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Results for “Ecology”

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bioRxiv2018

The interaction of phylogeny and community structure: linking clades’ ecological structures and trait evolution

1AimCommunity phylogenetic studies use information about species evolutionary relationships to understand the processes of community ecological assembly. A central premise of the field is that species evolution maps onto ecological patterns, and phylogeny reveals something more than species traits alone. We argue, therefore, that there is a need to better understand and model the interaction of phylogeny with species traits and community composition.\n\nInnovationWe outline a new method that identifies clades with unusual ecological structures, based around partitioning the variation of species site occupancies ({beta}-diversity). Eco-phylogenetic theory would predict that these clades should also demonstrate distinct evolutionary trajectories. We suggest that modelling the evolution of independent trait data in these clades represents a strong test of whether there is an association between species ecological structure and evolutionary history.\n\nMain conclusionsUsing an empirical dataset of mammals from around the world, we identify two clades of rodents that tend not to co-occur (are phylogenetically overdispersed), and then find independent evidence of slower rates of body mass evolution in these clades. We suggest that our approach, which assumes nothing about the mode of species trait evolution but rather seeks to explain it using ecological information, presents a new way to examine eco-phylogenetic structure.

evolutionary biology↗

bioRxiv2014

Early warning signs in social-ecological networks

A number of social-ecological systems exhibit complex behaviour associated with nonlinearities, bifurcations, and interaction with stochastic drivers. These systems are often prone to abrupt and unexpected instabilities and state shifts that emerge as a discontinuous response to gradual changes in environmental drivers. Predicting such behaviours is crucial to the prevention of or preparation for unwanted regime shifts. Recent research in ecology has investigated early warning signs that anticipate the divergence of univariate ecosystem dynamics from a stable attractor. To date, leading indicators of instability in systems with multiple interacting components have remained poorly investigated. This is a major limitation in the understanding of the dynamics of complex social-ecological networks. Here, we develop a theoretical framework to demonstrate that rising variance - measured, for example, by the maximum element of the covariance matrix of the network - is an effective leading indicator of network instability. We show that its reliability and robustness depend more on the sign of the interactions within the network than the network structure or noise intensity. Mutualistic, scale free and small world networks are less stable than their antagonistic or random counterparts but their instability is more reliably predicted by this leading indicator. These results provide new advances in multidimensional early warning analysis and offer a framework to evaluate the resilience of social-ecological networks.

Ecology↗

bioRxiv2016

EVIDENCE FOR ALTERNATE STABLE STATES IN COLLAPSING ECOLOGICAL NETWORKS

BackgroundThere has been considerable interest and progress in our perception of organized complexity in recent years. Recurrent debates on the dynamics and stability of complex systems have enriched our understanding of these systems, but generalities in the relationship between structure and dynamics are hard to come by. Although traditionally an arena for theoreticians, much of this research has been invigorated by demonstration of the existence of alternate stable equilibria in real world ecosystems such as lakes, coral reefs, forests and grasslands.\n\nResultsLinking up systems thinking with recent advances in our understanding of ecological networks opens up exciting possibilities. In an attempt to obtain general patterns of behaviour of complex systems, we have analyzed the response of eighty-six real world ecological networks to targeted extinctions, and the findings suggest that most networks are robust to loss of specialists until specific thresholds are reached in terms of geodesics. If the extinctions persist, a state change or flip occurs and the structural properties are altered drastically, although the network does not collapse. Further, we find that as opposed to simpler networks, larger networks have several such alternate states that ensure their long-term persistence and that indeed complexity does endow resilience to such networks.\n\nConclusionsThis is the first report of critical transitions in ecological networks and the implications of these findings for complex systems characterized by networks are likely to be profound with immediate significance in conservation biology, invasion biology and restoration ecology.

Ecology↗

bioRxiv2016

Bringing Elton and Grinnell together: a quantitative framework to represent the biogeography of ecological interaction networks

Biogeography has traditionally focused on the spatial distribution and abundance of species. Both are driven by the way species interact with one another, but also by the way these interactions vary across time and space. Here, we call for an integrated approach, adopting the view that community structure is best represented as a network of ecological interactions, and show how it translates to biogeography questions. We propose that the ecological niche should encompass the effect of the environment on species distribution (the Grinnellian dimension of the niche) and on the ecological interactions among them (the Eltonian dimension). Starting from this concept, we develop a quantitative theory to explain turnover of interactions in space and time i.e. a novel approach to interaction distribution modelling. We apply this framework to host parasite interactions across Europe and find that two aspects of the environment (temperature and precipitation) exert a strong imprint on species co-occurrence, but not on species interactions. Even where species co-occur, interaction proves to be stochastic rather than deterministic, adding to variation in realized network structure. We also find that a large majority of host-parasite pairs are never found together, thus precluding any inferences regarding their probability to interact. This first attempt to explain variation of network structure at large spatial scales opens new perspectives at the interface of species distribution modelling and community ecology.

