Predicting and Prioritising Community Assembly: Learning Outcomes via Experiments

Community assembly provides the foundation for applications in biodiversity conservation, climate change, invasion, restoration and synthetic ecology. However, predicting and prioritising assembly outcomes remains difficult. We address this challenge via a mechanism-free approach useful when little data or knowledge exist (LOVE; Learning Outcomes Via Experiments). We carry out assembly experiments ('actions', here, random combinations of species additions) potentially in multiple environments, wait, and measure abundance outcomes. We then train a model to predict outcomes of novel actions or prioritise actions that would yield the most desirable outcomes. Across 10 single- and multi-environment datasets, when trained on 89 randomly selected actions, LOVE predicts outcomes with 0.5%-3.4% mean error, and prioritises actions for maximising richness, maximising abundance, or removing unwanted species, with 94%-99% mean true positive rate and 10%-84% mean true negative rate across tasks. LOVE complements existing mechanism-first approaches for community ecology and may help address numerous applied challenges.

A taxonomy of multiple stable states in complex ecological communities

Natural systems are built from multiple interconnected units, making their dynamics, functioning and fragility notoriously hard to predict. A fragility scenario of particular relevance concerns so-called regime shifts: abrupt transitions from healthy to degraded ecosystem states. An explanation for these shifts is that they arise as transitions between alternative stable states, a process that is well-understood in few-species models. However, how multistability upscales with system complexity remains a debated question. Here, we identify that four different multistability regimes generically emerge in models of species-rich communities and other archetypical complex biological systems assuming random interactions. Across the studied models, each regime consistently emerges under a specific interaction scheme and leaves a distinct set of fingerprints in terms of the number of observed states, their species richness and their response to perturbations. Our results help clarify the conditions and types of multistability that can be expected to occur in complex ecological communities.

Power laws in species' biotic interaction networks can be inferred from co-occurrence data

Inferring biotic interactions from species co-occurrence patterns has long intrigued ecologists. Yet recent research revealed that co-occurrences may not reliably represent pairwise biotic interactions. We propose that examining network-level co-occurrence patterns can provide valuable insights into community structure and assembly. Analysing ten bipartite networks of empirically sampled biotic interactions and associated species spatial distribution, we find that approximately 20% of co-occurrences correspond to actual interactions. Moreover, the degree distribution shifts from exponential in co-occurrence networks to power laws in networks of biotic interactions. This shift results from a strong interplay between species' biotic (their interacting partners) and abiotic (their environmental requirements) niches, and is accurately predicted by considering co-occurrence frequencies. Our work offers a mechanistic understanding of the assembly of ecological communities and suggests simple ways to infer fundamental biotic interaction network characteristics from co-occurrence data.

Comment on "Interspecific competition limits bird species' ranges in tropical mountains"

Freeman et al. (Reports, 22 July 2022, p. 416) argue that interspecific competition rather than climate is the leading driver of bird species' elevational ranges. A reanalysis of their data shows no support for the competition hypothesis, but a strong effect of climate seasonality on species ranges. Their results are artifacts arising from a suboptimal model that misses important variables.

Co-occurrence pattern of congeneric tree species provides conflicting evidence for competition relatedness hypothesis

In plants, negative reproductive interaction among closely related species (i.e., reproductive interference) is known to hamper the coexistence of congeneric species while facilitation can increase species persistence. Since reproductive interference in plants may occur through interspecific pollination, the effective range of reproductive interference may reflects the spatial range of interspecific pollination. Therefore, we hypothesized that the coexistence of congeners on a small spatial scale would be less likely to occur by chance but that such coexistence would be likely to occur on a scale larger than interspecific pollination frequently occur. In the present study, we tested this hypothesis using spatially explicit woody plant survey data. Contrary to our prediction, congeneric tree species often coexisted at the finest spatial scale and significant exclusive distribution was not detected. Our results suggest that cooccurrence of congeneric tree species is not structured by reproductive interference, and they indicate the need for further research to explore the factors that mitigate the effects of reproductive interference.

Does trait-based joint species distribution modelling reveal the signature of competition in stream macroinvertebrate communities?

