Geology topics

Mariana M.P.B. Fuentes

Publications and source records attributed to Mariana M.P.B. Fuentes.

3 recordsLinked to original sources

USGS2026

Population structure and genetic stock identification in southeastern United States loggerhead sea turtles (Caretta caretta) using genome-wide SNPs

Characterizing the genetic structure and connectivity between populations of endangered species can be used to inform management actions. In vagile species with high gene flow or recently established populations, such characterizations can be difficult to undertake using traditional genetic markers, and genetic stock identification (GSI) may be confounded by allele-sharing between populations. Loggerhead sea turtles ( Caretta caretta ) in the southeastern United States comprise seven management units (MUs) based on female philopatry inferred via mitochondrial DNA sequences, yet nuclear microsatellite data do not reflect divergence. Further, loci for accurate GSI are not currently known. To address this, we generated genome-wide single nucleotide polymorphism (SNP) data from 146 females nesting at individual sites representative of each southeastern United States MU. We found weak (F ST =0.001–0.003) but significant divergence among all MUs, with more notable divergence between the Gulf Coast and Atlantic Ocean MUs, and amongst the Atlantic Ocean MUs. We then used an iterative leave-one-out approach to identify candidate loci for GSI. This approach identified loci that could assign individuals to natal ocean basins (i.e., to the Gulf Coast or to the Atlantic Ocean), and to individual MUs within the Atlantic Ocean, with high (≥90%) success and accuracy. Analyses of genome-wide SNPs refined our understanding of the magnitude and scale of population connectivity in loggerhead turtles in the southeastern United States, and provided a foundation for the development of SNP panels for accurate, fine-scale GSI in sea turtles.

Alabama, Florida, Georgia↗

USGS2024

A comparative framework to develop transferable species distribution models for animal telemetry data

Species distribution models (SDMs) have become increasingly popular for making ecological inferences, as well as predictions to inform conservation and management. In predictive modeling, practitioners often use correlative SDMs that only evaluate a single spatial scale and do not account for differences in life stages. These modeling decisions may limit the performance of SDMs beyond the study region or sampling period. Given the increasing desire to develop transferable SDMs, a robust framework is necessary that can account for known challenges of model transferability. Here, we propose a comparative framework to develop transferable SDMs, which was tested using satellite telemetry data from green turtles ( Chelonia mydas ). This framework is characterized by a set of steps comparing among different models based on (1) model algorithm (e.g., generalized linear model vs. Gaussian process regression) and formulation (e.g., correlative model vs. hybrid model), (2) spatial scale, and (3) accounting for life stage. SDMs were fitted as resource selection functions and trained on data from the Gulf of Mexico with bathymetric depth, net primary productivity, and sea surface temperature as covariates. Independent validation datasets from Brazil and Qatar were used to assess model transferability. A correlative SDM using a hierarchical Gaussian process regression (HGPR) algorithm exhibited greater transferability than a hybrid SDM using HGPR, as well as correlative and hybrid forms of hierarchical generalized linear models. Additionally, models that evaluated habitat selection at the finest spatial scale and that did not account for life stage proved to be the most transferable in this study. The comparative framework presented here may be applied to a variety of species, ecological datasets (e.g., presence-only, presence-absence, mark-recapture), and modeling frameworks (e.g., resource selection functions, step selection functions, occupancy models) to generate transferable predictions of species–habitat associations. We expect that SDM predictions resulting from this comparative framework will be more informative management tools and may be used to more accurately assess climate change impacts on a wide array of taxa.

Ecosphere↗

USGS2023

Monitoring population-level foraging distribution of a marine migratory species from land: Strengths and weaknesses of the isotopic approach on the Northwest Atlantic loggerhead turtle aggregation

Assessing the linkage between breeding and non-breeding areas has important implications for understanding the fundamental biology of and conserving animal species. This is a challenging task for marine species, and in sea turtles a combination of stable isotope analysis (SIA) and satellite telemetry has been increasingly used. The Northwest Atlantic (NWA) loggerhead ( Caretta caretta ) Regional Management Unit, one of the largest sea turtle populations in the world, provides an excellent opportunity to investigate key biological patterns as well as methodological aspects related to the use of stable isotopes to infer spatial distribution of turtles in foraging areas. We provide the first comprehensive assessment of the annual distribution of NWA adult female loggerheads among foraging areas and investigate the efficacy of various analytical approaches as well as the effect of sample size in these types of studies. A total of 5168 individual females were sampled from seven Management Units (MUs) between 2013-2018. We provide the first estimate of the proportion of females originating from each MU that uses each foraging area and show how this proportion varies over time. We also estimate the relative importance (in terms of number of turtles) of each foraging area to the overall loggerhead breeding aggregation nesting in Florida and in the NWA for each year of the study. The foraging area used by reproductively active females differs considerably across MUs. One of these, the Subtropical NWA, is by far the most important foraging area in terms of both number of individuals and genetic diversity, and therefore this region may be considered as a conservation priority. Through simulations, we show that limited sizes of sample groups (unknowns; training; priors) may result in false geographic differentiation and consequently mislead interpretations. We provide thresholds and methodological recommendations for future studies. This study establishes a fundamental baseline for monitoring the annual contribution of foraging area to a terrestrial-based breeding aggregation of a marine animal in a cost-effective way. This type of monitoring allows for early detection of changes in foraging distributions—a possible effect of climate change on marine ecosystems or of area-specific anthropogenic threats.

Frontiers in Marine Science↗