Predicting distributions, habitat preferences and associated conservation implications for a genus of rare fishes, seahorses (Hippocampus spp.)
Zhang, X.; Vincent, A.C.J. (2018). Predicting distributions, habitat preferences and associated conservation implications for a genus of rare fishes, seahorses (Hippocampus spp.). Diversity Distrib. 24(7): 1005-1017. https://dx.doi.org/10.1111/ddi.12741
In: Diversity and Distributions. Blackwell: Oxford. ISSN 1366-9516; e-ISSN 1472-4642, more
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| Keywords |
Data integrity > Data quality
Taxa > Species > Rare species
Hippocampus Rafinesque, 1810 [WoRMS]
Marine/Coastal |
| Author keywords |
citizen science; Maxent; museum collections; seahorse; species distribution model |
| Authors | | Top |
- Zhang, X.
- Vincent, A.C.J.
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| Abstract |
AimTo identify useful sources of species data and appropriate habitat variables for species distribution modelling on rare species, with seahorses as an example, deriving ecological knowledge and spatially explicit maps to advance global seahorse conservation. LocationThe shallow seas.MethodsWe applied a typical species distribution model (SDM), maximum entropy, to examine the utility of (1) two versions of habitat variables (habitat occurrences vs. proximity to habitats) and (2) three sources of species data: quality research‐grade (RG) data, quality‐unknown citizen science (CS) and museum‐collection (MC) data. We used the best combinations of species data and habitat variables to predict distributions and estimate species–habitat relations and threatened status for seahorse species. ResultsWe demonstrated that using “proximity to habitats” and integrating all species datasets (RG, CS and MC) derived models with the highest accuracies among all dataset variations. Based on this finding, we derived reliable models for 33 species. Our models suggested that only 0.4% of potential seahorse range was suitable to more than three species together; seahorse biogeographic epicentres were mainly in the Philippines; and proximity to sponges was an important habitat variable. We found that 12 “Data Deficient” species might be threatened based on our predictions according to IUCN criteria. Main conclusionsWe highlight that using proper habitat variables (e.g., proximity to habitats) is critical to determine distributions and key habitats for low‐mobility animals; collating and integrating quality‐unknown occurrences (e.g., CS and MC) with quality research data are meaningful for building SDMs for rare species. We encourage the application of SDMs to estimate area of occupancy for rare organisms to facilitate their conservation status assessment. |
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