The occupancy and abundance of species are jointly driven by local factors, such as environmental characteristics and biotic interactions, and regional-scale factors, such as dispersal and climate. Recently, it has been shown that biotic interactions shape species occupancies and abundances beyond local extents. However, for small ectothermic animals, particularly for those occurring in freshwater environments, the importance of biotic interactions remains understudied. Species-to-species associations from joint species distribution models (i.e. species associations while controlling for environmental characteristics) are increasingly used to draw hypotheses of which species possibly show biotic interactions. We studied whether species-to-species associations from joint species distribution models show signs of competition using a hypothesis testing framework in stream macroinvertebrate communities at regional extent. We sampled aquatic macroinvertebrates from 105 stream sites in western Finland encompassing a latitudinal gradient of c. 500 kilometres. We hypothesized that if competition drives these associations (H1) functionally, similar species are mostly negatively associated, whereas functionally dissimilar species show random associations. We further hypothesized that the relationship between functional dissimilarity and the strength of association is more pronounced (H2) for abundances rather than occupancies, (H3) at small grain (i.e. stream site) rather than at large grain (i.e. river basin), and (H4) among species having weak dispersal ability than among species with high dispersal ability. Stream macroinvertebrates showed both negative and positive species-to-species associations while controlling for habitat characteristics. However, the negative associations were mostly at large grain (river basin) rather than at small grain (stream site), in occupancy rather than abundance, and not related to species functional dissimilarity or to their dispersal ability. Thus, all our hypotheses considering possible competition (H1-H4) were rejected. Competition does not appear to be a major driving force of stream macroinvertebrate communities at the spatial grain sizes considered. The observed positive associations in occupancy at small grain (stream site) may be attributed to species' similar microhabitat preferences, whereas at large grain (river basin), they may stem from metacommunity dynamics. Our results highlight that species traits were necessary to interpret whether or not species-to-species associations from joint species distribution models resulted from biotic interactions.

Predicting mammalian hosts in which novel coronaviruses can be generated

Novel pathogenic coronaviruses – such as SARS-CoV and probably SARS-CoV-2 – arise by homologous recombination between co-infecting viruses in a single cell. Identifying possible sources of novel coronaviruses therefore requires identifying hosts of multiple coronaviruses; however, most coronavirus-host interactions remain unknown. Here, by deploying a meta-ensemble of similarity learners from three complementary perspectives (viral, mammalian and network), we predict which mammals are hosts of multiple coronaviruses. We predict that there are 11.5-fold more coronavirus-host associations, over 30-fold more potential SARS-CoV-2 recombination hosts, and over 40-fold more host species with four or more different subgenera of coronaviruses than have been observed to date at >0.5 mean probability cut-off (2.4-, 4.25- and 9-fold, respectively, at >0.9821). Our results demonstrate the large underappreciation of the potential scale of novel coronavirus generation in wild and domesticated animals. We identify high-risk species for coronavirus surveillance.

Homologous recombination between co-infecting coronaviruses can produce novel pathogens. Here, Wardeh et al. develop a machine learning approach to predict associations between mammals and multiple coronaviruses and hence estimate the potential for generation of novel coronaviruses by recombination.

Species co-occurrence networks of ground beetles in managed grasslands

Grassland biodiversity, including traditional rural biotopes maintained by traditional agricultural practices, has become threatened worldwide. Road verges have been suggested to be complementary or compensatory habitats for species inhabiting grasslands. Species co-occurrence patterns linked with species traits can be used to separate between the different mechanisms (stochasticity, environmental filtering, biotic interactions) behind community structure. Here, we study species co-occurrence networks and underlying mechanisms of ground beetle species (Carabidae) in three different managed grassland types (meadows, pastures, road verges, n = 12 in each type) in Central Finland. We aimed to find out whether road verges can be considered as compensatory to traditional rural biotopes (meadows and pastures). We found that stochasticity explained over 90% of the pairwise co-occurrences, and the non-random co-occurrences were best explained by environmental filtering, regardless of the grassland type. However, the identities and traits of the species showing non-random co-occurrences differed among the habitat types. Thus, environmental factors behind environmental filtering differ among the habitat types and are related to the site-specific characteristics and variation therein. This poses challenges to habitat management since the species’ response to management action may depend on the site-specific characteristics. Although road verges are not fully compensatory to meadows and pastures, the high similarity of species richness and the high level of shared species suggest that for carabids road verges may be corridors connecting the sparse network of the remaining traditional rural biotopes.

