The effect of environmental stochasticity on species richness in neutral communities

Environmental stochasticity is known to be a destabilizing factor, increasing abundance fluctuations and extinction rates of populations. However, the stability of a community may benefit from the differential response of species to environmental variations due to the storage effect. This paper provides a systematic and comprehensive discussion of these two contradicting tendencies, using the metacommunity version of the recently proposed time-average neutral model of biodiversity which incorporates environmental stochasticity and demographic noise and allows for extinction and speciation. We show that the incorporation of demographic noise into the model is essential to its applicability, yielding realistic behavior of the system when fitness variations are relatively weak. The dependence of species richness on the strength of environmental stochasticity changes sign when the correlation time of the environmental variations increases. This transition marks the point at which the storage effect no longer succeeds in stabilizing the community

Downscaling species occupancy from coarse spatial scales

The measurement and prediction of species' populations at different spatial scales is crucial to spatial ecology as well as conservation biology. An efficient yet challenging goal to achieve such population estimates consists of recording empirical species' presence and absence at a specific regional scale and then trying to predict occupancies at finer scales. So far the majority of the methods have been based on particular species' distributional features deemed to be crucial for downscaling occupancy. However, only a minority of them have dealt explicitly with specific spatial features. Here we employ a wide class of spatial point processes, the shot noise Cox processes (SNCP), to model species occupancies at different spatial scales and show that species' spatial aggregation is crucial for predicting population estimates at fine scales starting from coarser ones. These models are formulated in continuous space and locate points regardless of the arbitrary resolution that one employs to study the spatial pattern. We compare the performances of nine models, calibrated at regional scales and demonstrate that a very simple class of SNCP, the Thomas process, is able to outperform other published models in predicting occupancies down to areas four orders of magnitude smaller than the ones employed for the parameterization. We conclude by explaining the ability of the approach to infer spatially explicit information from spatially implicit measures, the potential of the framework to combine niche and spatial models, and the possibility of reversing the method to allow upscaling

Using exclusion rate to unify niche and neutral perspectives on coexistence

The competitive exclusion principle is one of the most influential concepts in ecology. The classical formulation suggests a correlation between competitor species similarity and competition severity, leading to rapid competitive exclusion where species are very similar; yet neutral models show that identical species can persist in competition for long periods. Here, we resolve the conflict by examining two components of similarity – niche overlap and competitive similarity – and modeling the effects of each on exclusion rate (defined as the inverse of time to exclusion). Studying exclusion rate, rather than the traditional focus on binary outcomes (coexistence vs exclusion), allows us to examine classical niche and neutral perspectives using the same currency. High niche overlap speeds exclusion, but high similarity in competitive ability slows it. These predictions are confirmed by a well-known model of two species competing for two resources. Under ecologically plausible scenarios of correlation between these two factors, the strongest exclusion rates may be among moderately similar species, while very similar and highly dissimilar competitors have very low exclusion rates. Adding even small amounts of demographic stochasticity to the model blurs the line between deterministic and probabilistic coexistence still further. Thus, focusing on exclusion rate, instead of on the binary outcome of coexistence versus exclusion, allows a variety of outcomes to result from competitive interactions. This approach may help explain species coexistence in diverse competitive communities and raises novel issues for future work

Indirect effects of agricultural pesticide use on parasite prevalence in wild pollinators

Insect pollinators appear to be experiencing worldwide declines, a phenomenon that has been correlated both with exposure to chemical pesticides and disease prevalence. These factors have been found to have strong and often interacting negative effects on multiple pollinator species in laboratory based studies, however their interactions in the field are less clear. To try and understand the link between pesticide use on pollinator communities, and how this might impact on disease transmission, we took two complementary approaches. First, we undertook a series of pollinator surveys to assess the abundance and diversity of pollinator groups across British agricultural field sites subject to varying levels of pesticide use. We then screened the offspring of two taxa of tube nesting solitary bees (Osmia bicornis and Megachile spp.) for three parasite groups commonly associated with pollinators. We found lower pollinator abundance, group richness and diversity across agricultural sites associated with higher pesticide use. Specifically, there were fewer honey bees, hoverflies, solitary bees and wasps. Surprisingly, we found a lower prevalence of all three parasite groups in O. bicornis offspring reared in sites associated with higher pesticide use compared to lower pesticide use. We also found a lower prevalence of Ascosphaera but a higher prevalence of Microsporidia in Megachile offspring reared in sites associated with higher pesticide use compared to lower pesticide use. Together, our results suggest that agricultural sites associated with higher pesticide use may be affecting pollinators indirectly by disrupting community structure and influencing disease epidemiology and vectoring opportunities. This highlights the importance of understanding the interactions between pesticide use and disease in both managed and wild bee populations for the future mitigation of pollinator declines