Ecology↗

bioRxiv2016

Genetic Allee effects and their interaction with ecological Allee effects

SummaryO_LIIt is now widely accepted that genetic processes such as inbreeding depression and loss of genetic variation can increase the extinction risk of small populations. However, it is generally unclear whether extinction risk from genetic causes gradually increases with decreasing population size or whether there is a sharp transition around a specific threshold population size. In the ecological literature, such threshold phenomena are called \"strong Allee effects\" and they can arise for example from mate limitation in small populations.\nC_LIO_LIIn this study, we aim to a) develop a meaningful notion of a \"strong genetic Allee effect\", b) explore whether and under what conditions such an effect can arise from inbreeding depression due to recessive deleterious mutations, and c) quantify the interaction of potential genetic Allee effects with the well-known mate-finding Allee effect.\nC_LIO_LIWe define a strong genetic Allee effect as a genetic process that causes a populations survival probability to be a sigmoid function of its initial size. The inflection point of this function defines the critical population size. To characterize survival-probability curves, we develop and analyze simple stochastic models for the ecology and genetics of small populations.\nC_LIO_LIOur results indicate that inbreeding depression can indeed cause a strong genetic Allee effect, but only if individuals carry sufficiently many deleterious mutations (lethal equivalents) on average and if these mutations are spread across sufficiently many loci. Populations suffering from a genetic Allee effect often first grow, then decline as inbreeding depression sets in, and then potentially recover as deleterious mutations are purged. Critical population sizes of ecological and genetic Allee effects appear to be often additive, but even superadditive interactions are possible.\nC_LIO_LIMany published estimates for the number of lethal equivalents in birds and mammals fall in the parameter range where strong genetic Allee effects are expected. Unfortunately, extinction risk due to genetic Allee effects can easily be underestimated as populations with genetic problems often grow initially, but then crash later. Also interactions between ecological and genetic Allee effects can be strong and should not be neglected when assessing the viability of endangered or introduced populations.\nC_LI

Ecology↗

bioRxiv2016

Liberation from equations: An equation-free method reveals the ecological interaction networks within complex microbial ecosystems

O_LIMapping the network of ecological interactions is key to understanding the composition, stability, function and dynamics of microbial communities. In recent years various approaches have been used to reveal microbial interaction networks from metagenomic sequencing data, such as time-series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics.\nC_LIO_LIWe developed the sparse S-map method (SSM), which generates a sparse interaction network from a multivariate ecological time-series without presuming any mathematical formulation for the underlying microbial processes. The advantage of the SSM over alternative methodologies is that it fully utilizes the observed data using a framework of empirical dynamic modelling. This makes the SSM robust to non-equilibrium dynamics and underlying complexity (nonlinearity) in microbial processes.\nC_LIO_LIWe showed that an increase in dataset size or a decrease in observational error improved the accuracy of SSM whereas, the accuracy of a comparative equation-based method was almost unchanged for both cases and equivalent to the SSM at best. Hence, the SSM outperformed a comparative equation-based method when datasets were large and the magnitude of observational errors were small. The results were robust to the magnitude of process noise and the functional forms of inter-specific interactions that we tested. We applied the method to a microbiome data of six mice and found that there were different microbial interaction regimes between young to middle age (4-40 week-old) and middle to old age (36-72 week-old) mice.\nC_LIO_LIThe complexity of microbial relationships impedes detailed equation-based modeling. Our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments and will be improved by further developments in metagenomics sequencing technologies leading to increased dataset size and improved accuracy and precision.\nC_LI

ecology↗

bioRxiv2017

Interactions between ecological, evolutionary, and environmental processes unveil complex dynamics of island biodiversity