Null model analyses of temporal patterns of bird assemblages and their foraging guilds revealed the predominance of positive and random associations

Patterns of species associations have been commonly used to infer interactions among species. If species positively co-occur, they may form predominantly neutral assemblages, and such patterns suggest a relatively weak role for compensatory dynamics. The main objective of this study was to test this prediction on temporal samples of bird assemblages (n = 19, 10-57 years) by the presence/absence and quantitative null models on assemblage and guild levels. These null model outcomes were further analyzed to evaluate the effects of various data set characteristics on the outcomes of the null models. The analysis of two binary null models in combination with three association indices revealed 20% with significant aggregations, 61% with random associations, and only 19% with significant segregations (n = 95 simulations). The results of the quantitative null model simulations detected more none-random associations: 61% aggregations, 6% random associations, and 33% segregations (n = 114 simulations). Similarly, quantitative analyses on guild levels showed 58% aggregations, 20% segregations, and 22% random associations (n = 450 simulations). Bayesian GLMs detected that the outcomes of the binary and quantitative null models applied to the assemblage analyses were significantly related to census plot size, whereas the outcomes of the quantitative analyses were also related to the mean population densities of species in the data matrices. In guild-level analyses, only 9% of the GLMs showed a significant influence of matrix properties (plot size, matrix size, species richness, and mean species population densities) on the null model outcomes. The results did not show the prevalence of negative associations that would have supported compensatory dynamics. Instead, we assume that a similar response of the majority of species to climate-driven and stochastic factors may be responsible for the revealed predominance of positive associations.

Good neighbors aplenty: fungal endophytes rarely exhibit competitive exclusion patterns across a span of woody habitats

Environmental forces and biotic interactions, both positive and negative, structure ecological communities, but their relative roles remain obscure despite strong theory. For instance, ecologically similar species, based on the principle of limiting similarity, are expected to be most competitive and show negative interactions. Specious communities that assemble along broad environmental gradients afford the most power to test theory, but the communities often are difficult to quantify. Microbes, specifically fungal endophytes of wood, are especially suited for testing community assembly theory because they are relatively easy to sample across a comprehensive range of environmental space with clear axes of variation. Moreover, endophytes mediate key forest carbon cycle processes, and although saprophytic fungi from dead wood typically compete, endophytic fungi in living wood may enhance success through cooperative symbioses. To classify interactions within endophyte communities, we analyzed fungal DNA barcode variation across 22 woody plant species growing in woodlands near Richmond, New South Wales, Australia. We estimated the response of endophytes to the measured wood environment (i.e., 11 anatomical and chemical wood traits) and each other using latent-variable models and identified recurrent communities across wood environments using model-based classification. We used this information to evaluate whether (1) co-occurrence patterns are consistent with strong competitive exclusion, and (2) a priori classifications by trophic mode and phylum distinguish taxa that are more likely to have positive vs. negative associations under the principle of limiting similarity. Fungal endophytes were diverse (mean = 140 taxa/sample), with differences in community composition structured by wood traits. Variation in wood water content and carbon concentration were associated with especially large community shifts. Surprisingly, after accounting for wood traits, fungal species were still more than three times more likely to have positive than negative co-occurrence patterns. That is, patterns consistent with strong competitive exclusion were rare, and positive interactions among fungal endophytes were more common than expected. Confirming the frequency of positive vs. negative interactions among fungal taxa requires experimental tests, and our findings establish clear paths for further study. Evidence to date intriguingly suggests that, across a wide range of wood traits, cooperation may outweigh combat for these fungi.

Co-occurrence patterns of rodents at multiple spatial scales: competitive release of generalists following habitat loss?

Theory predicts that habitat generalist species are excluded by specialist species in optimal habitat for specialists, and empirical data commonly show a shift from specialist- to generalist-dominated communities following disturbance. We investigated co-occurrence patterns of habitat generalist and specialist terrestrial rodents at two spatial scales in the Atlantic Forest, aiming at evaluating the following hypotheses: 1) within-patch spatial niche partitioning promotes coexistence of generalists and specialists, leading to checkerboard presence-absence patterns at small (within-patch) rather than large (among-patch) scales; and 2) the decrease in abundance of specialists due to habitat loss promotes a competitive release of generalists, leading to negative covariance in abundance between generalists and specialists among patches. Drawing on a large data set including 363 sites within three patches in continuous forest, and 45 patches within three landscapes, we used C-scores based on presence-absence and abundance data to evaluate spatial segregation. We found consistent segregation between specialists and generalists at the within-patch rather than among-patch scale, but no consistent negative covariance in abundance between generalists and specialists among patches (as covarying species pairs varied across landscapes). Our findings suggest that spatial patterns caused by competition are scale-dependent, and coexistence of generalists and specialists is promoted by within-patch spatial niche partitioning. However, the influence of competitive release on the proliferation of generalists may be outweighed by other factors in fragmented landscapes.

