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

Pacifici, Krishna; Reich, Brian J.; Miller, David A. W.; Gardner, Beth; Stauffer, Glenn E.; Singh, Susheela P.; McKerrow, Alexa J.; Collazo, Jaime A.

doi:10.1002/ecy.1710

Cited by 224 publications

(307 citation statements)

References 45 publications

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“…Ideally, SDMs should be based on standard sampling protocols and rigorous data quality control checks. Instead, many, if not most SDMs use data collected under methods which result in presence-only data (Pacifici et al, 2017). While in many studies, this is a pragmatic solution, building models using these data can violate assumptions of some of the models (Yackulic et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Marine-Based Species Distribution Models (SDMs) with Recommendations for Best Practice

et al. 2017

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In the marine environment Species Distribution Models (SDMs) have been used in hundreds of papers for predicting the present and future geographic range and environmental niche of species. We have analyzed ways in which SDMs are being applied to marine species in order to recommend best practice in future studies. This systematic review was registered as a protocol on the Open Science Framework: https:// osf.io/tngs6/. The literature reviewed (236 papers) was published between 1992 and July 2016. The number of papers significantly increased through time (R 2 = 0.92, p < 0.05). The studies were predominantly carried out in the Temperate Northern Atlantic (45%) followed by studies of global scale (11%) and studies in Temperate Australasia (10%). The majority of studies reviewed focused on theoretical ecology (37%) including investigations of biological invasions by non-native organisms, conservation planning (19%), and climate change predictions (17%). Most of the studies were published in ecological, multidisciplinary, or biodiversity conservation journals. Most of the studies (94%) failed to report the amount of uncertainty derived from data deficiencies and model parameters. Best practice recommendations are proposed here to ensure that novice and advanced SDM users can (a) understand the main elements of SDMs, (b) reproduce standard methods and analysis, and (c) identify potential limitations with their data. We suggest that in the future, studies of marine SDMs should report on key features of the approaches employed, data deficiencies, the selection of the best explanatory model, and the approach taken to validate the SDM results. In addition, based on the literature reviewed, we suggest that future marine SDMs should account for uncertainty levels as part of the modeling process.

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Section: Introductionmentioning

confidence: 99%

A Systematic Review of Marine-Based Species Distribution Models (SDMs) with Recommendations for Best Practice

et al. 2017

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“…Improving both the quality and quantity of species occurrence data is crucial for biological monitoring and species distribution modeling (SDM) in the investigation of biodiversity [1][2][3][4]. Although professionally collected data are the preferred data source for SDM, they are expensive to collect and are often in short supply.…”

Section: Introductionmentioning

confidence: 99%

“…Although professionally collected data are the preferred data source for SDM, they are expensive to collect and are often in short supply. Data collected using proper crowdsourcing techniques, often termed "opportunistic data" [3][4][5][6][7][8][9][10][11][12] or unstructured volunteer data, can provide ecologists with a variety of biodiversity monitoring data. Consequently, volunteer-based citizen science monitoring systems have attracted a lot of attention.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the sheer quantity and spatial extent of opportunistic data can provide researchers and policy makers with information on ecological trends that may otherwise go unnoticed due to the relative scarcity of professionally collected data [4,10]. Studies demonstrating the similar predictive results of models built on opportunistic data versus professional data further justify the use of opportunistic data [27].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, cross-validating opportunistic data quality using predicted species presence probabilities [32] enables assessment of record outlier veracity, subsequent flagging, and filtering of these records. Therefore, opportunistic data reliability is increased by direct or indirect data integration with professionally collected field survey data [4,8].…”

Section: Introductionmentioning

confidence: 99%

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Identifying Reliable Opportunistic Data for Species Distribution Modeling: A Benchmark Data Optimization Approach

Lin

Lien

et al. 2017

Environments

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Abstract:The purpose of this study is to increase the number of species occurrence data by integrating opportunistic data with Global Biodiversity Information Facility (GBIF) benchmark data via a novel optimization technique. The optimization method utilizes Natural Language Processing (NLP) and a simulated annealing (SA) algorithm to maximize the average likelihood of species occurrence in maximum entropy presence-only species distribution models (SDM). We applied the Kruskal-Wallis test to assess the differences between the corresponding environmental variables and habitat suitability indices (HSI) among datasets, including data from GBIF, Facebook (FB), and data from optimally selected FB data. To quantify uncertainty in SDM predictions, and to quantify the efficacy of the proposed optimization procedure, we used a bootstrapping approach to generate 1000 subsets from five different datasets: (1) GBIF; (2) FB; (3) GBIF plus FB; (4) GBIF plus optimally selected FB; and (5) GBIF plus randomly selected FB. We compared the performance of simulated species distributions based on each of the above subsets via the area under the curve (AUC) of the receiver operating characteristic (ROC). We also performed correlation analysis between the average benchmark-based SDM outputs and the average dataset-based SDM outputs. Median AUCs of SDMs based on the dataset that combined benchmark GBIF data and optimally selected FB data were generally higher than the AUCs of other datasets, indicating the effectiveness of the optimization procedure. Our results suggest that the proposed approach increases the quality and quantity of data by effectively extracting opportunistic data from large unstructured datasets with respect to benchmark data.

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Effects of sea‐level rise and storm‐enhanced flooding on Pacific harbour seal habitat: A comparison of haul‐out changes at the Russian and Eel river estuaries

Backe

Hines

Nielsen

et al. 2021

Aquatic Conservation

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1. Patterns and changes in the distribution of coastal marine mammals can serve as indicators of environmental change that fill critical information gaps in coastal and marine environments. Coastal habitats are particularly vulnerable to the effects of near-term sea-level rise.2. In California, Pacific harbour seals (Phoca vitulina richardii) are a natural indicator species of coastal change because of their reliance on terrestrial habitats, abundance, distribution, and site fidelity. Pacific harbour seals are marine top predators that are easily observed while hauled out at terrestrial sites, which are essential for resting, pupping, and moulting.3. Although increasing inundation from recent sea-level rise and storm-driven flooding has changed the Californian coastline, little is known about the effect of future sea-level rise and increased storm frequency and strength on harbour seal haulout site availability and quality in California. 4. Harbour seal habitat was modelled at two sandbar-built estuaries under a series of likely sea-level rise and storm scenarios. The model outputs suggest that, over time, habitat at both estuaries decreased with increasing sea level, and storm-enhanced water levels contributed significantly to habitat flooding. These changes reflect pressures on coastal habitats that have an impact on human and natural systems.

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

Cited by 224 publications

References 45 publications

A Systematic Review of Marine-Based Species Distribution Models (SDMs) with Recommendations for Best Practice

A Systematic Review of Marine-Based Species Distribution Models (SDMs) with Recommendations for Best Practice

Identifying Reliable Opportunistic Data for Species Distribution Modeling: A Benchmark Data Optimization Approach

Effects of sea‐level rise and storm‐enhanced flooding on Pacific harbour seal habitat: A comparison of haul‐out changes at the Russian and Eel river estuaries

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