Biology subjects

Simmons, B. I.

Publications and source records attributed to Simmons, B. I..

12 recordsLinked to original sources

bioRxiv2026

Vertebrates do not display consistent declines in abundance or stability with contemporary range or realized climate niche centrality

The abundant-centre (ACH) and stable-centre hypotheses (SCH) predict population abundance and stability decline with increasing distances from a species' range and multivariate climate niche centres. ACH and SCH are widely held assumptions underpinning many ecological and conservation applications, with range and climate centrality often assumed to reflect suitability. However, empirical support that abundance (distance-abundance) and stability (distance-variability) decline away from range and climate centres is mixed and hampered by radically different climate centrality assumptions. Here, we test distance-abundance relationships in >5000 vertebrate species and distance-variability relationships in >1000 vertebrate species across their realized geographic and climate niche spaces. We found no vertebrate taxa display consistent declines in abundance or stability with increasing distances from range centres or realized climate (niche) centres modelled from species' breeding ranges, native ranges or contemporary occurrences. Distance-abundance and distance-variability relationships were weak and inconsistent across taxa irrespective of centrality metric, modelling approach, climate variables, abundance metric or dataset. Contemporary vertebrate abundance patterns, therefore, are often decoupled from position in contemporary range and realized climate space, challenging a central assumption of core-periphery models. Broadly, our study indicates that frequently used range and climate centrality metrics may be weak predictors of vertebrate abundance patterns in contemporary landscapes and biological forecasting or climate change applications should be cautious of their unvalidated use.

ecology↗

bioRxiv2022

Rapid monitoring for ecological persistence

Effective conservation of ecological communities requires accurate and up-to-date information about whether species are persisting or declining to extinction. The persistence of ecological communities is largely supported by its structured architecture of species interactions, known as an ecological network. While the persistence of the network supporting the whole community is the most relevant scale for conservation, in practice, we can only monitor small subsets of these networks due to logistical sampling constraints. There is therefore an urgent need to establish links between the small snapshots of data conservationists are able to collect, and the big picture conclusions about ecosystem health demanded by policy makers, scientists and societies. Here we show that the persistence of small subnetworks in isolation -- that is, their persistence when considered separately from the larger network of which they are a part -- is a reliable probabilistic indicator of the persistence of the network as a whole. Our results are general across both antagonistic and mutualistic interaction networks. Empirically, we show that our theoretical predictions are supported by data on invaded networks in restored and unrestored areas, even in the presence of environmental variability. Our work suggests that coordinated action to aggregate information from incomplete sampling can provide a means to rapidly assess the persistence of entire ecological networks and the expected success of restoration strategies. This could significantly improve our ability to monitor progress towards achieving policy targets, such as those enshrined in the UN Convention on Biological Diversity.

ecology↗

bioRxiv2020

Niche-based processes and neutrality influence patterns of indirect interactions in mutualistic networks

Indirect interactions are central to ecological and evolutionary dynamics in pollination communities, yet we have little understanding about the processes determining patterns of indirect interactions, such as those between pollinators through shared flowering plants. Instead, research has concentrated on the processes responsible for direct interactions and whole-network structures. This is partly due to a lack of appropriate tools for characterising indirect interaction structures, because traditional network metrics discard much of this information. The recent development of tools for counting motifs (subnetworks depicting interactions between a small number of species) in bipartite networks enable detailed analysis of indirect interaction patterns. Here we generate plant-hummingbird pollination networks based on three major assembly processes - neutral effects (species interacting in proportion to abundance), morphological matching and phenological overlap - and evaluate the motifs associated with each one. We find that different processes produce networks with significantly different patterns of indirect interactions. Neutral effects tend to produce densely-connected motifs, with short indirect interaction chains, and motifs where many specialists interact indirectly through a single generalist. Conversely, niche-based processes (morphology and phenology) produced motifs with a core of interacting generalists, supported by peripheral specialists. These results have important implications for understanding the processes determining indirect interaction structures.

ecology↗

bioRxiv2020

maxnodf: an R package for fair and fast comparisons of nestedness between networks