Downscale: An R package for downscaling species occupancy from coarse-grain data to predict occupancy at fine-grain sizes

The geographical area occupied by a species is a valuable measure for assessing its conservation status. Coarse-grained occupancy maps are available for many taxa, e.g., as atlases, but often at spatial resolutions too coarse for conservation use. However, mapping occupancy at fine spatial resolution across the entire extent of the species’ distribution is often prohibitively expensive for the majority of species. Occupancy downscaling is a technique to estimate finer scale occupancy from coarse scale maps, by using the occupancy-area relationship (OAR) which reflects how the proportion of area occupied increases with spatial grain size. Models that describe the OAR are fitted to observed occupancies at the available coarse-grain sizes and then extrapolated to predict occupancy at the finer grain sizes required. The downscale package in the R programming environment provides users with easy-to-use functions for downscaling occupancy with ten published models. First, upgrain calculates occupancy for multiple grain sizes larger than the input data. Normal methods for aggregating raster data increase the extent of the focal area as grain size increases which is undesirable, so the function fixes the extent for all grain sizes, assigning unsampled cells as absences. Four suggested methods are provided to enable this and upgrain.threshold provides diagnostic plots that allow the user to explore the inherent trade-off between making assumptions about unsampled locations and discarding information from sampled locations. downscale fits nine possible models to the data generated from upgrain. hui.downscale fits the special case of the Hui model. predict and plot extrapolate the fitted models to predict and plot occupancy at finer grain sizes. Finally, ensemble.downscale simultaneously fits two or more of the downscaling models and calculates mean predicted occupancy across all selected models. Here we describe the package and apply the functions to atlas data of a hypothetical UK species

Multi-criterion trade-offs and synergies for spatial conservation planning

1. Nature conservation policies need to deliver on multiple criteria, including genetic diversity, population viability and species richness as well as ecosystem services. The challenge of integrating these may be addressed by simulation modelling. 2. We used four models (MetaConnect, SPOMSIM, a community model and InVEST) to assess a variety of spatial habitat patterns with two levels of total habitat cover and realised at two spatial scales, exploring which landscape structures performed best according to five different criteria assessed for four functional types of organisms (approximately representing trees, butterflies, small mammals and birds). 3. The results display both synergies and trade-offs: population size and pollination services generally benefitted more from fragmentation than did genetic heterozygosity, and species richness more than allelic richness, although the latter two varied considerably among the functional types. 4. No single landscape performed best across all criteria, but averaging over criteria and functional types, overall performance improved with greater levels of habitat cover and intermediate fragmentation (or less fragmentation in cases with lower habitat cover). 5. Synthesis and applications. Different conservation objectives must be traded off, and considering only a single taxon or criterion may result in sub-optimal choices when planning reserve networks. Nevertheless, heterogeneous spatial patterns of habitat can provide reasonable compromises for multiple criteria

Explainable neural networks for trait-based multispecies distribution modelling—A case study with butterflies and moths

Species response traits mediate environmental effects on species distribution. Traits are used in joint and multispecies distribution models (JSDMs and MSDMs) to enable community-wide shared parameters that characterise niche filtering along environmental gradients. Multispecies machine learning SDMs, however, do not use traits as their inclusion requires an additional taxonomic dimension that is incompatible with their usual tabular inputs. This has confined trait mediation in SDMs to hierarchical Bayesian models. Here we provide a novel artificial neural network (ANN) architecture that solves this dimensionality problem. Our ANN includes species traits (via a time distributed layer) and is therefore able to identify not only species-specific responses to the environment, but also shared responses across the community that are mediated by species traits. Model performance evaluated at the species level not only quantifies the reliability of species predictions, but also their departure from an average response dictated by traits only. We apply our model to two unique long-term spatio-temporal of butterfly and moth datasets collected across the United Kingdom between 1990 and 2019. In addition to species traits, predictors include numerous metrics derived from weather, land-cover and topology data. For butterflies and moths we show convincing model performance for classifying species occupancy. We use SHAP (Shapley Additive exPlanations) to explain the ANN and show how trait-mediated and species-specific responses can be approximated, hence yielding ecological insights on the key drivers of species distribution. We highlight a range of drivers of change that determine occupancy, including wind, temperature as well as habitat type. We demonstrate that a trait-based approach can be encoded as an ANN by using a time distributed layer. This brings ANNs unmatched predictive capabilities to the field of MSDMs, at the same time of lifting their reputed drawback of poor explainability