AimsUnderstanding how biodiversity emerges and varies in space and time is central to ecology and biogeography. Multiple processes affect biodiversity at different scales and organizational levels, hence progress in understanding biodiversity dynamics requires the integration of these underlying processes. Here we present BioGEEM (BioGeographical Eco-Evolutionary Model), a spatially-explicit, process-based model that integrates all processes hypothesized to be relevant for biodiversity dynamics and that can be used to evaluate their relative roles.\n\nLocationHypothetical oceanic islands\n\nMethodsThe model is stochastic, grid-based, and integrates ecological (metabolic constraints, demography, dispersal, and competition), evolutionary (mutation and speciation), and environmental (geo-climatic dynamics) processes. Plants on oceanic islands served as model system. We used the full model to test hypotheses about emergent patterns at different spatio-temporal scales and organizational levels (populations, species, communities, and assemblages), switching off processes to assess the importance 1) of competition for realistic population and range dynamics; 2) metabolic constraints for endemism and community composition; 3) environmental dynamics and 4) speciation for biogeographical patterns.\n\nResultsThe full model generated multiple patterns matching empirical and theoretical expectations. For example, populations were largest on young, species-poor islands. Species, particularly endemics, were better able to fill their potential range on small, species-poor islands. Richness gradients peaked at mid-elevations. The proportion of endemics was highest on old, large, and isolated environments within the islands. Species and trait richness showed unimodal temporal trends. Switching off selected processes affected these patterns, and we found most of our hypotheses supported.\n\nMain conclusionsIntegrating ecological, evolutionary, and environmental processes is essential to simultaneously generate realistic spatio-temporal dynamics at population, species, community, and assemblage level. Finally, large-scale biodiversity dynamics emerged directly from biological processes which make this mechanistic model a valuable virtual long-term field station to study the linkages between biogeography and ecology.

ecology↗

bioRxiv2017

Analyzing ecological networks of species interactions

Networks provide one of the best representations for ecological communities, composed of many species with sometimes complex connections between them. Yet the methodological literature allowing one to analyze and extract meaning from ecological networks is dense, fragmented, and unwelcoming. We provide a general overview to the field of using networks in community ecology, outlining both the intent of the different measures, their assumptions, and the contexts in which they can be used. When methodologically justified, we suggest good practices to use in the analysis of ecological networks. We anchor this synopsis with examples from empirical studies, and conclude by highlighting what identified as needed future developments in the field.

ecology↗

bioRxiv2017

A Theory For Ecological Survey Methods To Map Individual Distributions

Spatially-explicit approaches are widely recommended for ecosystem management. The quality of the data, such as presence/absence or habitat maps, affects the management actions recommended, and is, therefore, key to management success. However, available data are often biased and incomplete. Previous studies have advanced ways to resolve data bias and missing data, but questions remain about how we design ecological surveys to develop a dataset through field surveys. Ecological surveys may have multiple spatial scales, including the spatial extent of the target ecosystem (observation window), the resolution for mapping individual distributions (mapping unit), and the survey area within each mapping unit (sampling unit). We developed an ecological survey method for mapping individual distributions by applying spatially-explicit stochastic models. We used spatial point processes to describe individual spatial placements using either random or clustering processes. We then designed ecological surveys with different spatial scales and individual detectability. We found that the choice of mapping unit affected the presence mapped fraction, and the fraction of the total individuals covered by the presence mapped patches. Tradeoffs were found between these quantities and the map resolution, associated with equivalent asymptotic behaviors for both metrics at sufficiently small and large mapping unit scales. Our approach enabled consideration of the effect of multiple spatial scales in surveys, and estimation of the survey outcomes such as the presence mapped fraction and the number of individuals situated in the presence detected units. The developed theory may facilitate management decision-making and inform the design of monitoring and data gathering.

ecology↗

bioRxiv2017

Data-based, synthesis-driven: setting the agenda for computational ecology

Computational thinking is the integration of algorithms, software, and data, to solve general questions in a field. Computation ecology has the potential to transform the way ecologists think about the integration of data and models. As the practice is gaining prominence as a way to conduct ecological research, it is important to reflect on what its agenda could be, and how it fits within the broader landscape of ecological research. In this contribution, we suggest areas in which empirical ecologists, modellers, and the emerging community of computational ecologists could engage in a constructive dialogue to build on one anothers expertise; specifically, about the need to make predictions from models actionable, about the best standards to represent ecological data, and about the proper ways to credit data collection and data reuse. We discuss how training can be amended to improve computational literacy.

ecology↗

bioRxiv2017

The architecture and design of ecological null models

Many questions in ecology are best addressed using observational data because they concern spatial or temporal scales where experimentation is impractical. Null models, which make predictions in the absence of a particular ecological mechanism, are instrumental for making inferences in these situations, but which null models to use or how to best test them is often unclear; this ambiguity is problematic because different null models and tests can yield different results, suggesting contradictory ecological mechanisms. To address these challenges, this paper presents an overar ching framework for the development and testing of null models, in which desirable models and tests are obtained as solutions to mathematical optimization problems. As an example of how the framework can be applied, this paper shows how it can be used to design null model tests to check for effects of interspecific interactions on species co-occurrence patterns. A minimal sufficient statistic (metric) for effects of interspecific interactions is derived, which achieves the maximal level of data compression without losing information present in the data about interspecific in teractions. Existing, intuitive statistics are shown to lack this property. The paper then derives a statistical hypothesis test that has the greatest possible power (sen sitivity) for detecting effects of competition and facilitation given a controlled false positive rate. This test is shown numerically to improve greatly over existing tests. The optimization paradigm allows the most accurate inferences possible, and should be applicable throughout ecology where null models are used to make inferences.

