Patterns of coexistence of two species of freshwater turtles are affected by spatial scale

Inferring biotic interactions from the examination of patterns of species occurrences has been a central tenet in community ecology, and it has recently gained interest in the context of single-species distribution modelling. However, understanding of how spatial extent and grain size affect such inferences remains elusive. For example, would inferences of biotic interactions from broad-scale patterns of coexistence provide a surrogate for patterns at finer spatial scales? In this paper we examine how the spatial and environmental association between two closely related species of freshwater turtles in the Iberian Peninsula is affected by the geographical extent and resolution of the analysis. Species coexistence was compared across spatial scales using five datasets at varying spatial extents and resolutions. Both similarities in the two species’ use of space and in their responses to environmental variables were explored by means of regression analyses. We show that a positive association between the two species measured at broader scales can switch to a negative association at finer scales. We demonstrate that without examination of the effects of spatial scale when investigating biotic interactions using co-occurrence patterns observed at coarse resolutions, conclusions can be deeply misleading.     

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km² to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.     

Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents

Aim Biotic interactions – within guilds or across trophic levels – have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of ‘species interaction distribution models’ (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co‐occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non‐stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio‐temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species’ effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co‐occurrence datasets across large‐scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio‐temporal data on biotic interactions in multispecies communities.     

Niche and area of distribution modeling: a population ecology perspective

Statistical modeling of areas of distribution of species by correlative analysis of the environmental features of known presences has become widespread. However, to a large degree, the logic and the functioning of many of these applications remain obscure, not only due to the fact that some of the modeling methods are intrinsically complex (neural networks, genetic algorithms, generalized additive models, for example), but mainly because the role of other ecological processes affecting the species distributions sometimes is not explicitly stated. Resorting to fundamental principles of population ecology, a scheme of analysis based on separation of three factors affecting species distributions (environment, biotic interactions and movements) is used to clarify some results of niche modeling exercises. The area of distribution of a virtual species which was generated by both environmental and biotic factors serves to illustrate the possibility that, at coarse resolutions, the distribution can be approximately recovered using only information about the environmental factors and ignoring the biotic interactions. Finally, information on the distribution of a butterfly species, Baronia brevicornis , is used to illustrate the importance of interpreting the results of niche models by including hypothesis about one class of movements. The results clarify the roles of the three factors in interpreting the results of using correlative approaches to modeling species distributions or their niches.     

Exploring macroinvertebrate species distributions at regional and local scales across a sandy beach geographic continuum

Exposed sandy beaches are highly dynamic ecosystems where macroinvertebrate species cope with extremely variable environmental conditions. The majority of the beach ecology studies present exposed beaches as physically dominated ecosystems where abiotic factors largely determine the structure and distribution of macrobenthic communities. However, beach species patterns at different scales can be modified by the interaction between different environmental variables, including biotic interactions. In this study, we examined the role of different environmental variables for describing the regional and local scale distributions of common macrobenthic species across 39 beaches along the North coast of Spain. The analyses were carried out using boosted regression trees, a relatively new technique from the field of machine learning. Our study showed that the macroinvertebrate community on exposed beaches is not structured by a single physical factor, but instead by a complex set of drivers including the biotic compound. Thus, at a regional scale the macrobenthic community, in terms of number of species and abundance, was mainly explained by surrogates of food availability, such as chlorophyll a. The results also revealed that the local scale is a feasible way to construct general predictive species-environmental models, since relationships derived from different beaches showed similar responses for most of the species. However, additional information on aspects of beach species distribution can be obtained with large scale models. This study showed that species-environmental models should be validated against changes in spatial extent, and also illustrates the utility of BRTs as a powerful analysis tool for ecology data insight.     

Environmentally constrained null models: site suitability as occupancy criterion

Null models have proven to be an important quantitative tool in the search for ecological processes driving local diversity and species distribution. However, there remains an important concern that different processes, such as environmental conditions and biotic interactions may produce similar patterns in species distributions. In this paper we present an analytical protocol for incorporating habitat suitability as an occupancy criterion in null models. Our approach involves modeling species presence or absence as a function of environmental conditions, and using the estimated site-specific probabilities of occurrence as the likelihood of species occupancy of a site during the generation of "null communities". We validated this approach by showing that type I error is not affected by the use of probabilities as a site occupancy criterion and is robust against a variety of predictive performances of the species-environmental models. We describe the expected differences when contrasting classical and the environmentally constrained null models, and illustrate our approach with a data set of Dutch dune hunting spider assemblages. Together, an environmentally constrained approach to null models will provide a more robust evaluation of species associations by facilitating the distinction between mutually exclusive processes that may shape species distributions and community assembly.     

