Pairwise versus presence–absence approaches for analysing biodiversity patterns

It has been proposed that the study of co‐occurrence of species, which is traditionally performed using full presence–absence matrices of sets of many species, could benefit from simply testing for random co‐occurrence between pairs of species, and that use of a full presence–absence matrix is tantamount to regarding it as having some real ecological identity. Here I argue that although there are valid questions that can be answered using a pairwise approach, there are many others that naturally require the analysis of entire sets of species in a joint way, as provided for through the use of full presence–absence matrices. Moreover, there are theoretical and mathematical advantages to the use of presence–absence matrices, a few of which are briefly discussed in this short note.     

Species co‐occurrence analysis: pairwise versus matrix‐level approaches

Veech (2013, Global Ecology and Biogeography, 22 , 252–260) introduced a formula to calculate the probability of two species co‐occurring in various sites under the assumption of statistical independence between the two distributional patterns. He presented his model as a new procedure, a ‘pairwise approach’, different from analyses of whole presence–absence matrices to examine patterns of co‐occurrence. Here I show that: (1) Veech's method is identical to Fisher's exact test, a standard procedure for measuring the statistical association between two discrete variables; (2) in a broad sense, the pairwise approach is very similar to early analyses of spatial association, such as the one advanced by Forbes in 1907; (3) implicit in Veech's formula is a sampling scheme that is indistinguishable from well‐known matrix‐level null models that randomize the distribution of species among equiprobable sites; (4) pairwise co‐occurrence patterns can be analysed using any matrix‐level null model, so pairwise comparisons are not limited to using Veech's formula. The methodological distinction that Veech proposed between pairwise and matrix‐level approaches does not in fact exist, although the conceptual distinction between the two approaches is still a debated topic.     

A probability-based analysis of temporal and spatial co-occurrence in grassland birds

Aim To test for non‐random co‐occurrence in 36 species of grassland birds using a new metric and the C‐score. The analysis used presence–absence data of birds distributed among 305 sites (or landscapes) over a period of 35 years. This analysis departs from traditional analyses of species co‐occurrence in its use of temporal data and of individual species’ probabilities of occurrence to derive analytically the expected co‐occurrence between paired species. Location Great Plains region, USA. Methods Presence–absence data for the bird species were obtained from the North American Breeding Bird Survey. The analysis was restricted to species pairs whose geographic ranges overlapped. Each of 541 species pairs was classified as a positive, negative, or non‐significant association depending on the mean difference between the observed and expected frequencies of co‐occurrence over the 35‐year time‐span. Results Of the 541 species pairs that were examined, 202 to 293 (37–54%) were positively associated, depending on which of two null models was used. However, only a few species pairs (<5%) were negatively associated. An additional 89 species pairs did not have overlapping ranges and hence represented de facto negative associations. The results from analyses based on C‐scores generally agreed with the analyses based on the difference between observed and expected co‐occurrence, although the latter analyses were slightly more powerful. Main conclusions Grassland birds within the Great Plains region are primarily distributed among landscapes either independently or in conjunction with one another. Only a few species pairs exhibited repulsed or segregated distributions. This indicates that the shared preference for grassland habitat may be more important in producing coexistence than are negative species interactions in preventing it. The large number of non‐significant associations may represent random associations and thereby indicate that the presence/absence of other grassland bird species may have little effect on whether a given focal species is also found within the landscape. In a broader context, the probability‐based approach used in this study may be useful in future studies of species co‐occurrence.     

Co‐occurrence is not evidence of ecological interactions

There is a rich amount of information in co‐occurrence (presence–absence) data that could be used to understand community assembly. This proposition first envisioned by Forbes (1907) and then Diamond (1975) prompted the development of numerous modelling approaches (e.g. null model analysis, co‐occurrence networks and, more recently, joint species distribution models). Both theory and experimental evidence support the idea that ecological interactions may affect co‐occurrence, but it remains unclear to what extent the signal of interaction can be captured in observational data. It is now time to step back from the statistical developments and critically assess whether co‐occurrence data are really a proxy for ecological interactions. In this paper, we present a series of arguments based on probability, sampling, food web and coexistence theories supporting that significant spatial associations between species (or lack thereof) is a poor proxy for ecological interactions. We discuss appropriate interpretations of co‐occurrence, along with potential avenues to extract as much information as possible from such data.     

