ArticleEcological applications : a publication of the Ecological Society of America2021
Validation of presence-only models for conservation planning and the application to whales in a multiple-use marine park.
Article in Ecological applications : a publication of the Ecological Society of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
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- Comparative Analysis of MaxEnt and Deep Learning Approaches for Modeling Humpback Whale Distribution in North Iceland.Ecology and evolution · 2025Article
- Buzzing towards Resilience: Investigating the Spatial Alignment of the Desert Pallid Bee,Insects · 2024Article
- Spatial prioritization of dugong habitats in India can contribute towards achieving the 30 × 30 global biodiversity target.Scientific reports · 2024Article
- The analysis of ecological security and tourist satisfaction of ice-and-snow tourism under deep learning and the Internet of Things.Scientific reports · 2024Article
- Validation of presence-only models for conservation planning and the application to whales in a multiple-use marine park.Ecological applications : a publication of the Ecological Society of America · 2021Article
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Abstract
Identification of species' Biologically Important Areas (BIAs) is fundamental to conservation planning and species distribution models (SDMs) are a powerful tool commonly used to do this. Presence-only data are increasingly being used to develop SDMs to aid the conservation decision-making process. The application of presence-only SDMs for marine species' is particularly attractive due to often logistical and economic costs of obtaining systematic species' distribution data. However, robust model validation is important for conservation management applications that require accurate and reliable species' occurrence data (e.g., spatially explicit risk assessments). This is commonly done using a random subset of the data and less commonly with fully independent test data. Here, we apply a spatial block cross-validation (CV) approach to validate a MaxEnt presence-only model using independent presence/absence survey data for a highly mobile, marine species (humpback whale, Megaptera novaengliae) in the Great Barrier Reef (GBR). A MaxEnt model was developed using opportunistic whale sightings (2003-2007) and then used to identify areas differing in habitat suitability (low, medium, high) to conduct a systematic, line-transect, aerial survey (2012) and derive a density surface model. A spatial block CV buffering strategy was used to validate the MaxEnt model, using the opportunistic sightings as training data and independent aerial survey sightings data as test data. Moderate performance measures indicate MaxEnt was reliable in identifying the distribution patterns of a mobile whale species on their breeding ground, indicated by areas of high density aligned to areas of high habitat suitability. Furthermore, we demonstrate that MaxEnt models can be useful and cost-effective for designing a sampling scheme to undertake systematic surveys that significantly reduces sampling effort. In this study, higher quality information on whale reproductive class (calf vs. non-calf groups) was obtained that the presence-only data lacked, while sampling only 18% of the GBR World Heritage Area. The validation approach using fully independent data provides greater confidence in the MaxEnt model, which indicates significant overlap with the main breeding ground of humpback whales and the inner shipping route. This is important when evaluating presence-only models within certain conservation management applications, such as spatial risk assessments.
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