Causal inference and large‐scale expert validation shed light on the drivers of <scp>SDM</scp> accuracy and variance

Boyd, Robin J.; Harvey, Martin; Roy, David B.; Barber, Tony; Haysom, Karen A.; Macadam, Craig R.; Morris, Roger; Palmer, Carolyn G.; Palmer, Stephen; Preston, Chris; Taylor, Pam; Ward, Robert; Ball, Stuart G.; Pescott, Oliver L.

doi:10.1111/ddi.13698

Cited by 5 publications

(1 citation statement)

References 86 publications

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“…To produce initial SDMs for each virtual species in each assemblage, we used an ensemble of three commonly applied models: logistic regression (GLM), generalised additive models (GAM) and random forests (RF) using code modified from the package soaR (https:// github. com/ robbo yd/ soaR; Boyd et al, 2023). Ensemble models are commonly used in analyses of species distributions, particularly when modelling large numbers of species for which it is infeasible to fine-tune the explanatory variables used for individual species (Hao et al, 2020).…”

Section: Modelling Of Baseline Datamentioning

confidence: 99%

Adaptive sampling by citizen scientists improves species distribution model performance: A simulation study

Mondain‐Monval,

Pocock,

Rolph

et al. 2024

Methods Ecol Evol

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Volunteer recorders generate large amounts of biodiversity data through citizen science which is used in conservation planning and policy decision‐making. Unstructured sampling, where the volunteer can record what they want, where they want, leads to spatial unevenness in these data. While there are many statistical techniques to account for the resulting biases, it may be possible to improve datasets by directing a subset of recorders to sample in the most informative locations, known as adaptive sampling. We investigated the potential for adaptive sampling to improve the performance of species distribution models built on citizen science data using simulated ecological communities. We simulated ecological assemblages across Great Britain based on current butterfly data and modelled the distributions of each species. We then simulated the sampling of new data based on five adaptive sampling methods (one empirical method based simply on gap‐filling, and four model‐based methods using various measures from the model outputs) and one non‐adaptive method (a method in which recording continued in the current pattern), and re‐ran the species distribution models. In these, we also varied the rate of recording effort that was distributed according to adaptive sampling. The model predictions using the original and adaptively sampled data were compared to true species distributions to evaluate the performance of each method. We found that all adaptive sampling approaches improved model performance, with greatest improvement for model‐based approaches compared to the empirical sampling method (i.e. simple gap‐filling). All four model‐based adaptive sampling approaches provided similar benefits for model outputs. Improvements in model performance were greatest when the amount of adaptive sampling changed from no uptake to 1% uptake, indicating that only a small amount of change in recorder behaviour is needed to improve model performance. Directing volunteer recorders to places where records are most needed, based on information from model outputs, can improve species distribution models built on citizen science data, even with minimal uptake of suggested locations. Our results therefore suggest that adaptive sampling by recorders could be beneficial for real‐world citizen science datasets.

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Section: Modelling Of Baseline Datamentioning

confidence: 99%

Adaptive sampling by citizen scientists improves species distribution model performance: A simulation study

Mondain‐Monval,

Pocock,

Rolph

et al. 2024

Methods Ecol Evol

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An operational workflow for producing periodic estimates of species occupancy at national scales

et al. 2023

Self Cite

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Policy makers require high‐level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe an operational workflow for generating annual estimates of species occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy‐relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, the principal output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5000 species in each of several defined ‘regions’ (e.g. countries, protected areas, etc.) of the UK from 1970 to 2019. This data product corresponds closely to the notion of a species distribution Essential Biodiversity Variable (EBV). Throughout the paper, we highlight methodologies that might not be applicable outside of the UK and suggest alternatives. We also highlight areas where the workflow can be improved; in particular, methods are needed to mitigate and communicate the risk of bias arising from the lack of representativeness that is typical of biodiversity data. Finally, we revisit the ‘ideal’ and ‘minimal’ criteria for species distribution EBVs laid out in previous contributions and pose some outstanding questions that should be addressed as a matter of priority. Going forward, we hope that this paper acts as a template for research groups around the world seeking to develop similar data products.

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Improving distribution models of sparsely documented disease vectors by incorporating information on related species via joint modeling

Mowry,

Moore,

Achee

et al. 2024

Ecography

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A necessary component of understanding vector‐borne disease risk is accurate characterization of the distributions of their vectors. Species distribution models have been successfully applied to data‐rich species but may produce inaccurate results for sparsely documented vectors. In light of global change, vectors that are currently not well‐documented could become increasingly important, requiring tools to predict their distributions. One way to achieve this could be to leverage data on related species to inform the distribution of a sparsely documented vector based on the assumption that the environmental niches of related species are not independent. Relatedly, there is a natural dependence of the spatial distribution of a disease on the spatial dependence of its vector. Here, we propose to exploit these correlations by fitting a hierarchical model jointly to data on multiple vector species and their associated human diseases to improve distribution models of sparsely documented species. To demonstrate this approach, we evaluated the ability of twelve models – which differed in their pooling of data from multiple vector species and inclusion of disease data – to improve distribution estimates of sparsely documented vectors. We assessed our models on two simulated datasets, which allowed us to generalize our results and examine their mechanisms. We found that when the focal species is sparsely documented, incorporating data on related vector species reduces uncertainty and improves accuracy by reducing overfitting. When data on vector species are already incorporated, disease data only marginally improve model performance. However, when data on other vectors are not available, disease data can improve model accuracy and reduce overfitting and uncertainty. We then assessed the approach on empirical data on ticks and tick‐borne diseases in Florida and found that incorporating data on other vector species improved model performance. This study illustrates the value in exploiting correlated data via joint modeling to improve distribution models of data‐limited species.

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Causal inference and large‐scale expert validation shed light on the drivers of SDM accuracy and variance

Cited by 5 publications

References 86 publications

Adaptive sampling by citizen scientists improves species distribution model performance: A simulation study

Adaptive sampling by citizen scientists improves species distribution model performance: A simulation study

An operational workflow for producing periodic estimates of species occupancy at national scales

Improving distribution models of sparsely documented disease vectors by incorporating information on related species via joint modeling

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