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High host biodiversity is hypothesized to dilute the risk of vector‐borne diseases if many host species are ‘dead ends' that cannot effectively transmit the disease and low‐diversity areas tend to be dominated by competent host species. However, many studies on biodiversity–disease relationships characterize host biodiversity at single, local spatial scales, which complicates efforts to forecast disease risk if associations between host biodiversity and disease change with spatial scale. Here, our objective is to evaluate the spatial scaling of relationships between host biodiversity andBorrelia(the bacterial taxon which causes Lyme disease) infection prevalence in small mammals. We compared the associations between infection prevalence and small mammal host diversity for local communities (individual plots) and metacommunities (multiple plots aggregated within a landscape) sampled by the National Ecological Observatory Network (NEON), an emerging continental‐scale environmental monitoring program with a hierarchical sampling design. We applied a multispecies, spatially‐stratified capture–recapture model to a trapping dataset to estimate five small mammal biodiversity metrics, which we used to predict infection status for a subset of trapped individuals. We found that relationships betweenBorreliainfection prevalence and biodiversity did indeed vary when biodiversity was quantified at different spatial scales but that these scaling behaviors were idiosyncratic among the five biodiversity metrics. For example, species richness of local communities showed a negative (dilution) effect on infection prevalence, while species richness of the small mammal metacommunity showed a positive (amplification) effect on infection prevalence. Our modeling approach can inform future analyses as data from similar monitoring programs accumulate and become increasingly available through time. Our results indicate that a focus on single spatial scales when assessing the influence of biodiversity on disease risk provides an incomplete picture of the complexity of disease dynamics in ecosystems. 

Biologists routinely fit novel and complex statistical models to push the limits of our understanding. Examples include, but are not limited to, flexible Bayesian approaches (e.g. BUGS, stan), frequentist and likelihood‐based approaches (e.g. packageslme4) and machine learning methods.These software and programs afford the user greater control and flexibility in tailoring complex hierarchical models. However, this level of control and flexibility places a higher degree of responsibility on the user to evaluate the robustness of their statistical inference. To determine how often biologists are running model diagnostics on hierarchical models, we reviewed 50 recently published papers in 2021 in the journalNature Ecology & Evolution, and we found that the majority of published papers didnotreport any validation of their hierarchical models, making it difficult for the reader to assess the robustness of their inference. This lack of reporting likely stems from a lack of standardized guidance for best practices and standard methods.Here, we provide a guide to understanding and validating complex models using data simulations. To determine how often biologists use data simulation techniques, we also reviewed 50 recently published papers in 2021 in the journalMethods Ecology & Evolution. We found that 78% of the papers that proposed a new estimation technique, package or model used simulations or generated data in some capacity (18 of 23 papers); but very few of those papers (5 of 23 papers) included either a demonstration that the code could recover realistic estimates for a dataset with known parameters or a demonstration of the statistical properties of the approach. To distil the variety of simulations techniques and their uses, we provide a taxonomy of simulation studies based on the intended inference. We also encourage authors to include a basic validation study whenever novel statistical models are used, which in general, is easy to implement.Simulating data helps a researcher gain a deeper understanding of the models and their assumptions and establish the reliability of their estimation approaches. Wider adoption of data simulations by biologists can improve statistical inference, reliability and open science practices. 

Biodiversity is declining at unprecedented rates worldwide. Yet cascading effects of biodiversity loss on other taxa are largely unknown because baseline data are often unavailable. We document the collapse of a Neotropical snake community after the invasive fungal pathogen Batrachochytrium dendrobatidis caused a chytridiomycosis epizootic leading to the catastrophic loss of amphibians, a food source for snakes. After mass mortality of amphibians, the snake community contained fewer species and was more homogeneous across the study site, with several species in poorer body condition, despite no other systematic changes in the environment. The demise of the snake community after amphibian loss demonstrates the repercussive and often unnoticed consequences of the biodiversity crisis and calls attention to the invisible declines of rare and data-deficient species.