Predictor species: Improving assessments of rare species occurrence by modeling environmental co‐responses

Designing an effective conservation strategy requires understanding where rare species are located. Because rare species can be difficult to find, ecologists often identify other species called conservation surrogates that can help inform the distribution of rare species. Species distribution models typically rely on environmental data when predicting the occurrence of species, neglecting the effect of species' co‐occurrences and biotic interactions. Here, we present a new approach that uses Bayesian networks to improve predictions by modeling environmental co‐responses among species. For species from a European peat bog community, our approach consistently performs better than single‐species models and better than conventional multi‐species approaches that include the presence of nontarget species as additional independent variables in regression models. Our approach performs particularly well with rare species and when calibration data are limited. Furthermore, we identify a group of “predictor species” that are relatively common, insensitive to the presence of other species, and can be used to improve occurrence predictions of rare species. Predictor species are distinct from other categories of conservation surrogates such as umbrella or indicator species, which motivates focused data collection of predictor species to enhance conservation practices.     

Biotic interactions hold the key to understanding metacommunity organisation

Biotic interactions are fundamental drivers governing biodiversity locally, yet their effects on geographical variation in community composition (i.e. incidence-based) and community structure (i.e. abundance-based) at regional scales remain controversial. Ecologists have only recently started to integrate different types of biotic interactions into community assembly in a spatial context, a theme that merits further empirical quantification. Here, we applied partial correlation networks to infer the strength of spatial dependencies between pairs of organismal groups and mapped the imprints of biotic interactions on the assembly of pond metacommunities. To do this, we used a comprehensive empirical dataset from Mediterranean landscapes and adopted the perspective that community assembly is best represented as a network of interacting organismal groups. Our results revealed that the co-variation among the beta diversities of multiple organismal groups is primarily driven by biotic interactions and, to a lesser extent, by the abiotic environment. These results suggest that ignoring biotic interactions may undermine our understanding of assembly mechanisms in spatially extensive areas and decrease the accuracy and performance of predictive models. We further found strong spatial dependencies in our analyses which can be interpreted as functional relationships among several pairs of organismal groups (e.g. macrophytes–macroinvertebrates, fish–zooplankton). Perhaps more importantly, our results support the notion that biotic interactions make crucial contributions to the species sorting paradigm of metacommunity theory and raise the question of whether these biologically-driven signals have been equally underappreciated in other aquatic and terrestrial ecosystems. Although more research is still required to empirically capture the importance of biotic interactions across ecosystems and at different spatial resolutions and extents, our findings may allow decision makers to better foresee the main consequences of human-driven impacts on inland waters, particularly those associated with the addition or removal of key species.     

Can we disentangle predator–prey interactions from species distributions at a macro‐scale? A case study with a raptor species

There is a debate on whether an influence of biotic interactions on species distributions can be reflected at macro‐scale levels. Whereas the influence of biotic interactions on spatial arrangements is beginning to be studied at local scales, similar studies at macro‐scale levels are scarce. There is no example disentangling, from other similarities with related species, the influence of predator–prey interactions on species distributions at macro‐scale levels. In this study we aimed to disentangle predator–prey interactions from species distribution data following an experimental approach including a factorial design. As a case of study we selected the short‐toed eagle because of its known specialization on certain prey reptiles. We used presence–absence data at a 100 km2 spatial resolution to extract the explanatory capacity of different environmental predictors (five abiotic and two biotic predictors) on the short‐toed eagle species distribution in peninsular Spain. Abiotic predictors were relevant climatic and topographic variables, and relevant biotic predictors were prey richness and forest density. In addition to the short‐toed eagle, we also obtained the predictor's explanatory capacities for 1) species of the same family Accipitridae (as a reference), 2) for other birds of different families (as controls) and 3) artificial species with randomly selected presences (as null models). We run 650 models to test for similarities of the short‐toed eagle, controls and null models with reference species, assessed by regressions of explanatory capacities. We found higher similarities between the short‐toed eagle and other species of the family Accipitridae than for the other two groups. Once corrected by the family effect, our analyses revealed a signal of predator–prey interaction embedded in species distribution data. This result was corroborated with additional analyses testing for differences in the concordance between the distributions of different bird categories and the distributions of either prey or non‐prey species of the short‐toed eagle. Our analyses were useful to disentangle a signal of predator–prey interactions from species distribution data at a macro‐scale. This study highlights the importance of disentangling specific features from the variation shared with a given taxonomic level.     

