Capitalizing on Opportunistic Data for Monitoring Relative Abundances of Species

Giraud, Christophe; Clément, Christophe; Coron, Camille; Julliard, Romain

doi:10.1111/biom.12431

Cited by 52 publications

(80 citation statements)

References 36 publications

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“…(), and Giraud et al. () in Appendix . The general approaches we present are flexible, allowing for different data types (e.g., occurrence or abundance) and different species distribution modeling methods (e.g., occupancy estimators, Poisson point process models, and distance sampling) to be fit while using a common set of principles for integrating data sets.…”

Section: Introductionmentioning

confidence: 98%

Integrating multiple data sources in species distribution modeling: a framework for data fusion

Pacifici

Reich

Miller

et al. 2017

Ecology

216

278

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Abstract. The last decade has seen a dramatic increase in the use of species distribution models (SDMs) to characterize patterns of species' occurrence and abundance. Efforts to parameterize SDMs often create a tension between the quality and quantity of data available to fit models. Estimation methods that integrate both standardized and non-standardized data types offer a potential solution to the tradeoff between data quality and quantity. Recently several authors have developed approaches for jointly modeling two sources of data (one of high quality and one of lesser quality). We extend their work by allowing for explicit spatial autocorrelation in occurrence and detection error using a Multivariate Conditional Autoregressive (MVCAR) model and develop three models that share information in a less direct manner resulting in more robust performance when the auxiliary data is of lesser quality. We describe these three new approaches ("Shared," "Correlation," "Covariates") for combining data sources and show their use in a case study of the Brown-headed Nuthatch in the Southeastern U.S. and through simulations. All three of the approaches which used the second data source improved out-of-sample predictions relative to a single data source ("Single"). When information in the second data source is of high quality, the Shared model performs the best, but the Correlation and Covariates model also perform well. When the information quality in the second data source is of lesser quality, the Correlation and Covariates model performed better suggesting they are robust alternatives when little is known about auxiliary data collected opportunistically or through citizen scientists. Methods that allow for both data types to be used will maximize the useful information available for estimating species distributions.

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Section: Introductionmentioning

confidence: 98%

Integrating multiple data sources in species distribution modeling: a framework for data fusion

Pacifici

Reich

Miller

et al. 2017

Ecology

216

278

View full text Add to dashboard Cite

show abstract

“…), among multiple species (Giraud et al. , Thorson et al. , ), and among neighboring locations by incorporating spatial correlation (Thorson et al.…”

Section: Introductionmentioning

confidence: 99%

“…These new data-integration approaches seek to exhaust all available data sources to model species distributions while explicitly accounting for differences among data types (Dorazio 2014, Fithian et al 2015, Giraud et al 2016, Pacifici et al 2017, Coron et al 2018. The advantages of combining multiple data sources in integrated species distribution models (ISDMs) include increased spatial coverage, bias reduction and overall improvement in estimator accuracy (Dorazio 2014, Fithian et al 2015, Giraud et al 2016, Pacifici et al 2017. 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 2019, Miller et al 2019.…”

Section: Introductionmentioning

confidence: 99%

“…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 2019, Miller et al 2019. As a result, we now have a range of methods that leverage information across different data types (Dorazio 2014, Pacifici et al 2017, Zipkin et al 2017, among multiple species (Giraud et al 2016, Thorson et al 2016, and among neighboring locations by incorporating spatial correlation . As data becomes more available and easier to access the propensity to combine data will only increase, as will the demand to apply it rigorously to inform decision making.…”

Section: Introductionmentioning

confidence: 99%

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Resolving misaligned spatial data with integrated species distribution models

Pacifici

Reich

Miller

et al. 2019

Ecology

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Advances in species distribution modeling continue to be driven by a need to predict species responses to environmental change coupled with increasing data availability. Recent work has focused on development of methods that integrate multiple streams of data to model species distributions. Combining sources of information increases spatial coverage and can improve accuracy in estimates of species distributions. However, when fusing multiple streams of data, the temporal and spatial resolutions of data sources may be mismatched. This occurs when data sources have fluctuating geographic coverage, varying spatial scales and resolutions, and differing sources of bias and sparsity. It is well documented in the spatial statistics literature that ignoring the misalignment of different data sources will result in bias in both the point estimates and uncertainty. This will ultimately lead to inaccurate predictions of species distributions. Here, we examine the issue of misaligned data as it relates specifically to integrated species distribution models. We then provide a general solution that builds off work in the statistical literature for the change‐of‐support problem. Specifically, we leverage spatial correlation and repeat observations at multiple scales to make statistically valid predictions at the ecologically relevant scale of inference. An added feature of the approach is that addressing differences in spatial resolution between data sets can allow for the evaluation and calibration of lesser‐quality sources in many instances. Using both simulations and data examples, we highlight the utility of this modeling approach and the consequences of not reconciling misaligned spatial data. We conclude with a brief discussion of the upcoming challenges and obstacles for species distribution modeling via data fusion.

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“…The lack of well-tailored survey data highlighted the need to develop a hierarchical Bayesian SDM, which models the species observations as functions of environmental covariates, spatiotemporal location and sampling effort (Chakraborty, Gelfand, Wilson, Latimer, & Silander, 2011;Dorazio, 2014;Fithian, Elith, Hastie, & Keith, 2015;Giraud, Calenge, Coron, & Julliard, 2016).…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals

Mäkinen

Vanhatalo

2018

Diversity and Distributions

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Aim Our aim involved developing a method to analyse spatiotemporal distributions of Arctic marine mammals (AMMs) using heterogeneous open source data, such as scientific papers and open repositories. Another aim was to quantitatively estimate the effects of environmental covariates on AMMs’ distributions and to analyse whether their distributions have shifted along with environmental changes. Location Arctic shelf area. The Kara Sea. Methods Our literature search focused on survey data regarding polar bears (Ursus maritimus), Atlantic walruses (Odobenus rosmarus rosmarus) and ringed seals (Phoca hispida). We mapped the data on a grid and built a hierarchical Poisson point process model to analyse species’ densities. The heterogeneous data lacked information on survey intensity and we could model only the relative density of each species. We explained relative densities with environmental covariates and random effects reflecting excess spatiotemporal variation and the unknown, varying sampling effort. The relative density of polar bears was explained also by the relative density of seals. Results The most important covariates explaining AMMs’ relative densities were ice concentration and distance to the coast, and regarding polar bears, also the relative density of seals. The results suggest that due to the decrease in the average ice concentration, the relative densities of polar bears and walruses slightly decreased or stayed constant during the 17‐year‐long study period, whereas seals shifted their distribution from the Eastern to the Western Kara Sea. Main conclusions Point process modelling is a robust methodology to estimate distributions from heterogeneous observations, providing spatially explicit information about ecosystems and thus serves advances for conservation efforts in the Arctic. In a simple trophic system, a distribution model of a top predator benefits from utilizing prey species’ distributions compared to a solely environmental model. The decreasing ice cover seems to have led to changes in AMMs’ distributions in the marginal Arctic region.

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Capitalizing on Opportunistic Data for Monitoring Relative Abundances of Species

Cited by 52 publications

References 36 publications

Integrating multiple data sources in species distribution modeling: a framework for data fusion

Integrating multiple data sources in species distribution modeling: a framework for data fusion

Resolving misaligned spatial data with integrated species distribution models

Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals

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