SDM profiling: A tool for assessing the information-content of sampled and unsampled locations for species distribution models

Species distribution models (SDMs) are key tools in biodiversity and conservation, but assessing their reliability in unsampled locations is difficult, especially where there are sampling biases. We present a spatially-explicit sensitivity analysis for SDMs – SDM profiling – which assesses the leverage that unsampled locations have on the overall model by exploring the interaction between the effect on the variable response curves and the prevalence of the affected environmental conditions. The method adds a ‘pseudo-presence’ and ‘pseudo-absence’ to unsampled locations, re-running the SDM for each, and measuring the difference between the probability surfaces of the original and new SDMs. When the standardised difference values are plotted against each other (a ‘profile plot’), each point's location can be summarized by four leverage measures, calculated as the distances to each corner. We explore several applications: visualization of model certainty; identification of optimal new sampling locations and redundant existing locations; and flagging potentially erroneous occurrence records

The Mating System of the Wild-to-Domesticated Complex of Gossypium hirsutum L. Is Mixed

The domestication syndrome of many plants includes changes in their mating systems. The evolution of the latter is shaped by ecological and genetic factors that are particular to an area. Thus, the reproductive biology of wild relatives must be studied in their natural distribution to understand the mating system of a crop species as a whole. Gossypium hirsutum (upland cotton) includes both domesticated varieties and wild populations of the same species. Most studies on mating systems describe cultivated cotton as self-pollinated, while studies on pollen dispersal report outcrossing; however, the mating system of upland cotton has not been described as mixed and little is known about its wild relatives. In this study we selected two wild metapopulations for comparison with domesticated plants and one metapopulation with evidence of recent gene flow between wild relatives and the crop to evaluate the mating system of cotton’s wild-to-domesticated complex. Using classic reproductive biology methods, our data demonstrate that upland cotton presents a mixed mating system throughout the complex. Given cotton’s capacity for outcrossing, differences caused by the domestication process in cultivated individuals can have consequences for its wild relatives. This characterization of the diversity of the wild relatives in their natural distribution, as well as their interactions with the crop, will be useful to design and implement adequate strategies for conservation and biosecurity

Accounting for biotic interactions through alpha-diversity constraints in stacked species distribution models

1. Species Distribution Models (SDM) are widely used to predict occupancy patterns at fine resolution over wide extents. However, SDMs generally ignore the effect of biotic interactions and tend to overpredict the number of species that can coexist at a given location and time (hereafter, the alpha-capacity). We developed an extension of SDMs that integrates species-level and community-level modelling to account for the above drivers. 2. The alpha-adjusted SDM takes the Probabilities of Occurrence (PoO) for all species of a community and the site’s alpha-capacity and adjusts the PoO, such that: a. their sum will equal the alpha-capacity as predicted by probability theory; and b. the adjusted PoO are dependent upon the relative suitability of each species for that site. The new method was tested using community data comprising 87 freshwater invertebrate species in an LTER watershed in Germany. We explored the ability of the method to predict alpha and beta-diversity patterns. We further focused on the effect on model performance at the species-level of the error associated with modelling alpha-capacity, of differences in gamma diversity (the size of the community) and of the type of community (random or guild-based). 3. The models that predicted alpha-capacity contained considerable error, and thus adjusting the PoO according to the modelled alpha-capacity resulted with decreased performance at the species level. However, when using the observed alpha-capacity to mimic a good alpha-capacity model, the alpha-adjusted SDMs usually resulted in increased performance. We further found that the alpha-adjusted SDM was better than the original SDM at predicting beta-diversity patterns, especially when using similarity indices that are sensitive to double absences. 4. Using the alpha-adjusted SDM approach may increase the predictive performance at the species and community levels if alpha-capacity can be assessed or modelled with sufficient accuracy, especially in relatively small communities of closely interacting species. With better models to predict alpha-capacity being developed, alpha-adjusted SDM has considerable potential to provide more realistic predictions of species-distribution patterns