Ecological niche modeling reveals manta ray distribution and conservation priority areas in the Western Central Atlantic

Abstract

The Western Central Atlantic (WCA) hosts a population of manta rays whose distribution and habitat preference is poorly understood. Addressing this lack of knowledge will be essential to aid the effective implementation of targeted conservation measures. Here, we use ensemble ecological niche modeling to predict the monthly distribution of manta rays in the WCA using multiple sources of species occurrence data, and consequently identify core areas, which represent suitable habitat in all months of the year. Models suggest a seasonal cycle of expansion and contraction of suitable habitat from coastal areas in spring‐summer, to open ocean in autumn‐winter. Core areas are concentrated along the coast of the USA, Mexico, and Venezuela. Only 24% of predicted suitable habitat is encompassed by the present regional network of MPAs, and is intersected by numerous, heavily trafficked shipping lanes. Manta rays are slow‐growing animals that only produce a limited number of offspring; as such, this species is sensitive to even moderate increases in mortality rates. For this reason, the existing regional network of MPAs may offer them limited protection. As manta rays in the WCA are challenged by a complex landscape of threats and protective measures, future marine conservation planning in the region should explicitly address the conservation of manta rays, focusing on areas identified as key habitats for the species.

Phenology dictates the impact of climate change on geographic distributions of six co‐occurring North American grasshoppers

“…Projecting species distributions in future climates remains an important avenue of research. Doing so can inform us of habitat potentially at risk from species invasions (Gong et al, 2020 ; Kistner‐Thomas, 2019 ), identify species at risk of collapse (Lemoine, 2015 ), and pinpoint regions of high priority for conservation (Garzon et al, 2021 ). In doing so, researchers must carefully account for source of uncertainty.…”

“…ENMs correlate occurrence records with climate and are often used to predict range expansions. For example, ENMs can identify areas at risk of invasion under future climates (Gong et al, 2020 ; Kistner‐Thomas, 2019 ) or identify high‐priority conservation targets (Garzon et al, 2021 ), which is critically important as the ranges of many threatened species might collapse in the near future (Lemoine, 2015 ). One shortcoming is that ENMs rarely account for phenology (Ingenloff & Peterson, 2021 ); many simply use mean annual temperature or precipitation (Booth et al, 2014 ; Title & Bemmels, 2018 ).…”

The distribution of manta rays in the western North Atlantic Ocean off the eastern United States

“…The spatio-temporal distribution of manta rays in the EUS is poorly understood. A recent study evaluated habitat suitability and regional hotspots for manta rays in the Gulf of Mexico and Caribbean 30 . This ecological niche model 30 was developed using presence-only data for putative manta ray sightings, and found that distributions were best predicted by chlorophyll-a and bathymetric slope, with minimal predictive input from sea surface temperature (SST).…”

“…A recent study evaluated habitat suitability and regional hotspots for manta rays in the Gulf of Mexico and Caribbean 30 . This ecological niche model 30 was developed using presence-only data for putative manta ray sightings, and found that distributions were best predicted by chlorophyll-a and bathymetric slope, with minimal predictive input from sea surface temperature (SST). The authors noted that the majority of sightings were from coastal waters, potentially due to increased but unquantified survey effort 30 .…”

“…This ecological niche model 30 was developed using presence-only data for putative manta ray sightings, and found that distributions were best predicted by chlorophyll-a and bathymetric slope, with minimal predictive input from sea surface temperature (SST). The authors noted that the majority of sightings were from coastal waters, potentially due to increased but unquantified survey effort 30 . In our study, we integrate characterized manta ray sightings and survey effort data across numerous sources from decades of sampling across a broad geographic range, then evaluate these distance-weighted sampling data in a comprehensive species distribution modeling (SDM) framework to evaluate environmental drivers of manta ray occurrence.…”