How do species interactions affect species distribution models?

One of the most promising recent advances in biogeography has been the increased interest and understanding of species distribution models – estimates of the probability that a species is present given environmental data. Unfortunately, such analyses ignore many aspects of ecology, and so are difficult to interpret. In particular, we know that species interactions have a profound influence on distributions, but it is not usually possible to incorporate this knowledge into species distribution models. What is needed is a rigorous understanding of how unmeasured biotic interactions affect the inferences generated by species distribution models. To fill this gap, we develop a general mathematical approach that uses probability theory to determine how unmeasured biotic interactions affect inferences from species distribution models. Using this approach, we reanalyze one of the most important classes of mechanistic models of competition: models of consumer resource dynamics. We determine how measurements of one aspect of the environment – a single environmental variable – can be used to estimate the probability that an environment is suitable with species distribution models. We show that species distribution models, which ignore numerous facets of consumer resource dynamics such as the presence of a competitor or the dynamics of depletable resources, can furnish useful predictions for the probability that an environment is suitable in some circumstances. These results provide a rigorous link between complex mechanistic models of species interactions and species distribution models. In so doing they demonstrate that unmeasured biotic interactions can have strong and counterintuitive consequences on species distribution models.     

Incorporating biotic interactions in the distribution models of African wild silk moths (Gonometa species,Lasiocampidae) using different representations of modelled host tree distributions

Biotic interactions influence species niches and may thus shape distributions. Nevertheless, species distribution modelling has traditionally relied exclusively on environmental factors to predict species distributions, while biotic interactions have only seldom been incorporated into models. This study tested the ability of incorporating biotic interactions, in the form of host plant distributions, to increase model performance for two host‐dependent lepidopterans of economic interest, namely the African silk moth species, Gonometa postica and Gonometa rufobrunnea (Lasiocampidae). Both species are dependent on a small number of host tree species for the completion of their life cycle. We thus expected the host plant distribution to be an important predictor of Gonometa distributions. Model performance of a species distribution model trained only on abiotic predictors was compared to four species distribution models that additionally incorporated biotic interactions in the form of four different representations of host plant distributions as predictors. We found that incorporating the moth–host plant interactions improved G. rufobrunnea model performance for all representations of host plant distribution, while for G. postica model performance only improved for one representation of host plant distribution. The best performing representation of host plant distribution differed for the two Gonometa species. While these results suggest that incorporating biotic interactions into species distribution models can improve model performance, there is inconsistency in which representation of the host tree distribution best improves predictions. Therefore, the ability of biotic interactions to improve species distribution models may be context‐specific, even for species which have obligatory interactions with other organisms.     

Combining food web and species distribution models for improved community projections

The ability to model biodiversity patterns is of prime importance in this era of severe environmental crisis. Species assemblage along environmental gradients is subject to the interplay of biotic interactions in complement to abiotic filtering and stochastic forces. Accounting for complex biotic interactions for a wide array of species remains so far challenging. Here, we propose using food web models that can infer the potential interaction links between species as a constraint in species distribution models. Using a plant–herbivore (butterfly) interaction dataset, we demonstrate that this combined approach is able to improve species distribution and community forecasts. The trophic interaction network between butterfly larvae and host plant was phylogenetically structured and driven by host plant nitrogen content allowing forecasting the food web model to unknown interactions links. This combined approach is very useful in rendering models of more generalist species that have multiple potential interaction links, where gap in the literature may occur. Our combined approach points toward a promising direction for modeling the spatial variation in entire species interaction networks.     

Incorporating biotic factors in species distribution modeling: are interactions with soil microbes important?

It is increasingly recognized that species distributions are driven by both abiotic factors and biotic interactions. Despite much recent work incorporating competition, predation, and mutualism into species distribution models (SDMs), the focus has been confined to aboveground macroscopic interactions. Biotic interactions between plants and soil microbial communities are understudied as potentially important drivers of plant distributions. Some soil bacteria promote plant growth by cycling nutrients, while others are pathogenic; thus they have a high potential for influencing plant occurrence. We investigated the influence of soil bacterial clades on the distributions of bryophytes and 12 vascular plant species in a high elevation talus‐field ecosystem in the Rocky Mountain Front Range, Colorado, USA. We used an information‐theoretic criterion (AICc) modeling approach to compare SDMs with the following different sets of predictors: abiotic variables, abiotic variables and other plant abundances, abiotic variables and soil bacteria clade relative abundances, and a full model with abiotic factors, plant abundances, and bacteria relative abundances. We predicted that bacteria would influence plant distributions both positively and negatively, and that these interactions would improve prediction of plant species distributions. We found that inclusion of either plant or bacteria biotic predictors generally improved the fit, deviance explained, and predictive power of the SDMs, and for the majority of the species, adding information on both other plants and bacteria yielded the best model. Interactions between the modeled species and biotic predictors were both positive and negative, suggesting the presence of competition, parasitism, and facilitation. While our results indicate that plant–plant co‐occurrences are a stronger driver of plant distributions than plant–bacteria co‐occurrences, they also show that bacteria can explain parts of plant distributions that remain unexplained by abiotic and plant predictors. Our results provide further support for including biotic factors in SDMs, and suggest that belowground factors be considered as well.     

