box plots..
Differences among flower-visitors at monads, dyads and triads in terms of: A. abundance B. species richness, C. interaction evenness and D. species evenness. Circles indicate each site and the habitat combination therein with a random horizontal jitter to reduce overlap. Data are from 30 independent field sites and 5,729 flower-visitor interactions (526 species). Boxes represent the 25% (Q1) and 75% (Q3) quartiles around the median line, and whiskers are Q1 − 1.5xIQR to Q1 and Q3 to Q3 + 1.5xIQR.
Robustness calculations for each site under multiple extinction scenarios and with different levels of dietary flexibility. Dietary flexibility for insects was set at 0% (A, E, I), 25% (B, F, J), 75% (C, G, K) and 100% (D, H, L) dietary flexibility. Species’ sensitivity to interaction loss was set at: 25% (A-D), 50% (E-H) and 75% (I-L) of interactions. Plant species were removed from least to most abundant. For each landscape type (monad, dyad and triad), n = 5,000 (10 sites x 500 replicates). Boxes represent the 25% (Q1) and 75% (Q3) quartiles around the median line, and whiskers are Q1 − 1.5xIQR to Q1 and Q3 to Q3 + 1.5xIQR.
Robustness calculations for each site under multiple extinction scenarios and with different levels of dietary flexibility. Dietary flexibility for insects was set at 0% (A, E, I), 25% (B, F, J), 75% (C, G, K) and 100% (D, H, L) dietary flexibility. Species’ sensitivity to interaction loss was set at: 25% (A-D), 50% (E-H) and 75% (I-L) of interactions. Plant species were removed randomly. For each landscape type (monad, dyad and triad), n = 5,000 (10 sites x 500 replicates). Boxes represent the 25% (Q1) and 75% (Q3) quartiles around the median line, and whiskers are Q1 − 1.5xIQR to Q1 and Q3 to Q3 + 1.5xIQR.
Pollination experiment set-up at a monad (left) and triad (right). Five wild-type strawberry ( Fragaria vesca) plants were placed in each of the four buckets (total 20 plants) and surrounded with chicken wire to prevent grazing. Plants in flower bud were placed at habitat boundaries, where relevant, and as close to the centre of the plot as possible, left for two weeks and then retrieved and kept in a pollinator-free greenhouse.
Perfect pollination allows all achenes to be pollinated evenly and fully and the surrounding tissue will swell to form fleshy fruit. This results in a symmetrical, Class I fruit (left). Imperfect pollination leads to an asymmetric, Class II (right), fruit as flesh only forms around fully pollinated achenes. In the same mechanism, better pollination also leads to larger fruit. While the Class I and II terminology was developed for commercial strawberries, it can be applied to wild-type strawberries too.
Difference in (A) Fruit weight, (B) Fruit quality and (C) Interaction complementarity at Monad and Triad sites. (D) The relationship between Interaction complementarity and the proportion of class 1 strawberries. Yellow squares are Monad sites and blue triangles are triads. Interaction complementarity is calculated as the functional dispersion of the species at each site and measured as the sum of the distances of all species in that site to the community centroid. Twenty strawberry plants were placed at 20 independent field sites, yielding a total of 144 strawberries. Boxes represent the 25% (Q1) and 75% (Q3) quartiles around the median line, and whiskers are Q1 − 1.5xIQR to Q1 and Q3 to Q3 + 1.5xIQR.
Interaction evenness (A&C) and interaction complementarity (B&D) as a function of plant phylogenetic diversity in random triads (coloured points) and observed triads (circles with coloured fill). Interaction complementarity is calculated as the functional dispersion of species at the real or null triads. Two variants of the same null model are displayed here, one controlling only for the number of interactions (A&B), and the other (C&D) also controlling also for the number of plant species.
Null Model results while additionally constraining for equal sampling completeness: Interaction evenness (A and E) and functional dispersion (C and G) as a function of plant phylogenetic diversity in random triads (coloured points) and observed triads (circles with coloured fill). Two variants of the same null model are displayed here, one only controlling only for the number of interactions (first row), and the other controlling also for the number of plant species. Within each plot, insets (B, D, F, H) compare the expected value of interaction evenness and functional dispersion against the observed values. For each boxplot (1 per site), n = 1,000 replicates. Boxes represent the 25% (Q1) and 75% (Q3) quartiles around the median line, and whiskers are Q1 − 1.5xIQR to Q1 and Q3 to Q3 + 1.5xIQR.