A teoria ecológica prevê que espécies generalistas de habitat são excluídas por espécies especialistas em hábitats ótimos para as especialistas, e dados empíricos comumente mostram uma mudança de dominância das comunidades - de especialistas para generalistas - após distúrbios. Nós investigamos os padrões de coocorrência de roedores terrestres generalistas e especialistas de habitat em duas escalas espaciais na Mata Atlântica, para testar as seguintes hipóteses: 1) a partição espacial do nicho dento de fragmentos promove a coexistência de generalistas e especialistas, levando a padrões de presença-ausência “tabuleiro de damas” em escalas pequenas (dentro de fragmento) mas não em escalas grandes (entre fragmentos); 2) a diminuição da abundância de especialistas devido à perda de habitat promove uma liberação competitiva de generalistas, levando a covariância negativa da abundância de generalistas e especialistas entre fragmentos. A partir de um grande banco de dados - 363 sítios dentro de três fragmentos de floresta contínua, e 45 fragmentos dentro de três paisagens, usamos C-scores baseados em dados de presença/ausência e abundância para avaliar a segregação espacial. Encontramos segregação consistente entre especialistas e generalistas na escala menor (dentro de fragmentos) e não na maior (entre fragmentos), mas não encontramos covariância negativa na abundância de generalistas e especialistas entre fragmentos (dado que os pares de espécies que covariaram mudou entre as paisagens). Nossos resultados sugerem que padrões espaciais causados por competição são dependentes de escala, e que a coexistência de generalistas e especialistas é promovida pela partição espacial de nicho dentro dos fragmentos. No entanto, a influência da liberação competitiva na proliferação de generalistas pode ser superada por outros fatores em paisagens fragmentadas.

Quenched disorder in the contact process on bipartite sublattices

We study the effects of distinct types of quenched disorder in the contact process with a competitive dynamics on bipartite sublattices. In the model, the particle creation depends on its first and second neighbors and the extinction increases according to the local density. The clean (without disorder) model exhibits three phases: inactive (absorbing), active symmetric, and active asymmetric, where the latter exhibits distinct sublattice densities. These phases are separated by continuous transitions; the phase diagram is reentrant. By performing mean-field analysis and Monte Carlo simulations we show that symmetric disorder destroys the sublattice ordering and therefore the active asymmetric phase is not present. On the other hand, for asymmetric disorder (each sublattice presenting a distinct dilution rate) the phase transition occurs between the absorbing and the active asymmetric phases. The universality class of this transition is governed by the less-disordered sublattice. Finally, our results suggest that random-field disorder destroys the phase transition if it breaks the symmetry between two active states.

Niche dissociated assembly drives insular lizard community organization

Interspecific competition for resources leading to niche partitioning is considered as one of the major drivers of community assembly. Competitive niche partitioning is diagnosed from species co-occurrence, species abundance distributions (SADs), and body size distributions of species. For several decades, studies have explored these patterns for the relative significance of interspecific competition in shaping communities. We explored these patterns in a finite assemblage of insectivorous lizards in the Andaman & Nicobar Islands, both at the level of archipelago and individual islands. Negative geographic co-occurrences occurred only between species pairs in islands separated by deep ocean channels. Ecologically similar species did not show positive co-occurrence in guild co-occurrence analyses, indicating that the negative geographical co-occurrences between species in islands were due to historical allopatry. Species abundance distribution was best explained by a Pareto distribution in both metacommunity and local communities. There was no predictable spacing of body sizes among co-existing species in local communities. The empirical data on insular lizard community on species co-occurrence, SADs, and body size ratios does not lend support to assortment of species in islands caused by niche subdivision. Such niche-dissociated assembly of species in islands might be an important factor in formation of biological communities, regardless of geographic scale.

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 intrageneric 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.

