Disentangling data discrepancies with integrated population models

Saunders, Sarah P.; Farr, Matthew; Wright, Alexander D.; Bahlai, Christie A.; Ribeiro, José Wagner; Rossman, Sam; Sussman, Allison; Arnold, Todd W.; Zipkin, Elise F.

doi:10.1002/ecy.2714

Cited by 61 publications

(67 citation statements)

References 61 publications

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“…The problem of data weighting for integrated population models has been emphasized in fisheries (Francis , Punt , see also Saunders et al. ), and a variety of data weighting schemes have been considered. For example, individual data points have been weighted based on measures of variability (e.g., SD, CV of data used to generate abundance indices) or sample size (e.g., number of fish caught for compositional data) for each sampling unit (Francis , Punt ).…”

Section: Discussionmentioning

confidence: 99%

A practical guide for combining data to model species distributions

Fletcher

Hefley

Robertson

et al. 2019

Ecology

196

257

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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.

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Section: Discussionmentioning

confidence: 99%

A practical guide for combining data to model species distributions

Fletcher

Hefley

Robertson

et al. 2019

Ecology

196

257

View full text Add to dashboard Cite

show abstract

“…a). Tools for measuring population variation over time and accounting for complex ecological information (Saunders et al ., ; Bahlai & Zipkin, ) already exist in different fields such as paleoecology (e.g. Wilf et al ., ; Howard et al ., ), and conservation genomics (Beichman et al ., ).…”

Section: A Way Forwardmentioning

confidence: 99%

Interpreting insect declines: seven challenges and a way forward

Didham

Basset

Collins

et al. 2020

Insect Conserv Diversity

334

355

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Many insect species are under threat from the anthropogenic drivers of global change. There have been numerous well‐documented examples of insect population declines and extinctions in the scientific literature, but recent weaker studies making extreme claims of a global crisis have drawn widespread media coverage and brought unprecedented public attention. This spotlight might be a double‐edged sword if the veracity of alarmist insect decline statements do not stand up to close scrutiny. We identify seven key challenges in drawing robust inference about insect population declines: establishment of the historical baseline, representativeness of site selection, robustness of time series trend estimation, mitigation of detection bias effects, and ability to account for potential artefacts of density dependence, phenological shifts and scale‐dependence in extrapolation from sample abundance to population‐level inference. Insect population fluctuations are complex. Greater care is needed when evaluating evidence for population trends and in identifying drivers of those trends. We present guidelines for best‐practise approaches that avoid methodological errors, mitigate potential biases and produce more robust analyses of time series trends. Despite many existing challenges and pitfalls, we present a forward‐looking prospectus for the future of insect population monitoring, highlighting opportunities for more creative exploitation of existing baseline data, technological advances in sampling and novel computational approaches. Entomologists cannot tackle these challenges alone, and it is only through collaboration with citizen scientists, other research scientists in many disciplines, and data analysts that the next generation of researchers will bridge the gap between little bugs and big data.

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“…The papers by Saunders et al. () and Plard et al. () focus on integrated population models (one kind of IPMs), a specific subset of integrated models in which the joint likelihoods of multiple data sets (typically including capture – recapture and population count data) are used to estimate life history demographic rates (e.g., survival, fecundity, and immigration).…”

mentioning

confidence: 99%

“…While IPMs are typically used because of deficiencies in various data sets, Saunders et al. () highlight how IPMs can also be used to resolve discrepancies in inferences from individual analysis of independent data sets. Plard et al.…”

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confidence: 99%