Bat assemblages on Neotropical land-bridge islands: nested subsets and null model analyses of species co-occurrence patterns

A fundamental goal of ecology is to understand whether ecological communities are structured according to general assembly rules or are essentially dictated by random processes. In the context of fragmentation, understanding assembly patterns and their mechanistic basis also has important implications for conservation. Using distribution data of 20 bat species collected on 11 islands in Gatún Lake, Panama, we tested for non‐randomness in presence–absence matrices with respect to nestedness and negative species co‐occurrence. We examined the causal basis for the observed patterns and conducted separate analyses for the entire assemblage and for various species submatrices reflecting differences in species’ trophic position and mobility. Furthermore, we explored the influence of weighting factors (area, isolation, abundance) on co‐occurrence analyses. Unweighted analyses revealed a significant negative co‐occurrence pattern for the entire assemblage and for phytophagous bats alone. Weighting analyses by isolation retained a pattern of species segregation for the whole assemblage but nullified the non‐random structure for phytophagous bats and suggested negative associations for animalivores and species with low mobility. Area‐ and abundance‐weighted analyses always indicated random structuring. Bat distributions followed a nested subset structure across islands, regardless of whether all species or different submatrices were analysed. Nestedness was in all cases unrelated to island area but weakly correlated with island isolation for incidence matrices of all species, phytophagous bats, and mobile species. Overall, evidence for negative interspecific interactions indicative of competitive effects was weak, corroborating previous studies based on ecomorphological analyses. Our findings indicate that bat assemblages on our study islands are most strongly shaped by isolation effects and species’ differential movement and colonization ability. From a conservation viewpoint this suggests that even in systems with high fragment–matrix contrast, a purely area‐based approach may be inadequate, and structural and functional connectivity among patches are important to consider in reserve planning.     

Research frontiers in null model analysis

Null models are pattern‐generating models that deliberately exclude a mechanism of interest, and allow for randomization tests of ecological and biogeographic data. Although they have had a controversial history, null models are widely used as statistical tools by ecologists and biogeographers. Three active research fronts in null model analysis include biodiversity measures, species co‐occurrence patterns, and macroecology. In the analysis of biodiversity, ecologists have used random sampling procedures such as rarefaction to adjust for differences in abundance and sampling effort. In the analysis of species co‐occurrence and assembly rules, null models have been used to detect the signature of species interactions. However, controversy persists over the details of computer algorithms used for randomizing presence–absence matrices. Finally, in the newly emerging discipline of macroecology, null models can be used to identify constraining boundaries in bivariate scatterplots of variables such as body size, range size, and population density. Null models provide specificity and flexibility in data analysis that is often not possible with conventional statistical tests.     

Disentangling community patterns of nestedness and species co-occurrence

Two opposing patterns of meta‐community organization are nestedness and negative species co‐occurrence. Both patterns can be quantified with metrics that are applied to presence‐absence matrices and tested with null model analysis. Previous meta‐analyses have given conflicting results, with the same set of matrices apparently showing high nestedness (Wright et al. 1998) and negative species co‐occurrence (Gotelli and McCabe 2002). We clarified the relationship between nestedness and co‐occurrence by creating random matrices, altering them systematically to increase or decrease the degree of nestedness or co‐occurrence, and then testing the resulting patterns with null models. Species co‐occurrence is related to the degree of nestedness, but the sign of the relationship depends on how the test matrices were created. Low‐fill matrices created by simple, uniform sampling generate negative correlations between nestedness and co‐occurrence: negative species co‐occurrence is associated with disordered matrices. However, high‐fill matrices created by passive sampling generate the opposite pattern: negative species co‐occurrence is associated with highly nested matrices. The patterns depend on which index of species co‐occurrence is used, and they are not symmetric: systematic changes in the co‐occurrence structure of a matrix are only weakly associated with changes in the pattern of nestedness. In all analyses, the fixed‐fixed null model that preserves matrix row and column totals has lower type I and type II error probabilities than an equiprobable null model that relaxes row and column totals. The latter model is part of the popular nestedness temperature calculator, which detects nestedness too frequently in random matrices (type I statistical error). When compared to a valid null model, a matrix with negative species co‐occurrence may be either highly nested or disordered, depending on the biological processes that determine row totals (number of species occurrences) and column totals (number of species per site).     

