Dataset Information

Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media.

ABSTRACT: A species' distribution provides fundamental information on: climatic niche, biogeography, and conservation status. Species distribution models often use occurrence records from biodiversity databases, subject to spatial and taxonomic biases. Deficiencies in occurrence data can lead to incomplete species distribution estimates. We can incorporate other data sources to supplement occurrence datasets. The general public is creating (via GPS-enabled cameras to photograph wildlife) incidental occurrence records that may present an opportunity to improve species distribution models. We investigated (1) occurrence data of a cryptic group of animals: non-marine snakes, in a biodiversity database (Global Biodiversity Information Facility (GBIF)) and determined (2) whether incidental occurrence records extracted from geo-tagged social media images (Flickr) could improve distribution models for 18 tropical snake species. We provide R code to search for and extract data from images using Flickr's API. We show the biodiversity database's 302,386 records disproportionately originate from North America, Europe and Oceania (250,063, 82.7%), with substantial gaps in tropical areas that host the highest snake diversity. North America, Europe and Oceania averaged several hundred records per species; whereas Asia, Africa and South America averaged less than 35 per species. Occurrence density showed similar patterns; Asia, Africa and South America have roughly ten-fold fewer records per 100 km2than other regions. Social media provided 44,687 potential records. However, including them in distribution models only marginally impacted niche estimations; niche overlap indices were consistently over 0.9. Similarly, we show negligible differences in Maxent model performance between models trained using GBIF-only and Flickr-supplemented datasets. Model performance appeared dependent on species, rather than number of occurrences or training dataset. We suggest that for tropical snakes, accessible social media currently fails to deliver appreciable benefits for estimating species distributions; but due to the variation between species and the rapid growth in social media data, may still be worth considering in future contexts.

SUBMITTER: Marshall BM

PROVIDER: S-EPMC6924322 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media.

Marshall Benjamin M BM Strine Colin T CT

PeerJ 20191217

A species' distribution provides fundamental information on: climatic niche, biogeography, and conservation status. Species distribution models often use occurrence records from biodiversity databases, subject to spatial and taxonomic biases. Deficiencies in occurrence data can lead to incomplete species distribution estimates. We can incorporate other data sources to supplement occurrence datasets. The general public is creating (via GPS-enabled cameras to photograph wildlife) incidental occurr ...[more]

PMID: 31871833

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Project description:Biases in data availability have serious consequences on scientific inferences that can be derived. The potential consequences of these biases could be more detrimental in the less-studied megadiverse regions, often characterized by high biodiversity and serious risks of human threats, as conservation and management actions could be misdirected. Here, focusing on 134 bat species in Mexico, we analyze spatial and taxonomic biases and their drivers in occurrence data; and identify priority areas for further data collection which are currently under-sampled or at future environmental risk. We collated a comprehensive database of 26,192 presence-only bat records in Mexico to characterize taxonomic and spatial biases and relate them to species' characteristics (range size and foraging behavior). Next, we examined variables related to accessibility, species richness and security to explain the spatial patterns in occurrence records. Finally, we compared the spatial distributions of existing data and future threats to these species to highlight those regions that are likely to experience an increased level of threats but are currently under-surveyed. We found taxonomic biases, where species with wider geographical ranges and narrow-space foragers (species easily captured with traditional methods), had more occurrence data. There was a significant oversampling toward tropical regions, and the presence and number of records was positively associated with areas of high topographic heterogeneity, road density, urban, and protected areas, and negatively associated with areas which were predicted to have future increases in temperature and precipitation. Sampling efforts for Mexican bats appear to have focused disproportionately on easily captured species, tropical regions, areas of high species richness and security; leading to under-sampling in areas of high future threats. These biases could substantially influence the assessment of current status of, and future anthropogenic impacts on, this diverse species group in a tropical megadiverse country.

Project description:Species Distribution Models (SDMs) are widely used to understand environmental controls on species' ranges and to forecast species range shifts in response to climatic changes. The quality of input data is crucial determinant of the model's accuracy. While museum records can be useful sources of presence data for many species, they do not always include accurate geographic coordinates. Therefore, actual locations must be verified through the process of georeferencing. We present a practical, standardized manual georeferencing method (the Spatial Analysis Georeferencing Accuracy (SAGA) protocol) to classify the spatial resolution of museum records specifically for building improved SDMs. We used the high-elevation plant Saxifraga austromontana Wiegand (Saxifragaceae) as a case study to test the effect of using this protocol when developing an SDM. In MAXENT, we generated and compared SDMs using a comprehensive occurrence dataset that had undergone three different levels of georeferencing: (1) trained using all publicly available herbarium records of the species, minus outliers (2) trained using herbarium records claimed to be previously georeferenced, and (3) trained using herbarium records that we have manually georeferenced to a ≤ 1-km resolution using the SAGA protocol. Model predictions of suitable habitat for S. austromontana differed greatly depending on georeferencing level. The SDMs fitted with presence locations georeferenced using SAGA outperformed all others. Differences among models were exacerbated for future distribution predictions. Under rapid climate change, accurately forecasting the response of species becomes increasingly important. Failure to georeference location data and cull inaccurate samples leads to erroneous model output, limiting the utility of spatial analyses. We present a simple, standardized georeferencing method to be adopted by curators, ecologists, and modelers to improve the geographic accuracy of museum records and SDM predictions.

Project description:PurposeBrooke is a non-government organisation with working equine welfare programmes across Africa, Asia and Latin America. In 2014, staff from ten country programmes were asked to identify 'no-win' situations (subsequently reframed as 'hard-wins')-where improving equine welfare is proving difficult, expensive and/or marginal-in order to inform strategic decisions on how to approach, manage and mitigate for such situations.MethodsThe Delphi-type consultation process had three phases. Round 1 posed five questions in the form of a workshop, survey and semi-structured interviews. Round 2 re-presented key themes and sense-checked initial conclusions. Round 3 reviewed the nature and prevalence of hard-win situations at an international meeting of all participants.ResultsReasons given for hard-win situations included: no economic or social benefit from caring for working animals; poor resource availability; lack of empathy for working equids or their owners among wider stakeholders; deep-seated social issues, such as addiction or illegal working; areas with a high animal turnover or migratory human population; lack of community cooperation or cohesion; unsafe areas where welfare interventions cannot be adequately supported. Participants estimated the prevalence of hard-win situations as 40-70% of their work. They suggested some current ways of working that may be contributing to the problem, and opportunities to tackle hard-wins more effectively.Conclusion and animal welfare implicationsRespondents agreed that if equine welfare improvements are to span generations of animals, interventions cannot rely on relatively simple, technical knowledge-transfer strategies and quick-wins alone. Programmes need to be more flexible and iterative and less risk-averse in their approaches to embedding good equine welfare practices in all relevant actors. Consultation recommendations informed development of Brooke's new global strategy, a revised organisational structure and redefinition of roles and responsibilities to streamline ways to approach hard-wins in the complex environments and socio-economic contexts in which working equids are found.

Dataset Information

Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media.

Publications

Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media.

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