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

Spatial autocorrelation in species' distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species' distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse-grained models with a large extent.     

Improving species distribution models using biotic interactions: a case study of parasites,pollinators and plants

Biotic interactions have been considered as an important factor to be included in species distribution modelling, but little is known about how different types of interaction or different strategies for modelling affect model performance. This study compares different methods for including interspecific interactions in distribution models for bees, their brood parasites, and the plants they pollinate. Host–parasite interactions among bumble bees (genus Bombus: generalist pollinators and brood parasites) and specialist plant–pollinator interactions between Centris bees and Krameria flowers were used as case studies. We used 7 different modelling algorithms available in the BIOMOD R package. For Bombus, the inclusion of interacting species distributions generally increased model predictive accuracy. The improvement was better when the interacting species was included with its raw distribution rather than with its modeled suitability. However, incorporating the distributions of non‐interacting species sometimes resulted in similarly increased model accuracy despite their being no significance of any interaction for the distribution. For the Centris‐Krameria system the best strategy for modelling biotic interactions was to include the interacting species model‐predicted values. However, the results were less consistent than those for Bombus species, and most models including biotic interactions showed no significant improvement over abiotic models. Our results are consistent with previous studies showing that biotic interactions can be important in structuring species distributions at regional scales. However, correlations between species distributions are not necessarily indicative of interactions. Therefore, choosing the correct biotic information, based on biological and ecological knowledge, is critical to improve the accuracy of species distribution models and forecast distribution change.     

No species is an island: testing the effects of biotic interactions on models of avian niche occupation

Traditionally, the niche of a species is described as a hypothetical 3D space, constituted by well‐known biotic interactions (e.g. predation, competition, trophic relationships, resource–consumer interactions, etc.) and various abiotic environmental factors. Species distribution models (SDMs), also called “niche models” and often used to predict wildlife distribution at landscape scale, are typically constructed using abiotic factors with biotic interactions generally been ignored. Here, we compared the goodness of fit of SDMs for red‐backed shrike Lanius collurio in farmlands of Western Poland, using both the classical approach (modeled only on environmental variables) and the approach which included also other potentially associated bird species. The potential associations among species were derived from the relevant ecological literature and by a correlation matrix of occurrences. Our findings highlight the importance of including heterospecific interactions in improving our understanding of niche occupation for bird species. We suggest that suite of measures currently used to quantify realized species niches could be improved by also considering the occurrence of certain associated species. Then, an hypothetical “species 1” can use the occurrence of a successfully established individual of “species 2” as indicator or “trace” of the location of available suitable habitat to breed. We hypothesize this kind of biotic interaction as the “heterospecific trace effect” (HTE): an interaction based on the availability and use of “public information” provided by individuals from different species. Finally, we discuss about the incomes of biotic interactions for enhancing the predictive capacities on species distribution models.     

The effect of competition on species' distributions depends on coexistence,rather than scale alone

One of the key problems in ecology is our need to anticipate the set of locations in which a species will be found (hereafter species' distributions). A major source of uncertainty in these models is the role of interactions among species (hereafter biotic interactions). Unfortunately, it is difficult to directly study this problem at large spatial scales and we lack a clear understanding of when biotic interactions shape species' distributions. We show a simple, direct link between the ease of species' coexistence and the importance of competition for shaping species' distributions. We show that increasing the ease of species' coexistence reduces the influence of biotic interactions. Changing the spatial scale of the analysis can reduce the influence of species interactions, but only when it promotes regional coexistence. Using these ideas, we analyze the conditions under which biotic interactions alter species' distributions in a Lotka–Volterra model of competition along an environmental gradient and argue that coexistence theory, rather than scale alone, provides a guide to the influence of species interactions. Our results provide a guide to the facets of biotic interactions that are necessary to anticipate their effects on species distributions. As such, we expect our work will help the development of more realistic distribution models.     