O_LINestedness is a widespread pattern in mutualistic networks that has high ecological and evolutionary importance due to its role in enhancing species persistence and community stability. Nestedness measures tend to be correlated with fundamental properties of networks, such as size and connectance, and so nestedness values must be normalised to enable fair comparisons between different ecological communities. Current approaches, such as using null-corrected nestedness values and z-scores, suffer from extensive statistical issues. Thus a new approach called NODFc was recently proposed, where nestedness is expressed relative to network size, connectance and the maximum nestedness that could be achieved in a particular network. While this approach is demonstrably effective in overcoming the issues of collinearity with basic network properties, it is computationally intensive to calculate, and current approaches are too slow to be practical for many types of analysis, or for analysing large networks. C_LIO_LIWe developed three highly-optimised algorithms, based on greedy, hillclimbing and simulated annealing approaches, for calculation of NODFc, spread along a speed-quality continuum. Users thus have the choice between a fast algorithm with a less accurate estimate, a slower algorithm with a more accurate estimate, and an intermediate option. C_LIO_LIWe outline the package, and its implementation, as well as provide comparative performance benchmarking and two example analyses. We show that maxnodf enables speed increases of hundreds of times faster than existing approaches, with large networks seeing the biggest improvements. For example, for a large network with 3000 links, computation time was reduced from 50 minutes using the fastest existing algorithm to 11 seconds using maxnodf. C_LIO_LImaxnodf makes correctly-normalised nestedness measures feasible for complex analyses of even large networks. Analyses that would previously take weeks to complete can now be finished in hours or even seconds. Given evidence that correctly normalising nestedness values can significantly change the conclusions of ecological studies, we believe this package will usher in necessary widespread use of appropriate comparative nestedness statistics. C_LI

ecology↗

bioRxiv2020

Poor availability of context-specific evidence hampers decision-making in conservation

Evidence-based conservation relies on robust and relevant evidence. Practitioners often prefer locally relevant studies whose results are more likely to be transferable to the context of planned conservation interventions. To quantify the availability of relevant evidence for amphibian and bird conservation we reviewed Conservation Evidence, a database of quantitative tests of conservation interventions. Studies were geographically clustered and found at extremely low densities - fewer than one study was present within a 2,000 km radius of a given location. The availability of relevant evidence was extremely low when we restricted studies to those studying biomes or taxonomic orders containing high percentages of threatened species, compared to the most frequently studied biomes and taxonomic orders. Further constraining the evidence by study design showed that only 17-20% of amphibian and bird studies used robust designs. Our results highlight the paucity of evidence on the effectiveness of conservation interventions, and the disparity in evidence for local contexts that are frequently studied and those where conservation needs are greatest. Addressing the serious global shortfall in context-specific evidence requires a step change in the frequency of testing conservation interventions, greater use of robust study designs and standardized metrics, and methodological advances to analyze patchy evidence bases.

ecology↗

bioRxiv2019

Insights from two decades of the Student Conference on Conservation Science

Conservation science is a crisis-oriented discipline focused on delivering robust answers to reducing human impacts on nature. To explore how the field might have changed during the past two decades, we analyzed 3,245 applications for oral presentations submitted to the Student Conference on Conservation Science (SCCS) in Cambridge, UK. SCCS has been running every year since 2000, aims for global representation by providing bursaries to early-career conservationists from lower-income countries, and has never had a thematic focus, beyond conservation in the broadest sense. We found that the majority of submissions to SCCS were based on primary biological data collection from local scale field studies in the tropics, contrary to established literature which highlights gaps in tropical research. Our results showed a small increase over time in submissions framed around how nature benefits people as well as a small increase in submissions integrating social science. Our findings also suggest that students and early-career conservationists could provide pathways to increased availability of data from the tropics and for addressing well-known biases in the published literature towards wealthier countries. We hope this research will motivate efforts to support student projects, ensuring data and results are published and made publicly available.

ecology↗

bioRxiv2019

The challenge of heterogeneous evidence in conservation

Conservation efforts to tackle the current biodiversity crisis need to be as efficient and effective as possible. To inform decision-makers of the most effective conservation actions, it is important to identify biases and gaps in the conservation literature to prioritize future evidence generation. We assessed the state of this global literature base using the Conservation Evidence database, a comprehensive collection of quantitative tests of conservation actions (interventions) from the published literature. For amphibians and birds, we investigated the nature of Conservation Evidence spatially and taxonomically, as well as by biome, effectiveness metrics, and study design. Studies were heavily concentrated in Western Europe and North America for birds and particularly amphibians. Studies that used the most robust study designs - Before-After Control-Impact and Randomized Controlled Trials - were also the most geographically restricted. Furthermore, there was no relationship between the number of studies in each 1x1 degree grid cell and the number of species, threatened species or data-deficient species. Taxonomic biases and gaps were apparent for amphibians and birds - some orders were absent from the evidence base and others were poorly represented relative to the proportion of threatened species they contained. Temperate forest and grassland biomes were highly represented, which reinforced observed geographic biases. Various metrics were used to evaluate the effectiveness of a given conservation action, potentially making studies less directly comparable and evidence synthesis more difficult. We also found that the least robust study designs were the most commonly used; studies using robust designs were scarce. Future research should prioritize testing conservation actions on threatened species outside of Western Europe and North America. Standardizing metrics and improving the robustness of study designs used to test conservation actions would also improve the quality of the evidence base for synthesis and decision-making.