Towards an eco-phylogenetic framework for infectious disease ecology

Identifying patterns and drivers of infectious disease dynamics across multiple scales is a fundamental challenge for modern science. There is growing awareness that it is necessary to incorporate multi-host and/or multi-parasite interactions to understand and predict current and future disease threats better, and new tools are needed to help address this task. Eco-phylogenetics (phylogenetic community ecology) provides one avenue for exploring multi-host multi-parasite systems, yet the incorporation of eco-phylogenetic concepts and methods into studies of host pathogen dynamics has lagged behind. Eco-phylogenetics is a transformative approach that uses evolutionary history to infer present-day dynamics. Here, we present an eco-phylogenetic framework to reveal insights into parasite communities and infectious disease dynamics across spatial and temporal scales. We illustrate how eco-phylogenetic methods can help untangle the mechanisms of host-parasite dynamics from individual (e.g. co-infection) to landscape scales (e.g. parasite/host community structure). An improved ecological understanding of multi-host and multi-pathogen dynamics across scales will increase our ability to predict disease threats.

Predicting cryptic links in host-parasite networks

Networks are a way to represent interactions among one (e.g., social networks) or more (e.g., plant-pollinator networks) classes of nodes. The ability to predict likely, but unobserved, interactions has generated a great deal of interest, and is sometimes referred to as the link prediction problem. However, most studies of link prediction have focused on social networks, and have assumed a completely censused network. In biological networks, it is unlikely that all interactions are censused, and ignoring incomplete detection of interactions may lead to biased or incorrect conclusions. Previous attempts to predict network interactions have relied on known properties of network structure, making the approach sensitive to observation errors. This is an obvious shortcoming, as networks are dynamic, and sometimes not well sampled, leading to incomplete detection of links. Here, we develop an algorithm to predict missing links based on conditional probability estimation and associated, node-level features. We validate this algorithm on simulated data, and then apply it to a desert small mammal host-parasite network. Our approach achieves high accuracy on simulated and observed data, providing a simple method to accurately predict missing links in networks without relying on prior knowledge about network structure.

Community assembly in Nothobranchius annual fishes: Nested patterns, environmental niche and biogeographic history

The assembly of local communities from regional species pools is shaped by historical aspects of distribution, environmental conditions, and biotic interactions. We studied local community assembly patterns in African annual killifishes of the genus Nothobranchius (Cyprinodontiformes), investigating data from 168 communities across the entire range of regionally co-existing species. Nothobranchius are small fishes associated with annually desiccating pools. We detected a nested pattern of local communities in one region (Southern Mozambique, with Nothobranchius furzeri as the core and dominant species), but no nestedness was found in the second region (Central Mozambique, with Nothobranchius orthonotus being the dominant species). A checkerboard pattern of local Nothobranchius community assembly was demonstrated in both regions. Multivariate environmental niche modeling revealed moderate differences in environmental niche occupancy between three monophyletic clades that largely co-occurred geographically and greater differences between strictly allopatric species within the clades. Most variation among species was observed along an altitudinal gradient; N. furzeri and Nothobranchius kadleci were absent from coastal plains, Nothobranchius pienaari, Nothobranchius rachovii, and Nothobranchius krysanovi were associated with lower altitude and N. orthonotus was intermediate and geographically most widespread species. We discuss implications for ecological and evolutionary research in this taxon.

Species-free species distribution models describe macroecological properties of protected area networks

