voluModel: Modelling species distributions in three‐dimensional space

Owens, Hannah L.; Rahbek, Carsten

doi:10.1111/2041-210x.14064

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…In addition to using different abiotic predictors to inform ecological niches, this evidence allowed us to infer the occurrence [or suitability] of jellyfish taxa within a threedimensional space, despite the two-dimensional data structure. This is a key component of our data and models, as marine organisms inhabit a three-dimensional environmental and ecological niche; therefore, modeling should include depth [63][64][65]. Given that the intrinsic nature of iNaturalist reports (photographs) primarily comprises reports of beached or shallow jellyfish lacking associated metadata (e.g., cooccurring taxa, substrate, or depth in situ), we are left to infer that jellyfish originated from nearby waters.…”

Section: Inferring Ecological Nichesmentioning

confidence: 99%

Leveraging Public Data to Predict Global Niches and Distributions of Rhizostome Jellyfishes

Anthony

Tan

Pitt

et al. 2023

Animals

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As climate change progresses rapidly, biodiversity declines, and ecosystems shift, it is becoming increasingly difficult to document dynamic populations, track fluctuations, and predict responses to climate change. Concurrently, publicly available databases and tools are improving scientific accessibility, increasing collaboration, and generating more data than ever before. One of the most successful projects is iNaturalist, an AI-driven social network doubling as a public database designed to allow citizen scientists to report personal biodiversity reports with accuracy. iNaturalist is especially useful for the research of rare, dangerous, and charismatic organisms, but requires better integration into the marine system. Despite their abundance and ecological relevance, there are few long-term, high-sample datasets for jellyfish, which makes management difficult. To provide some high-sample datasets and demonstrate the utility of publicly collected data, we synthesized two global datasets for ten genera of jellyfishes in the order Rhizostomeae containing 8412 curated datapoints from both iNaturalist (n = 7807) and the published literature (n = 605). We then used these reports in conjunction with publicly available environmental data to predict global niche partitioning and distributions. Initial niche models inferred that only two of ten genera have distinct niche spaces; however, the application of machine learning-based random forest models suggests genus-specific variation in the relevance of abiotic environmental variables used to predict jellyfish occurrence. Our approach to incorporating reports from the literature with iNaturalist data helped evaluate the quality of the models and, more importantly, the quality of the underlying data. We find that free, accessible online data is valuable, yet subject to biases through limited taxonomic, geographic, and environmental resolution. To improve data resolution, and in turn its informative power, we recommend increasing global participation through collaboration with experts, public figures, and hobbyists in underrepresented regions capable of implementing regionally coordinated projects.

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Section: Inferring Ecological Nichesmentioning

confidence: 99%

Leveraging Public Data to Predict Global Niches and Distributions of Rhizostome Jellyfishes

Anthony

Tan

Pitt

et al. 2023

Animals

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Pan-Atlantic 3D distribution model incorporating water column for commercial fish

Valle,

Ramírez-Romero,

Ibaibarriaga

et al. 2024

Ecological Modelling

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Perspectives and pitfalls in preserving subterranean biodiversity through protected areas

Mammola,

Altermatt,

Alther

et al. 2024

npj biodivers

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Subterranean ecosystems (comprising terrestrial, semi-aquatic, and aquatic components) are increasingly threatened by human activities; however, the current network of surface-protected areas is inadequate to safeguard subterranean biodiversity. Establishing protected areas for subterranean ecosystems is challenging. First, there are technical obstacles in mapping three-dimensional ecosystems with uncertain boundaries. Second, the rarity and endemism of subterranean organisms, combined with a scarcity of taxonomists, delays the accumulation of essential biodiversity knowledge. Third, establishing agreements to preserve subterranean ecosystems requires collaboration among multiple actors with often competing interests. This perspective addresses the challenges of preserving subterranean biodiversity through protected areas. Even in the face of uncertainties, we suggest it is both timely and critical to assess general criteria for subterranean biodiversity protection and implement them based on precautionary principles. To this end, we examine the current status of European protected areas and discuss solutions to improve their coverage of subterranean ecosystems.

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voluModel: Modelling species distributions in three‐dimensional space

Cited by 3 publications

References 29 publications

Leveraging Public Data to Predict Global Niches and Distributions of Rhizostome Jellyfishes

Leveraging Public Data to Predict Global Niches and Distributions of Rhizostome Jellyfishes

Pan-Atlantic 3D distribution model incorporating water column for commercial fish

Perspectives and pitfalls in preserving subterranean biodiversity through protected areas

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