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Supplementary Information sections 1–7, including Figs. 1–14, Tables 1–3 and references.
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Hackett, T.D., Sauve, A.M.C., Maia, K.P. et al. Multi-habitat landscapes are more diverse and stable with improved function. Nature (2024). https://doi.org/10.1038/s41586-024-07825-y
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Lateralization of brain function.
The left and right hemispheres of the brain process sensory information in different ways and function differently in controlling behavior. This lateralization of brain function, originally thought to be unique to humans, is now known to occur in a broad range of non-human vertebrates and even in invertebrates, indicating that it is an essential feature of both large and small brains. Some evidence indicates that lateralization of brain function improves cognitive capacity of the brain. Many, often unrelated, brain functions are lateralized. In humans, these include differential specialization of the hemispheres to process language and produce speech, express emotions, respond to faces, attend to spatial information, and control hand use. As now clear for handedness, both genetic expression and environmental influences are involved in complex ways. Since lateral division of function in the brains of non-human animals has sufficient similarity to that of humans, it is probable that at least some asymmetries were evolutionary precursors to language and speech.
It is notable that lateralization is in no species totally in a single direction. Some evidence from studies of animal species provides support for the hypothesis that alignment of laterality in most individuals in a species occurs only for control of behavior that requires one lateralized individual to interact with another lateralized individual (i.e., in social interaction). In humans, individuals with non-right-handedness (left-handed and mixed-handed) are more prone to a number of psychiatric conditions or other behavioral conditions, including autism, schizophrenia, and dyslexia.
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Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4.
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 ...
The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research ...
A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
Essentially, a hypothesis is a tentative statement that predicts the relationship between two or more variables in a research study. It is usually derived from a theoretical framework or previous ...
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.".
A research hypothesis helps test theories. A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior. It serves as a great platform for investigation activities.
The specific group being studied. The predicted outcome of the experiment or analysis. 5. Phrase your hypothesis in three ways. To identify the variables, you can write a simple prediction in if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ...
The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility. It's advisable to focus on a single primary research question for the study. The primary question, clearly stated at the end of a grant proposal's introduction, usually specifies the study population, intervention, and ...
The scientific method can derive and test predictions as hypotheses. Empirical research can then provide support (or lack thereof) for the hypotheses. Even failure to find support for a hypothesis still represents a valuable contribution to scientific knowledge. ... While all hypotheses serve the essential function of guiding your study, there ...
With the exception of a few historic papers, the vast majority of hypotheses attract relatively small number of citations in a long term.36 Plausible explanations are that these articles often contain a single or only a few citable points and that suggested research studies to test hypotheses are rarely conducted and reported, limiting chances ...
The Function of the Hypotheses. A hypothesis states what one is looking for in an experiment. When facts are assembled, ordered, and seen in a relationship, they build up to become a theory. ... The research would be much unfocused and a random empirical wandering without it. The hypothesis provides a necessary link between the theory and ...
The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem. 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a 'if-then' structure.
Types of hypotheses include simple, complex, directional, non-directional, associative and causal, empirical, and statistical hypotheses. Each type serves a specific purpose and is used based on the nature of the research question or problem. The research hypothesis is a logical supposition and an educated prediction of the assumed relationship ...
Abstract. A hypothesis is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator's thinking about the problem and, therefore, facilitates a solution. There are three primary modes of inference by which hypotheses are developed ...
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...
Theories and Hypotheses. Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena.Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions ...
A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence. Within social science, a hypothesis can ...
At the primary level, a hypothesis is the possible and probable explanation of the sequence of happenings or data. Sometimes, hypothesis may emerge from an imagination, common sense or a sudden event. Hypothesis can be a probable answer to the research problem undertaken for study. 4. Hypothesis may not always be true.
A hypothesis is an educated guess, based on the probability of an outcome. Scientists formulate hypotheses after they understand all the current research on their subject. Hypotheses specify the relationship between at least two variables, and are testable. For a hypothesis to function properly, other scientists must be able to reproduce the ...
Research question. Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know "where the boundary between current ...
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.
Species interaction data, a field experiment and modelling of plant-insect communities show that landscapes with more habitat types support more even species, more complementary ...
This lateralization of brain function, originally thought to be unique to humans, is now known to occur in a broad range of non-human vertebrates and even in invertebrates, indicating that it is an essential feature of both large and small brains. Some evidence indicates that lateralization of brain function improves cognitive capacity of the ...