Among the greatest challenges facing the conservation of plants and animal species in protected areas are threats from a rapidly changing climate. An altered climate creates both challenges and opportunities for improving the management of protected areas in networks. Increasingly, quantitative tools like species distribution modeling are used to assess the performance of protected areas and predict potential responses to changing climates for groups of species, within a predictive framework. At larger geographic domains and scales, protected area network units have spatial geoclimatic properties that can be described in the gap analysis typically used to measure or aggregate the geographic distributions of species (stacked species distribution models, or S-SDM). We extend the use of species distribution modeling techniques in order to model the climate envelope (or “footprint”) of individual protected areas within a network of protected areas distributed across the 48 conterminous United States and managed by the US National Park System. In our approach we treat each protected area as the geographic range of a hypothetical endemic species, then use MaxEnt and 5 uncorrelated BioClim variables to model the geographic distribution of the climatic envelope associated with each protected area unit (modeling the geographic area of park units as the range of a species). We describe the individual and aggregated climate envelopes predicted by a large network of 163 protected areas and briefly illustrate how macroecological measures of geodiversity can be derived from our analysis of the landscape ecological context of protected areas. To estimate trajectories of change in the temporal distribution of climatic features within a protected area network, we projected the climate envelopes of protected areas in current conditions onto a dataset of predicted future climatic conditions. Our results suggest that the climate envelopes of some parks may be locally unique or have narrow geographic distributions, and are thus prone to future shifts away from the climatic conditions in these parks in current climates. In other cases, some parks are broadly similar to large geographic regions surrounding the park or have climatic envelopes that may persist into near-term climate change. Larger parks predict larger climatic envelopes, in current conditions, but on average the predicted area of climate envelopes are smaller in our single future conditions scenario. Individual units in a protected area network may vary in the potential for climate adaptation, and adaptive management strategies for the network should account for the landscape contexts of the geodiversity or climate diversity within individual units. Conservation strategies, including maintaining connectivity, assessing the feasibility of assisted migration and other landscape restoration or enhancements can be optimized using analysis methods to assess the spatial properties of protected area networks in biogeographic and macroecological contexts.

Complex relationships between species niches and environmental heterogeneity affect species co-occurrence patterns in modelled and real communities

Species co-occurrence analysis is commonly used to assess how interspecific interactions dictate community assembly. Non-random co-occurrences, however, may also emerge from niche differences as well as environmental heterogeneity. The relationships between species co-occurrence patterns, environmental heterogeneity and species niches are not fully understood, due to complex interactions among them. To analyse the relationships among these patterns and processes, I developed synthetic community models and analysed a large dataset of tree species across the conterminous United States. Niche overlap and environmental heterogeneity had significant and contrasting effects on species co-occurrence patterns, in both modelled and real communities. Niche breadth, in turn, affected the effect sizes of both variables on species co-occurrence patterns. The effect of niche breadth on the relationship between co-occurrence and niche overlap was markedly consistent between modelled and real communities, while its effect on the relationship between co-occurrence and environmental heterogeneity was mostly consistent between real and modelled data. The results of this analysis highlight the complex and interactive effects of species niche overlap, niche breadth and environmental heterogeneity on species co-occurrence patterns. Therefore, inferring ecological processes from co-occurrence patterns without accounting for these fundamental characteristics of species and environments may lead to biased conclusions.

Holocene shifts in the assembly of plant and animal communities implicate human impacts

Understanding how ecological communities are organized and how they change through time is critical to predicting the effects of climate change. Recent work documenting the co-occurrence structure of modern communities found that most significant species pairs co-occur less frequently than would be expected by chance. However, little is known about how co-occurrence structure changes through time. Here we evaluate changes in plant and animal community organization over geological time by quantifying the co-occurrence structure of 359,896 unique taxon pairs in 80 assemblages spanning the past 300 million years. Co-occurrences of most taxon pairs were statistically random, but a significant fraction were spatially aggregated or segregated. Aggregated pairs dominated from the Carboniferous period (307 million years ago) to the early Holocene epoch (11,700 years before present), when there was a pronounced shift to more segregated pairs, a trend that continues in modern assemblages. The shift began during the Holocene and coincided with increasing human population size and the spread of agriculture in North America. Before the shift, an average of 64% of significant pairs were aggregated; after the shift, the average dropped to 37%. The organization of modern and late Holocene plant and animal assemblages differs fundamentally from that of assemblages over the past 300 million years that predate the large-scale impacts of humans. Our results suggest that the rules governing the assembly of communities have recently been changed by human activity.

A fast and unbiased procedure to randomize ecological binary matrices with fixed row and column totals

A well-known problem in numerical ecology is how to recombine presence-absence matrices without altering row and column totals. A few solutions have been proposed, but all of them present some issues in terms of statistical robustness (that is, their capability to generate different matrix configurations with the same probability) and their performance (that is, the computational effort that they require to generate a null matrix). Here we introduce the 'Curveball algorithm', a new procedure that differs from existing methods in that it focuses rather on matrix information content than on matrix structure. We demonstrate that the algorithm can sample uniformly the set of all possible matrix configurations requiring a computational effort orders of magnitude lower than that required by available methods, making it possible to easily randomize matrices larger than 10(8) cells.