Integrating multiple datasets with species distribution models to inform conservation of the poorly-recorded Chinese seahorses

Zhang, Xiong; Vincent, Amanda C. J.

doi:10.1016/j.biocon.2017.05.020

Cited by 21 publications

(26 citation statements)

References 66 publications

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“…We are the first to provide quantitative evidence that habitat dependency varies largely among seahorse species. We demonstrate global divergences on distributions and habitat associations among species, which has been observed in local studies (Curtis & Vincent, ; Lourie, Green, & Vincent, ; Zhang & Vincent, ). Similar divergence patterns among congeneric species are also common in pipefishes (Malavasi et al., ) and other fishes (Fairclough, Clarke, Valesini, & Potter, ; Lombarte, Recasens, González, & de Sola, ).…”

Section: Discussionsupporting

confidence: 74%

“…These maps can further be used with threat maps if available to determine global priority areas for seahorse conservation. Conservation programmes can use and validate our maps and habitat knowledge to study and protect seahorse populations at local scales (Zhang & Vincent, ).…”

Section: Discussionmentioning

confidence: 99%

“…Model Group 2 : To examine the utility of different sources of species data, we conducted models for each species based on the “best performing” predictor dataset (derived from Model Group 1) and each of the seven datasets of seahorse occurrences: RG, CS, MC, CS plus MC, RG plus CS, RG plus MC and ALL. We compared the accuracy in predicting presences (i.e., sensitivity; Altman & Bland, ) among the models and estimated prediction agreement between models of individual species‐data sources (RG, CS and MC) based on I Similarity Statistic (Warren, Glor, & Turelli, ) and Presence Agreement (Zhang & Vincent, ). The former was used to estimate similarity between two presence‐probability maps, while the latter evaluated overlapping rate between two predictive‐presence maps.…”

Section: Methodsmentioning

confidence: 99%

“…We applied Maxent to generate the presence‐probability map for every species that had at least five occurrences (Pearson et al., ), based on the best predictor and species datasets identified above. To estimate species richness, we stacked the presence‐probability maps of all species that had fair to excellent model performance (i.e., AUC ≥ 0.7; Calabrese, Certain, Kraan, & Dormann, ; Zhang & Vincent, ). For species that we were unable to derive acceptable predictive maps or that had few occurrences ( n < 5), we added their collated occurrences directly to corresponding pixels of the stacked map in ArcMap.…”

Section: Methodsmentioning

confidence: 99%

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Predicting distributions, habitat preferences and associated conservation implications for a genus of rare fishes, seahorses (Hippocampus spp.)

Zhang

Vincent

2018

Diversity and Distributions

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Aim To 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. Location The shallow seas. Methods We 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. Results We 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 conclusions We 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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Section: Discussionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Predicting distributions, habitat preferences and associated conservation implications for a genus of rare fishes, seahorses (Hippocampus spp.)

Zhang

Vincent

2018

Diversity and Distributions

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“…; Halpern et al. ; Zhang & Vincent ). From 2012 to 2018, Asian countries generally had slight increases in ocean health scores (OHI Team ), suggesting that threat mitigation in this area is ongoing but perhaps should be accelerated.…”

Section: Discussionmentioning

confidence: 99%

Using cumulative human‐impact models to reveal global threat patterns for seahorses

Zhang

Vincent

2019

Conservation Biology

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Understanding threats acting on marine organisms and their conservation status is vital but challenging given a paucity of data. We studied the cumulative human impact (CHI) on and conservation status of seahorses (Hippocampus spp.)—a genus of rare and data‐poor marine fishes. With expert knowledge and relevant spatial data sets, we built linear‐additive models to assess and map the CHI of 12 anthropogenic stressors on 42 seahorse species. We examined the utility of indices of estimated impact (impact of each stressor and CHI) in predicting conservation status for species with random forest (RF) models. The CHI values for threatened species were significantly higher than those for nonthreatened species (category based on International Union for Conservation of Nature Red List). We derived high‐accuracy RF models (87% and 96%) that predicted that 5 of the 17 data‐deficient species were threatened. Demersal fishing practices with high bycatch and pollution were the best predictors of threat category. Major threat epicenters were in China, Southeast Asia, and Europe. Our results and maps of CHI may help guide global seahorse conservation and indicate that modeling and mapping human impacts can reveal threat patterns and conservation status for data‐poor species. We found that for exploring threat patterns of focal species, species‐level CHI models are better than existing ecosystem‐level CHI models.

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Distribution and habitat preferences of Hippocampus species along the Apulian coast

Lazic

Pierri

Gristina

et al. 2018

Aquatic Conservation

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The aim of this study was to provide further knowledge of the ecological, spatial, and temporal distribution and life‐cycle traits of the sympatric seahorse species Hippocampus hippocampus and Hippocampus guttulatus. From 2011 to 2014, data were collected from 20 sites along the Apulian coast (Southern Italy) in order to cover a wide range of coastal marine, open water, and lagoon sites, from the surface to a depth of 48 metres. Seahorses were found at seven of the 20 sites investigated. Both species displayed temporal persistence, patchy distribution, and a high spatial demographic heterogeneity. This study revealed a smaller maximum size and size at maturity of H. guttulatus than has been reported elsewhere. Although it is possible that environmental factors are influencing the size of these animals, there may be genetic explanations, especially for lagoon populations. Despite the fragmented distribution and often low abundance found along the Apulian coast, some seahorse populations appear to be locally persistent and stable over time, suggesting that there is a need for appropriate location‐specific conservation strategies. Not just maintaining but increasing the area of artificial substrates, which enhance habitat complexity and provide holdfasts, could represent a possible approach to help conserve marine littoral biodiversity and to protect endangered species in lagoons.

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Integrating multiple datasets with species distribution models to inform conservation of the poorly-recorded Chinese seahorses

Cited by 21 publications

References 66 publications

Predicting distributions, habitat preferences and associated conservation implications for a genus of rare fishes, seahorses (Hippocampus spp.)

Predicting distributions, habitat preferences and associated conservation implications for a genus of rare fishes, seahorses (Hippocampus spp.)

Using cumulative human‐impact models to reveal global threat patterns for seahorses

Distribution and habitat preferences of Hippocampus species along the Apulian coast

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