Range‐constrained co‐occurrence simulation reveals little niche partitioning among rock‐dwelling Montenegrina land snails (Gastropoda: Clausiliidae)

Aim

Taxon co‐occurrence analysis is commonly used in ecology, but it has not been applied to range‐wide distribution data of partly allopatric taxa because existing methods cannot differentiate between distribution‐related effects and taxon interactions. Our first aim was to develop a taxon co‐occurrence analysis method that is also capable of taking into account the effect of species ranges and can handle faunistic records from museum databases or biodiversity inventories. Our second aim was to test the independence of taxon co‐occurrences of rock‐dwelling gastropods at different taxonomic levels, with a special focus on the Clausiliidae subfamily Alopiinae, and in particular the genus Montenegrina.

Location

Balkan Peninsula in south‐eastern Europe (46N–36N, 13.5E–28E).

Methods

We introduced a taxon‐specific metric that characterizes the occurrence probability at a given location. This probability was calculated as a distance‐weighted mean of the taxon's presence and absence records at all sites. We applied corrections to account for the biases introduced by varying sampling intensity in our dataset. Then we used probabilistic null‐models to simulate taxon distributions under the null hypothesis of no taxon interactions and calculated pairwise and cumulated co‐occurrences. Independence of taxon occurrences was tested by comparing observed co‐occurrences to simulated values.

Results

We observed significantly fewer co‐occurrences among species and intra‐generic lineages of Montenegrina than expected under the assumption of no taxon interaction.

Main conclusions

Fewer than expected co‐occurrences among species and intra‐generic clades indicate that species divergence preceded niche partitioning. This suggests a primary role of non‐adaptive processes in the speciation of rock‐dwelling gastropods. The method can account for the effects of distributional constraints in range‐wide datasets, making it suitable for testing ecological, biogeographical, or evolutionary hypotheses where interactions of partly allopatric taxa are in question.     

A comprehensive framework for the study of species co‐occurrences,nestedness and turnover

Binary presence–absence matrices (rows = species, columns = sites) are often used to quantify patterns of species co‐occurrence, and to infer possible biotic interactions from these patterns. Previous classifications of co‐occurrence patterns as nested, segregated, or modular have led to contradictory results and conclusions. These analyses usually do not incorporate the functional traits of the species or the environmental characteristics of the sites, even though the outcomes of species interactions often depend on trait expression and site quality. Here we address this shortcoming by developing a method that incorporates realized functional and environmental niches, and relates them to species co‐occurrence patterns. These niches are defined from n‐dimensional ellipsoids, and calculated from the n eigenvectors and eigenvalues of the variance–covariance matrix of measured environmental or trait variables. Average niche overlap among species and the spatial distribution of niches define a triangle plot with vertices of species segregation (low niche overlap), nestedness (high niche overlap), and modular co‐occurrence (clusters of overlapping niches). Applying this framework to temperate understorey plant communities in southwest Poland, we found a consistent modular structure of species occurrences, a pattern not detected by conventional presence–absence analysis. These results suggest that, in our case study, habitat filtering is the most important process structuring understorey plant communities. Furthermore, they demonstrate how incorporating trait and environmental data into co‐occurrence analysis improves pattern detection and provides a stronger theoretical framework for understanding community structure.     

Pattern detection in null model analysis

Synthesis The identification of distinctive patterns in species x site presence‐absence matrices is important for understanding meta‐community organisation. We compared the performance of a suite of null models and metrics that have been proposed to measure patterns of segregation, aggregation, nestedness, coherence, and species turnover. We found that any matrix with segregated species pairs can be re‐ordered to highlight aggregated pairs, indicating that these seemingly opposite patterns are closely related. Recently proposed classification schemes failed to correctly classify realistic matrices that included multiple co‐occurrence structures. We propose using a combination of metrics and decomposing matrix‐wide patterns into those of individual pairs of species and sites to pinpoint sources of non‐randomness. Null model analysis has been a popular tool for detecting pattern in binary presence–absence matrices, and previous tests have identified algorithms and metrics that have good statistical properties. However, the behavior of different metrics is often correlated, making it difficult to distinguish different patterns. We compared the performance of a suite of null models and metrics that have been proposed to measure patterns of segregation, aggregation, nestedness, coherence, and species turnover. We found that any matrix with segregated species pairs can be re‐ordered to highlight aggregated pairs. As a consequence, the same null model can identify a single matrix as being simultaneously aggregated, segregated or nested. These results cast doubt on previous conclusions of matrix‐wide species segregation based on the C‐score and the fixed‐fixed algorithm. Similarly, we found that recently proposed classification schemes based on patterns of coherence, nestedness, and segregation and aggregation cannot be uniquely distinguished using proposed metrics and null model algorithms. It may be necessary to use a combination of different metrics and to decompose matrix‐wide patterns into those of individual pairs of species or pairs of sites to pinpoint the sources of non‐randomness.     