ecology↗

bioRxiv2018

Accounting for location uncertainty in azimuthal telemetry data improves ecological inference

Characterizing animal space use is critical to understand ecological relationships. Despite many decades of using radio-telemetry to track animals and make spatial inference, there are few statistical options to handle these unique data and no synthetic framework for modeling animal location uncertainty and accounting for it in ecological models. We describe a novel azimuthal telemetry model (ATM) to account for azimuthal uncertainty with covariates and propagate location uncertainty into ecological models. We evaluate the ATM with commonly used estimators in several study design scenarios using simulation. We also provide illustra-tive empirical examples, demonstrating the impact of ignoring location uncertainty within home range and resource selection analyses. We found the ATM to have good performance and the only model that has appropriate measures of coverage. Ignoring animal location un-certainty when estimating resource selection or home ranges can have pernicious effects on ecological inference. We demonstrate that home range estimates can be overly confident and conservative when ignoring location uncertainty and resource selection coefficients can lead to incorrect inference and over confidence in the magnitude of selection. Our findings and model development have important implications for interpreting historical analyses using this type of data and the future design of radio-telemetry studies.

ecology↗

bioRxiv2018

Benefits And Limitations Of Three-Dimensional Printing Technology For Ecological Research

BackgroundEcological research often involves sampling and manipulating non-model organisms that reside in heterogeneous environments. As such, ecologists often adapt techniques and ideas from industry and other scientific fields to design and build equipment, tools, and experimental contraptions custom-made for the ecological systems under study. Three-dimensional (3D) printing provides a way to rapidly produce identical and novel objects that could be used in ecological studies, yet ecologists have been slow to adopt this new technology. Here, we provide ecologists with an introduction to 3D printing.\n\nResultsFirst, we give an overview of the ecological research areas in which 3D printing is predicted to be the most impactful and review current studies that have already used 3D printed objects. We then outline a methodological workflow for integrating 3D printing into an ecological research program and give a detailed example of a successful implementation of our 3D printing workflow for 3D printed models of the brown anole, Anolis sagrei, for a field predation study. After testing two print media in the field, we show that the models printed from the less expensive and more sustainable material (blend of 70% plastic and 30% recycled wood fiber) were just as durable and had equal predator attack rates as the more expensive material (100% virgin plastic).\n\nConclusionsOverall, 3D printing can provide time and cost savings to ecologists, and with recent advances in less toxic, biodegradable, and recyclable print materials, ecologists can choose to minimize social and environmental impacts associated with 3D printing. The main hurdles for implementing 3D printing - availability of resources like printers, scanners, and software, as well as reaching proficiency in using 3D image software - may be easier to overcome at institutions with digital imaging centers run by knowledgeable staff. As with any new technology, the benefits of 3D printing are specific to a particular project, and ecologists must consider the investments of developing usable 3D materials for research versus other methods of generating those materials.

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

A computational model of task allocation in social insects: ecology and interactions alone can drive specialisation

Social insect colonies are capable of allocating their workforce in a decentralised fashion; addressing a variety of tasks and responding effectively to changes in the environment. This process is fundamental to their ecological success, but the mechanisms behind it remain poorly understood. While most models focus on internal and individual factors, empirical evidence highlights the importance of ecology and social interactions. To address this gap we propose a game theoretical model of task allocation. Individuals are characterised by a trait that determines how they split their energy between two prototypical tasks: foraging and regulation. To be viable, a colony needs to learn to adequately allocate its workforce between these two tasks. We study two different processes: individuals can learn relying exclusively on their own experience, or by using the experiences of others via social learning. We find that social organisation can be determined by the ecology alone, irrespective of interaction details. Weakly specialised colonies in which all individuals tend to both tasks emerge when foraging is cheap; harsher environments, on the other hand, lead to strongly specialised colonies in which each individual fully engages in a single task. We compare the outcomes of self-organised task allocation with optimal group performance. Counter to intuition, strongly specialised colonies perform suboptimally, whereas the group performance of weakly specialised colonies is closer to optimal. Social interactions lead to important differences when the colony deals with dynamic environments. Colonies whose individuals rely on their own experience are more exible when dealing with change. Our computational model is aligned with mathematical predictions in tractable limits. This different kind of model is useful in framing relevant and important empirical questions, where ecology and interactions are key elements of hypotheses and predictions.

