Opportunities and challenges for big data ornithology

Sorte, Frank A. La; Lepczyk, Christopher A.; Burnett, Jessica L.; Hurlbert, Allen H.; Tingley, Morgan W.; Zuckerberg, Benjamin

doi:10.1650/condor-17-206.1

Cited by 68 publications

(74 citation statements)

References 140 publications

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“…, La Sorte et al. ). Although big data provide a wealth of information, the use of big data presents a number of challenges such as massive volumes, high dimensionality, and sampling biases (Dickinson et al.…”

Section: Discussionmentioning

confidence: 99%

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A practical guide for combining data to model species distributions

Fletcher

Hefley

Robertson

et al. 2019

Ecology

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187

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%

“…Increasingly available “big data” sets in ecology may provide opportunities in this way (e.g., La Sorte et al. ). In addition, some data sources may have biases that can be alleviated by combining a second data source that does not contain the same limitations (Dorazio ).…”

Section: Introductionmentioning

confidence: 99%

A practical guide for combining data to model species distributions

Fletcher

Hefley

Robertson

et al. 2019

Ecology

Self Cite

187

257

View full text Add to dashboard Cite

show abstract

“…The resulting data, therefore, often suffer from limitations, including spatial biases, imprecise temporal and spatial resolutions, and under‐ or over‐reporting of species (Dickinson, Zuckerberg, & Bonter, ; Fitzpatrick, Preisser, Ellison, & Elkinton, ; Steger, Butt, & Hooten, ; Szabo, Vesk, Baxter, & Possingham, ; Tulloch, Mustin, Possingham, Szabo, & Wilson, ; Tulloch & Szabo, ; Tye, McCleery, Fletcher, Greene, & Butryn, ). However, because citizen scientists provide such high data volume, determining how best to use these data for ecological research will likely improve biogeographical insights, conservation decisions and conservation outcomes (Dickinson et al, ; La Sorte et al, ).…”

Section: Introductionmentioning

confidence: 99%

An evaluation of stringent filtering to improve species distribution models from citizen science data

Steen

Elphick

Tingley

2019

Diversity and Distributions

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Aim Citizen science data are increasingly used for modelling species distributions because they offer broad spatiotemporal coverage of local observations. However, such data are often collected without experimental design or set survey methods, raising the risk that bias and noise will compromise modelled predictions. We tested the ability of species distribution models (SDMs) built from these low‐structure citizen science data to match the quality of SDMs from systematically collected data and tested whether stringent data filtering improved predictions. Location Northeastern USA. Methods We evaluated models built from a rapidly growing dataset of avian occurrences reported by birders—eBird—against models built from four independent, systematically collected datasets. We developed SDMs for 96 species using both data sources and compared their predictive abilities. We also tested whether culling eBird data by applying stringent data filters on survey effort or observer expertise improved predictions. Results We found that SDMs built from low‐structure citizen science data matched or exceeded performance of SDMs from systematically collected datasets for 12%–31% of species (truex¯ = 22%), depending on the dataset. At least one culling option produced equivalent or better performance for 40%–70% of species (truex¯ = 49%). Data culling by restricting survey effort improved predictions more than restricting by observer expertise. The optimal effort restriction differed by dataset, and for three of the datasets was further informed by species traits. Main conclusions Species distribution models developed using low‐structure citizen science data sometimes performed as well as those from systematic data. Culling generally improved models, but results were heterogeneous, prohibiting clear recommendations for how to cull. Our results indicate that the growing availability of citizen science data holds potential for creating high‐quality spatial predictions, but that time should be invested in determining how best to cull datasets and that one‐size‐fits‐all solutions beyond basic outlier filtering may be hard to find.

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“…) can provide large temporal and spatial coverage of bird biodiversity (McCaffrey , La Sorte et al. ). Sightings may be disproportionately biased toward cities (Kelling et al.…”

Section: Introductionmentioning

confidence: 99%

The effects of local and landscape habitat attributes on bird diversity in urban greenspaces

et al. 2018

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Abstract. Contrasting trajectories of biodiversity loss and urban expansion make it imperative to understand biodiversity persistence in cities. Size-, local-, and landscape-level habitat factors of greenspaces in cities may be critical for future design and management of urban greenspaces in conserving bird biodiversity. Most current understanding of bird communities in cities has come from disparate analyses of single cities, over relatively short time periods, producing limited understanding of processes and characteristics of bird patterns for improved biodiversity management of the world's cities. We analyzed bird biodiversity in 112 urban greenspaces from 51 cities across eight countries, using eBird, a broadscale citizen science project. Species richness and Shannon diversity were used as response variables, while percent tree cover, percent water cover, and vegetation index were used as habitat predictor variables at both a landscape (5 and 25 km radius) and local-scale level (specific to an individual greenspace) in the modeling process, retrieved using Google Earth Engine. Area of a greenspace was the most important predictor of bird biodiversity, underlining the critical importance of habitat area as the most important factor for increasing bird biodiversity and mitigating loss from urbanization. Surprisingly, distance from the city center and distance from the coast were not significantly related to bird biodiversity. Landscape-scale habitat predictors were less related to bird biodiversity than local-scale habitat predictors. Ultimately, bird biodiversity loss could be mitigated by protecting and developing large greenspaces with varied habitat in the world's cities.

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Opportunities and challenges for big data ornithology

Cited by 68 publications

References 140 publications

A practical guide for combining data to model species distributions

A practical guide for combining data to model species distributions

An evaluation of stringent filtering to improve species distribution models from citizen science data

The effects of local and landscape habitat attributes on bird diversity in urban greenspaces

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