Discovering biogeographic and ecological clusters with a graph theoretic spin on factor analysis

Factor analysis (FA) has the advantage of highlighting each semi‐distinct cluster of samples in a data set with one axis at a time, as opposed to simply arranging samples across axes to represent gradients. However, in the case of presence–absence data it is confounded by absences when gradients are long. No statistical model can cope with this problem because the raw data simply do not present underlying information about the length of such gradients. Here I propose an easy way to tease out this information. It is a simple emendation of FA called stepping down, which involves giving an absence a negative value when the missing species nowhere co‐occurs with the species found in the relevant sample. Specifically, a binary co‐occurrence graph is created, and the magnitude of negative values is made a function of how far the graph must be traversed in order to link the missing species with each species that is present. Simulations show that standard FA yields inferior results to FA based on stepped‐down matrices in terms of mapping clusters into axes one‐by‐one. Standard FA is also uninformative when applied to a global bat inventory data set. Step‐down FA (SDFA) easily flags the main biogeographic groupings. Methods like correspondence analysis, non‐metric multidimensional scaling, and Bayesian latent variable modelling are not commensurate with SDFA because they do not seek to find a one‐to‐one mapping of axes and clusters. Stepping down seems promising as a means of illustrating clusters of samples, especially when there are subtle or complex discontinuities in gradients.     

Using biodiversity deconstruction to disentangle assembly and diversity dynamics of understorey plants along post‐fire succession in boreal forest

Aim The study aims to decipher the co‐occurrence of understorey plant assemblages and, accordingly, to identify a set of species groups (diversity deconstruction) to better understand the multiple causal processes underlying post‐fire succession and diversity patterns in boreal forest. Location North‐eastern Canadian boreal forest (49°07′–51°44′ N; 70°13′–65°15′ W). Methods Data on understorey plant communities and habitat factors were collected from 1097 plots. Species co‐occurrence was analysed using null model analysis. We derive species groups (i.e. biodiversity deconstruction) using the strength of pairwise species co‐occurrences after accounting for random expectation under a null model and cluster analyses. We examine the influence of a set of spatiotemporal environmental variables (overstorey composition, time‐since‐fire, spatial location and topography) on richness of species groups using Bayesian model averaging, and their relative influence through hierarchical partitioning of variance. Results Understorey plant assemblages were highly structured, with co‐occurrence‐based classification providing species groups that were coherently aggregated within, but variably segregated between, species groups. Group richness models indicate both common and distinct responses to factors affecting plant succession. For example, Group 2 (e.g. Rhododendron groenlandicum and Cladina rangiferina) showed concurrent contrasting responses to overstorey composition and was strongly segregated from Groups 3 (e.g. Clintonia borealis and Maianthenum canadense) and 4 (e.g. Epilobium angustifolium and Alnus rugosa). Groups 3 and 4 showed partial similarity, but they differed in their response to time‐since‐fire, drainage and latitude, which were more important for Group 1 (e.g. Ptilium crista‐castrensis and Empetrum nigrum). A single successional model based on total richness masked crucial group‐level relationships with factors that we examined, such as latitude. Main conclusions By demonstrating the co‐occurrence structure and linking to causal factors, the results from this study characterize both common and distinct responses of understorey plants to biophysical attributes of sites, and potential interspecific interactions, behind non‐random assemblage structure during post‐fire succession. A biodiversity deconstruction approach could offer a concise and explicit framework to gain a better understanding of the complex assembly of ecological communities during succession.     