Shared environmental responses drive co‐occurrence patterns in river bird communities

Positive or negative patterns of co‐occurrence might imply an influence of biotic interactions on community structure. However, species may co‐occur simply because of shared environmental responses. Here, we apply two complementary modelling methodologies – a probabilistic model of significant pairwise associations and a hierarchical multivariate probit regression model – to 1) attribute co‐occurrence patterns in 100 river bird communities to either shared environmental responses or to other ecological mechanisms such as interaction with heterospecifics, and 2) examine the strength of evidence for four alternative models of community structure. Species co‐occurred more often than would be expected by random community assembly and the species composition of bird communities was highly structured. Co‐occurrence patterns were primarily explained by shared environmental responses; species’ responses to the environmental variables were highly divergent, with both strong positive and negative environmental correlations occurring. We found limited evidence for behaviour‐driven assemblage patterns in bird communities at a large spatial scale, although statistically significant positive associations amongst some species suggested the operation of facilitative mechanisms such as heterospecific attraction. This lends support to an environmental filtering model of community assembly as being the principle mechanism shaping river bird community structure. Consequently, species interactions may be reduced to an ancillary role in some avifaunal communities, meaning if shared environmental responses are not quantified studies of co‐occurrence may overestimate the role of species interactions in shaping community structure.     

Predicting species establishment using absent species and functional neighborhoods

Species establishment within a community depends on their interactions with the local environment and resident community. Such environmental and biotic filtering is frequently inferred from functional trait and phylogenetic patterns within communities; these patterns may also predict which additional species can establish. However, differentiating between environmental and biotic filtering can be challenging, which may complicate establishment predictions. Creating a habitat‐specific species pool by identifying which absent species within the region can establish in the focal habitat allows us to isolate biotic filtering by modeling dissimilarity between the observed and biotically excluded species able to pass environmental filters. Similarly, modeling the dissimilarity between the habitat‐specific species pool and the environmentally excluded species within the region can isolate local environmental filters. Combined, these models identify potentially successful phenotypes and why certain phenotypes were unsuccessful. Here, we present a framework that uses the functional dissimilarity among these groups in logistic models to predict establishment of additional species. This approach can use multivariate trait distances and phylogenetic information, but is most powerful when using individual traits and their interactions. It also requires an appropriate distance‐based dissimilarity measure, yet the two most commonly used indices, nearest neighbor (one species) and mean pairwise (all species) distances, may inaccurately predict establishment. By iteratively increasing the number of species used to measure dissimilarity, a functional neighborhood can be chosen that maximizes the detection of underlying trait patterns. We tested this framework using two seed addition experiments in calcareous grasslands. Although the functional neighborhood size that best fits the community's trait structure depended on the type of filtering considered, selecting these functional neighborhood sizes allowed our framework to predict up to 50% of the variation in actual establishment from seed. These results indicate that the proposed framework may be a powerful tool for studying and predicting species establishment.     

A theory for species co-occurrence in interaction networks

The study of species co-occurrences has been central in community ecology since the foundation of the discipline. Co-occurrence data are, nevertheless, a neglected source of information to model species distributions and biogeographers are still debating about the impact of biotic interactions on species distributions across geographical scales. We argue that a theory of species co-occurrence in ecological networks is needed to better inform interpretation of co-occurrence data, to formulate hypotheses for different community assembly mechanisms, and to extend the analysis of species distributions currently focused on the relationship between occurrences and abiotic factors. The main objective of this paper is to provide the first building blocks of a general theory for species co-occurrences. We formalize the problem with definitions of the different probabilities that are studied in the context of co-occurrence analyses. We analyze three species interactions modules and conduct multi-species simulations in order to document five principles influencing the associations between species within an ecological network: (i) direct interactions impact pairwise co-occurrence, (ii) indirect interactions impact pairwise co-occurrence, (iii) pairwise co-occurrence rarely are symmetric, (iv) the strength of an association decreases with the length of the shortest path between two species, and (v) the strength of an association decreases with the number of interactions a species is experiencing. Our analyses reveal the difficulty of the interpretation of species interactions from co-occurrence data. We discuss whether the inference of the structure of interaction networks is feasible from co-occurrence data. We also argue that species distributions models could benefit from incorporating conditional probabilities of interactions within the models as an attempt to take into account the contribution of biotic interactions to shaping individual distributions of species.