ecology↗

bioRxiv2018

Identifying models of a pragmatic theory of ecology

Theoretical syntheses have the role of describing and guiding knowledge generation, and are usually done by enunciating the conceptual bases that guide research in a given field. In fields that develop axiomatically, the conceptual basis can be easily identified in the set of axioms guiding model building. However, ecology does not develop axiomatically but rather pragmatically, i.e., ecologists do not build models based on a predefined set of assumptions (axioms). They rather resort to any information that seems useful to learn about ecological phenomena. Therefore, a theoretical synthesis in ecology cannot rely on the enunciation of axioms; instead, it requires identifying what information and knowledge ecologists use (i.e., what they decide is useful to learn). Here we present an approach for producing theoretical syntheses based on the information/knowledge most frequently used to learn about the world. The approach consists of (i) defining a phenomenon of interest; (ii) defining a collective of scientists studying the phenomenon; (iii) surveying the scientific studies about the phenomenon published by this collective; (iv) identifying the most relevant publications used in these studies; (v) identifying how the studies refer to the most relevant publications; (vi) synthesizing what is being used by this collective to learn about the phenomenon. We implemented the approach in a case study on the phenomenon of ecological succession, defining the collective as the scientists currently studying succession. We identified three propositions that synthesize the views of the defined collective about succession. The theoretical synthesis revealed that there is no clear division between "classical" and "contemporary" succession models, and that neutral models are being used to explain successional patterns alongside with models based on niche assumptions.By implementing the pragmatic approach in a case study, we show that it can be successfully used to produce syntheses describing the conceptual bases of a field, which have the potential to guide knowledge generation. As such, these syntheses fulfil the roles ascribed to scientific theories in the epistemological literature.

ecology↗

bioRxiv2018

Machine learning to classify animal species in camera trap images: applications in ecology

1. Motion-activated cameras (\"camera traps\") are increasingly used in ecological and management studies for remotely observing wildlife and have been regarded as among the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analyzed, typically by visually observing each image, in order to extract data that can be used in ecological analyses.\n\n2. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or \"out-of-distribution\" in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model.\n\n3. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy, and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an R package (Machine Learning for Wildlife Image Classification; MLWIC) that allows the users to A) implement the trained model presented here and B) train their own model using classified images of wildlife from their studies.\n\n4. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analyzing images. We present an R package making these methods accessible to ecologists. We discuss the implications of this technology for ecology and considerations that should be addressed in future implementations of these methods.

ecology↗

bioRxiv2018

Structured environments fundamentally alter dynamics and stability of ecological communities

The dynamics and stability of ecological communities are intimately linked with the specific interactions - like cooperation or predation - between constituent species. In microbial communities, like those found in soils or the mammalian gut, physical anisotropies produced by fluid flow and chemical gradients impact community structure and ecological dynamics, even in structurally isotropic environments. Though natural communities existing in physically unstructured environments is rare, the role of environmental structure in determining community dynamics and stability remains poorly studied. To address this gap, we used modified Lotka-Volterra simulations of competitive microbial communities to characterize the effects of surface structure on community dynamics. We find that environmental structure has profound effects on communities, in a manner dependent on the specific pattern of interactions between community members. For two mutually competing species, eventual extinction of one competitor is effectively guaranteed in isotropic environments. However, addition of environmental structure enables long-term coexistence of both species via local pinning of competition interfaces, even when one species has a significant competitive advantage. In contrast, while three species competing in an intransitive loop (as in a game of rock-paper-scissors) coexist stably in isotropic environments, structural anisotropy disrupts the spatial patterns on which coexistence depends, causing chaotic population fluctuations and subsequent extinction cascades. These results indicate that the stability of microbial communities strongly depends on the structural environment in which they reside. Therefore, a more complete ecological understanding, including effective manipulation and interventions in natural communities of interest, must account for the physical structure of the environment.\n\nSIGNIFICANCEMany microbial communities of ecological and medical importance reside in complex and heterogeneous environments, such as soils or intestinal tracts. While many studies consider the effects of flow or chemical gradients in structuring these communities, how the physical structure of the environment shapes community dynamics and outcomes remains poorly understood. Using simulations of competitive microbial communities, we show that stability and dynamics qualitatively shift in environments with complex surface structures compared to open isotropic environments. Therefore, in addition to biochemical interactions between species, our work suggests that the physical structure of the environment is an equally important determinant of dynamics and stability in microbial communities, in a manner dependent on the specific patterns of interactions within that community.

ecology↗