Spatio-temporal co-occurrence of cougars (Felis concolor), wolves (Canis lupus) and their prey during winter: a comparison of two analytical methods

Aims To examine the spatio‐temporal co‐occurrence of cougars (Felis concolor), wolves (Canis lupus), and their prey during winter using monthly (November–March) species–environment relationship models. In addition, to contrast predictions across two methods: logistic regression and Geographic Information System (GIS) image correlation. Location The eastern front ranges of the Canadian Rocky Mountains (south‐central Alberta), approximately 100 km west of Calgary, including portions of Banff National Park and Kananaskis Country. Methods Snow‐tracking data were collected simultaneously for cougars, wolves, elk (Cervus elaphus), and deer (Odocoileus virginianus and O. hemionus) between November and March, 1997–2000. Track data were synthesized in a GIS. Logistic regression and Akaike's information criterion (AIC) were used to select optimal environmental relationship models for each species. We first examined co‐occurrence by iteratively using each species as a dependent variable (presence/absence) in a logistic regression analysis and using all other species track‐density estimates as independent variables. We built predictive surfaces in a GIS using the exponent form of the logistic regression models, and assessed model accuracy with a receiver operating characteristic curve. We then re‐examined co‐occurrence using pairwise correlations of species probability surfaces by month. The correlation results were compared with logistic regression results to illuminate mechanisms of co‐occurrence and to investigate predictive consistency across the two methods. Results Cougars showed a trend in distribution from higher elevation and less rugged terrain in December, to lower elevation and more rugged terrain in March. This trend differed from that for wolves, which showed a more stable affinity for low elevation and less rugged valley bottoms across all months. The logistic regression models indicated variable positive and negative associations of cougars with wolves by month, and changes in prey associations over time. Notably, there was a shift in co‐occurrence for both predators from elk to deer in March. We found high predictive accuracy for all probability surfaces, except for the month of January. Our image comparison showed that spatial co‐occurrence amongst all species increased over winter, except that wolves and cougars were negatively correlated in February. Combining the results of each approach we found that cougars and wolves converged spatially over winter at the landscape scale (i.e. the valley), while showing more discrete use of that space over time and by habitat attributes (e.g. forest cover, topographic complexity, and prey track density). Main conclusions In the Rocky Mountains, the spatial distributions of cougars and wolves converged into the valley floor as winter progressed. Cougars were distinct from wolves and prey in the intensity of this shift. We determined that a comparison of predictive surfaces alone fails to explain species co‐occurrence. The surfaces must be coupled with investigation of respective species–environment models to account for temporal changes in associations. We suggest that the two approaches represent different ecological scales: image comparison may be best for landscape‐ (valley) level analysis, while logistic regression is best for site‐level analysis. Ultimately, both approaches were critical to our analysis. Finally, the variability observed over time suggested that annual and seasonal models may obscure important ecological patterns and processes, especially for cougars.     

A new integrative framework for large‐scale assessments of biodiversity and community dynamics,using littoral gastropods and crabs of British Columbia,Canada

Improving our understanding of species responses to environmental changes is an important contribution ecologists can make to facilitate effective management decisions. Novel synthetic approaches to assessing biodiversity and ecosystem integrity are needed, ideally including all species living in a community and the dynamics defining their ecological relationships. Here, we present and apply an integrative approach that links high‐throughput, multicharacter taxonomy with community ecology. The overall purpose is to enable the coupling of biodiversity assessments with investigations into the nature of ecological interactions in a community‐level data set. We collected 1195 gastropods and crabs in British Columbia. First, the General mixed Yule‐coalescent (GMYC) and the Poisson Tree Processes (PTP) methods for proposing primary species‐hypotheses based on cox1 sequences were evaluated against an integrative taxonomic framework. We then used data on the geographic distribution of delineated species to test species co‐occurrence patterns for nonrandomness using community‐wide and pairwise approaches. Results showed that PTP generally outperformed GMYC and thus constitutes a more effective option for producing species‐hypotheses in community‐level data sets. Nonrandom species co‐occurrence patterns indicative of ecological relationships or habitat preferences were observed for grazer gastropods, whereas assemblages of carnivorous gastropods and crabs appeared influenced by random processes. Species‐pair associations were consistent with current ecological knowledge, thus suggesting that applying community assembly within a large taxonomical framework constitutes a valuable tool for assessing ecological interactions. Combining phylogenetic, morphological and co‐occurrence data enabled an integrated view of communities, providing both a conceptual and pragmatic framework for biodiversity assessments and investigations into community dynamics.     

Competitive interactions change the pattern of species co‐occurrences under neutral dispersal

Non‐random patterns of species segregation and aggregation within ecological communities are often interpreted as evidence for interspecific interactions. However, it is unclear whether theoretical models can predict such patterns and how environmental factors may modify the effects of species interactions on species co‐occurrence. Here we extend a spatially explicit neutral model by including competitive effects on birth and death probabilities to assess whether competition alone is able to produce non‐random patterns of species co‐occurrence. We show that transitive and intransitive competitive hierarchies alone (in the absence of environmental heterogeneity) are indeed able to generate non‐random patterns with commonly used metrics and null models. Moreover, even weak levels of intransitive competition can increase local species richness. However, there is no simple rule or consistent directional change towards aggregation or segregation caused by competitive interactions. Instead, the spatial pattern depends on both the type of species interaction and the strength of dispersal. We conclude that co‐occurrence analysis alone may not able to identify the underlying processes that generate the patterns.     