ecology↗

bioRxiv2019

Simple study designs in ecology produce inaccurate estimates of biodiversity responses

O_LIEcologists use a wide range of study designs to estimate the impact of interventions or threats but there are no quantitative comparisons of their accuracy. For example, while it is accepted that simpler designs, such as After (sampling sites post-impact without a control), Before-After (BA) and Control-Impact (CI), are less robust than Randomised Controlled Trials (RCT) and Before-After Control-Impact (BACI) designs, it is not known how much less accurate they are.\nC_LIO_LIWe simulate a step-change response of a population to an environmental impact using empirically-derived estimates of the major parameters. We use five ecological study designs to estimate the effect of this impact and evaluate each one by determining the percentage of simulations in which they accurately estimate the direction and magnitude of the environmental impact. We also simulate different numbers of replicates and assess several accuracy thresholds.\nC_LIO_LIWe demonstrate that BACI designs could be 1.1-1.5 times more accurate than RCTs, 2.9-4.1 times more accurate than BA, 3.8-5.6 times more accurate than CI, and 6.8-10.8 times more accurate than After designs, when estimating to within {+/-}30% of the true effect (depending on the sample size). We also found that increasing sample size substantially increases the accuracy of BACI designs but only increases the precision of simpler designs around a biased estimate; only by using more robust designs can accuracy increase. Modestly increasing replication of both control and impact sites also increased the accuracy of BACI designs more than substantially increasing replicates in just one of these groups.\nC_LIO_LIWe argue that investment into using more robust designs in ecology, where possible, is extremely worthwhile given the inaccuracy of simpler designs, even when using large sample sizes. Based on our results we propose a weighting system that quantitatively ranks the accuracy of studies based on their study design and the number of replicates used. We hope these accuracy weights enable researchers to better account for study design in evidence synthesis when assessing the reliability of a range of studies using a variety of designs.\nC_LI

ecology↗

bioRxiv2019

Vulnerable species interactions are important for the stability of mutualistic networks

Species are central to ecology and conservation. However, it is the interactions between species that generate the functions on which ecosystems and humans depend. Despite the importance of interactions, we lack an understanding of the risk that their loss poses to ecological communities. Here, we quantify risk as a function of the vulnerability (likelihood of loss) and importance (contribution to network stability in terms of species coexistence) of 4330 mutualistic interactions from 41 empirical pollination and seed dispersal networks across six continents. Remarkably, we find that more vulnerable interactions are also more important: the interactions that contribute most to network stability are those that are most likely to be lost. Furthermore, most interactions tend to have more similar vulnerability and importance across networks than expected by chance, suggesting that vulnerability and importance may be intrinsic properties of interactions, rather than only a function of ecological context. These results provide a starting point for prioritising interactions for conservation in species interaction networks and, in areas lacking network data, could allow interaction properties to be inferred from taxonomy alone.

