A practical guide for combining data to model species distributions

Abstract

Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for modeling species distributions, such as data from citizen science programs, atlases, museums, and planned surveys. Yet reliably combining data sources can be challenging because data sources can vary considerably in their design, gradients covered, and potential sampling biases. We review, synthesize, and illustrate recent developments in combining multiple sources of data for species distribution modeling. We identify five ways in which multiple sources of data are typically combined for modeling species distributions. These approaches vary in their ability to accommodate sampling design, bias, and uncertainty when quantifying environmental relationships in species distribution models. Many of the challenges for combining data are solved through the prudent use of integrated species distribution models: models that simultaneously combine different data sources on species locations to quantify environmental relationships for explaining species distribution. We illustrate these approaches using planned survey data on 24 species of birds coupled with opportunistically collected eBird data in the southeastern United States. This example illustrates some of the benefits of data integration, such as increased precision in environmental relationships, greater predictive accuracy, and accounting for sample bias. Yet it also illustrates challenges of combining data sources with vastly different sampling methodologies and amounts of data. We provide one solution to this challenge through the use of weighted joint likelihoods. Weighted joint likelihoods provide a means to emphasize data sources based on different criteria (e.g., sample size), and we find that weighting improves predictions for all species considered. We conclude by providing practical guidance on combining multiple sources of data for modeling species distributions.

Next‐generation serology: integrating cross‐sectional and capture–recapture approaches to infer disease dynamics

“…Indeed, Integrated Population Modeling (IPM) has proven effective to improve demographic parameter estimations by integrating data sets of different natures (e.g., capture-recapture and counts) on the condition that they depend partly on the same set of (demographic) parameters (Besbeas et al 2002, Schaub et al 2007, Abadi et al 2010, Fletcher et al 2019. Indeed, Integrated Population Modeling (IPM) has proven effective to improve demographic parameter estimations by integrating data sets of different natures (e.g., capture-recapture and counts) on the condition that they depend partly on the same set of (demographic) parameters (Besbeas et al 2002, Schaub et al 2007, Abadi et al 2010, Fletcher et al 2019.…”

Resolving misaligned spatial data with integrated species distribution models

“…Several authors have put forth different approaches for integrating different data sources, typically when one source is collected through standardized surveys and the other source is not (Fletcher et al. , Miller et al. ).…”

“…) and are flexible enough to incorporate a wide range of auxiliary data sources (Fletcher et al. , Miller et al. ).…”

Multi‐scale integration of tree recruitment and range dynamics in a changing climate

“…). Variation in sample sizes and the amount of information contained within different datasets present a limitationfor integrated modelling approaches such as the one applied here, and the best approach for accommodating data disparity remains unclear(Fletcher et al, 2019). These regions may indicate target locations for activities intended to better anticipate and manage the effects of climate change including demographic monitoring and assisted migration(Aitken, Yeaman, Holliday, Wang, & Curtis-McLane, 2008;McLachlan, Hellmann, & Schwartz, 2007).At the same time, this modelling approach and resultant inferences have several important limitations.…”

“…While our modelling approach did not directly account for density dependence or other non-climatic factors that may influence carrying capacities, spatial evaluation of model uncertainty can be used to identify regions and populations where species may not respond to climate change in the manner expected based upon occurrence-environment relationships -a task for which SDMs based only on presence-absence data are inadequate. While beyond the scope of this current study, future applications of this model and other integrated models would benefit from a clearer understanding of the impact of weighting approaches on integrated models(Fletcher et al, 2019).Second, our models did not account for a number of potentially important ecological processes that are likely to impact range dynamics under future climate including biotic interactions, seed dispersal, and adaptation. First, the mean coefficient estimates resulting from the integrated model deviate little from the estimates of the naïve model, indicating that the posterior estimates of the integrated models were driven largely by the adult occurrence data with little contribution from the seedling data.…”