Liana co‐occurrence patterns in a temperate rainforest

Questions: Are liana–host interactions structured at the community level? Do liana–host interactions differ between species, growth form guilds or habitats? Location: Otari‐Wilton's Bush, on the southern tip of North Island, New Zealand. The forest contains 75 ha of mature and regenerating conifer–broadleaf forest. Methods: Nine liana species were quantified among 217 trees to test for negative co‐occurrence patterns. We also conducted additional analyses within and among compartments embedded in the community‐level matrix. Liana and host abundance distributions were assessed across two contrasting habitats. Results: Community‐level analyses revealed negative co‐occurrence patterns. Positive, neutral and negative co‐occurrence patterns were found among compartments within the community‐level matrix. Host species compartments were consistent with randomized expectations, while positive co‐occurrence patterns were found within the host species matrix. Negative co‐occurrence patterns were found inconsistently among lianas that share the same region of host space, and those that do not. Conclusions: Overall, results indicate the liana community is structured non‐randomly. Liana–host interactions appear to follow an opportunistic growth strategy and interactions are due mostly to habitat partitioning.     

Fine-scale structure and cross-taxon congruence of bird and beetle assemblages in an old-growth boreal forest mosaic

Aim Nestedness occurs when species present in depauperate sites are subsets of those found in species‐rich sites. The degree of congruence of site nestedness among different assemblages can inform commonalities of mechanisms structuring the assemblages. Well‐nested assemblages may still contain idiosyncratic species and sites that notably depart from the typical assemblage pattern. Idiosyncrasy can arise from multiple processes, including interspecific interactions and habitat preferences, which entail different consequences for species co‐occurrences. We investigate the influence of fine‐scale habitat variation on nestedness and idiosyncrasy patterns of beetle and bird assemblages. We examine community‐level and pairwise species co‐occurrence patterns, and highlight the potential influence of interspecific interactions for assemblage structure. Location Côte‐Nord region of Québec, Canada. Methods We sampled occurrences of ground‐dwelling beetles, flying beetles and birds at sites within old‐growth boreal forest. We examined the nestedness and idiosyncrasy of sites and sought relationships to habitat attributes. We analysed non‐random species co‐occurrence patterns at pairwise and community levels, using null model analysis and five ‘association’ indices. Results All three assemblages were significantly nested. There was limited congruence only between birds and flying beetles whose nestedness was related to canopy openness. For ground‐dwelling beetles, nestedness was related to high stand heterogeneity and sapling density, whereas site idiosyncrasy was inversely related to structural heterogeneity. For birds, site idiosyncrasy increased with canopy cover, and most idiosyncratic species were closed‐canopy specialists. In all assemblages, species idiosyncrasy was positively correlated with the frequency of negative pairwise associations. Species co‐occurrence patterns were non‐random, and for flying beetles and birds positive species pairwise associations dominated. Community‐level co‐occurrence summaries may not, however, always reflect these patterns. Main conclusions Nestedness patterns of different assemblages may not correlate, even when sampled at common locations, because of different responses to local habitat attributes. We found idiosyncrasy patterns indicating opposing habitat preferences, consistent with antagonistic interactions among species within assemblages. Analysis of such patterns can thus suggest the mechanisms generating assemblage structures, with implications for biodiversity conservation.     

Testing the link between species interactions and species co‐occurrence in a trophic network