ecology↗

bioRxiv2018

Abundance drives generalisation in hummingbird-plant pollination networks

Abundant pollinators are often more generalised than rare pollinators. This could be because abundance drives generalisation: neutral effects suggest that more abundant species will be more generalised simply because they have more chance encounters with potential interaction partners. On the other hand, generalisation could drive abundance, as generalised species could have a competitive advantage over specialists, being able to exploit a wider range of resources and gain a more balanced nutrient intake. Determining the direction of the abundance-generalisation relationship is therefore a chicken-and-egg dilemma. Here we determine the direction of the relationship between abundance and generalisation in plant-hummingbird pollination networks sampled from a variety of locations across the Americas. For the first time we resolve the direction of the abundance-generalisation relationship using independent data on animal abundance. We find evidence that hummingbird pollinators are generalised because they are abundant, and little evidence that hummingbirds are abundant because they are generalised. Additionally, a null model analysis suggests this pattern is due to neutral processes: most patterns of species-level abundance and generalisation were well explained by a null model that assumed interaction neutrality. These results suggest that neutral processes play a key role in driving broad patterns of generalisation in animal pollinators across large spatial scales.\n\nDeclarationsFunding - BIS is supported by the Natural Environment Research Council as part of the Cambridge Earth System Science NERC DTP [NE/L002507/1]. JVB was funded by CERL - Engineer Research and Development Center. PKM was funded by the Sao Paulo Research Foundation (FAPESP grant #2015/21457-4). PAC was funded by the David Lack studentship from the British Ornithologists Union and Wolfson College, University of Oxford. CL was funded by the ESDEPED-UAT grant. MAM acknowledges the Consejo Nacional para Investigaciones Cientificas y Tecnologicas (Costa Rica), German Academic Exchange Service and the research funding program LOEWE-Landes-Offensive zur Entwicklung Wissenschaftlicho konomischer Exzellenz of Hesses Ministry of Higher Education, Research, and the Arts (Germany). ROP was funded by CONACyT (project 258364). MAR was supported by the State of Sao Paulo Research Foundation (FAPESP) within the BIOTA/FAPESP, The Biodiversity Institute Program (www.biota.org.br) and the Parcelas Permanentes project, as well as by Coordenacao de Pessoal de Nivel Superior (CAPES), Fundo de Apoio ao Ensino e a Pesquisa (FAEP)/Funcamp/Unicamp and The Nature Conservancy (TNC) of Brazil. LCR was supported by CNPq and Capes. MS was funded by CNPq (grant #302781/2016-1). AMMG is supported through a Marie Skodowska-Curie Individual Fellowship (H2020-MSCA-IF-2016-704409). LVD was supported by the Natural Environment Research Council (grants NE/K015419/1 and NE/N014472/1). AMMG, JS, CR and BD thank the Danish National Research Foundation for its support of the Center for Macroecology, Evolution and Climate (grant no. DNRF96). WJS is funded by Arcadia.

ecology↗

bioRxiv2018

Uncovering indirect interactions in bipartite ecological networks

Indirect interactions play an essential role in governing population, community and coevolutionary dynamics across a diverse range of ecological communities. Such communities are widely represented as bipartite networks: graphs depicting interactions between two groups of species, such as plants and pollinators or hosts and parasites. For over thirty years, studies have used indices, such as connectance and species degree, to characterise the structure of these networks and the roles of their constituent species. However, compressing a complex network into a single metric necessarily discards large amounts of information about indirect interactions. Given the large literature demonstrating the importance and ubiquity of indirect effects, many studies of network structure are likely missing a substantial piece of the ecological puzzle. Here we use the emerging concept of bipartite motifs to outline a new framework for bipartite networks that incorporates indirect interactions. While this framework is a significant departure from the current way of thinking about networks, we show that this shift is supported by quantitative analyses of simulated and empirical data. We use simulations to show how consideration of indirect interactions can highlight ecologically important differences missed by the current index paradigm. We extend this finding to empirical plant-pollinator communities, showing how two bee species, with similar direct interactions, differ in how specialised their competitors are. These examples underscore the need for a new paradigm for bipartite ecological networks: one incorporating indirect interactions.

ecology↗

bioRxiv2018

bmotif: a package for counting motifs in bipartite networks

O_LIBipartite networks are widely-used to represent a diverse range of species interactions, such as pollination, herbivory, parasitism and seed dispersal. The structure of these networks is usually characterised by calculating one or more metrics that capture different aspects of network architecture. While these metrics capture useful properties of networks, they only consider structure at the scale of the whole network (the macro-scale) or individual species (the micro-scale). Meso-scale structure between these scales is usually ignored, despite representing ecologically-important interactions. Network motifs are a framework for capturing this meso-scale structure and are gaining in popularity. However, there is no software available in R, the most popular programming language among ecologists, for conducting motif analyses in bipartite networks. Similarly, no mathematical formalisation of bipartite motifs has been developed.\nC_LIO_LIHere we introduce bmotif: a package for counting motifs, and species positions within motifs, in bipartite networks. Our code is primarily an R package, but we also provide MATLAB and Python code of the core functionality. The software is based on a mathematical framework where, for the first time, we derive formal expressions for motif frequencies and the frequencies with which species occur in different positions within motifs. This framework means that analyses with bmotif are fast, making motif methods compatible with the permutational approaches often used in network studies, such as null model analyses.\nC_LIO_LIWe describe the package and demonstrate how it can be used to conduct ecological analyses, using two examples of plant-pollinator networks. We first use motifs to examine the assembly and disassembly of an Arctic plant-pollinator community, and then use them to compare the roles of native and introduced plant species in an unrestored site in Mauritius.\nC_LIO_LIbmotif will enable motif analyses of a wide range of bipartite ecological networks, allowing future research to characterise these complex networks without discarding important meso-scale structural detail.\nC_LI

ecology↗