Species interactions are dynamic processes that vary across environmental and ecological contexts, and operate across scale boundaries, making them difficult to quantify. Nevertheless, ecologists are increasingly interested in inferring species interactions from observational data using statistical analyses of their spatial co‐occurrence patterns. Trophic interactions present a particular challenge, as predators and prey may frequently or rarely co‐occur, depending on the spatial or temporal scale of observation. In this study, we investigate the accuracy of inferred interactions among species that both compete and trophically interact. We utilized a long‐term dataset of pond‐breeding amphibian co‐occurrences from Mt Rainier National Park (Washington, USA) and compiled a new dataset of their empirical interactions from the literature. We compared the accuracy of four statistical methods in inferring these known species interactions from spatial associations. We then used the best performing statistical method, the Markov network, to further investigate the sensitivity of interaction inference to spatial scale‐dependence and the presence of predators. We show that co‐occurrence methods are generally inaccurate when estimating trophic interactions. Further the strength and sign of inferred interactions were dependent upon the spatial scale of observation and predator presence influenced the detectability of competitive interactions among prey species. However, co‐occurrence analysis revealed new patterns of spatial association among pairs of species with known interactions. Overall, our study highlights a limiting frontier in co‐occurrence theory and the disconnect between widely implemented methodologies and their ability to accurately infer interactions in trophically‐structured communities.     

Species co‐occurrence networks show reptile community reorganization under agricultural transformation

Agricultural transformation represents one of the greatest threats to biodiversity, causing degradation and loss of habitat, leading to changes in the richness and composition of communities. These changes in richness and composition may, in turn, lead to altered species co‐occurrence, but our knowledge of this remains limited. We used a novel co‐occurrence network approach to examine the impact of agricultural transformation on reptile community structure within two large (> 172 000 km²; 224 sites) agricultural regions in southeastern Australia. We contrasted assemblages from sites surrounded by intact and modified landscapes and tested four key hypotheses that agricultural transformation leads to (H1) declines in species richness, (H2) altered assemblages, (H3) declines in overall co‐occurrence, and (H4) complex restructuring of pairwise associations. We found that modified landscapes differed in composition but not richness compared with intact sites. Modified landscapes were also characterized by differences in co‐occurrence network structure; with species sharing fewer sites with each other (reduced co‐occurrence connectance), fewer highly‐connected species (truncation of the frequency distribution of co‐occurrence degree) and increased modularity of co‐occurrence networks. Critically, overall loss of co‐occurrence was underpinned by complex changes to the number and distribution of pair‐wise co‐occurrence links, with 41–44% of species also gaining associations with other species. Change in co‐occurrence was not correlated with changes in occupancy, nor by functional trait membership, allowing a novel classification of species susceptibility to agricultural transformation. Our study reveals the value of using co‐occurrence analysis to uncover impacts of agricultural transformation that may be masked in conventional studies of species richness and community composition.     

Fire disturbance disrupts co‐occurrence patterns of terrestrial vertebrates in Mediterranean woodlands

Aim This paper uses null model analysis to explore the pattern of species co‐occurrence of terrestrial vertebrate fauna in fire‐prone, mixed evergreen oak woodlands. Location The Erico–Quercion ilicis of the Mediterranean belt (50–800 m a.s.l.) in the Madonie mountain range, a regional park in northern Sicily (37°50′ N, 14°05′ E), Italy. Methods The stratified sampling of vertebrates in a secondary succession of recent burned areas (BA, 1–2 years old), intermediate burned areas (INT, 4–10 years old) and ancient burned areas (CNB, > 50 years old), plus forest fragments left within burned areas (FF, 1–2 years old) permitted the comparison of patterns of species co‐occurrence using a set of separate presence/absence matrices. First, the breeding avifauna derived from standardized point counts was analysed using Stone & Roberts’C‐score, and by a null model algorithm (fixed/equiprobable). Secondly, the analysis was repeated using all vertebrate species recorded in the succession. Results Sixty‐five species were recorded in the 2‐year study period in the four sample treatments. Birds were found to make up the largest component (63%) of the recorded assemblage. The BA treatment had the lowest species richness, followed in order by the small, medium and large FFs, and then by the CNBs. For both analyses (birds and total vertebrates), the C‐scores were quite small and not significantly different from those that could be expected by chance in the BA and INT burned areas; this indicates a random co‐occurrence among vertebrates of those assemblages. Contrariwise, for both analyses in the CNBs, the C‐scores were large and significantly different from the simulated indices, thereby indicating a non‐random co‐occurrence pattern (segregation) of vertebrates in the undisturbed woodlands. In addition, C‐score values for the surviving FFs show a significant aggregation of species. Main conclusions The null model analyses highlighted a new aspect of fire disturbance in Mediterranean woodland ecosystems: the disruption in patterns of co‐occurrence in the terrestrial vertebrate community. Wildfire alters community organization, inducing, for at least 10 years, a random aggregate of species. Communities re‐assemble themselves, showing the occurrence of species segregation at least 50 years after fire.