Geology topics

Christopher R. Sasso

Publications and source records attributed to Christopher R. Sasso.

3 recordsLinked to original sources

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

Satellite tracking reveals use of Biscayne National Park by sea turtles tagged in multiple locations

Although historical observations date back to the 1800’s, there is little information on sea turtle occupancy within Biscayne National Park (BNP). The park is located along the Florida reef tract and is dominated by the Gulfstream, which acts as a corridor for many marine animals. Here we used satellite telemetry to determine areas of use in BNP for two species of imperiled sea turtles, loggerhead ( Caretta caretta ) and green ( Chelonia mydas ) turtles. We included data for turtles tagged between 2009–2021 at sites both within park waters and in five locations outside the park boundary; individuals were captured both in the water and on land. We tagged 60 individuals (female, n = "> = 48; male, n = "> = 3; immature, n = "> = 9); loggerheads (n = "> = 33) ranged in size from 66.2 to 109.9 cm CCL (curved carapace length) and green turtles (n = "> = 27) ranged in size from 39.1 to 111.9 cm CCL. We used behavioral switching state-space modeling (SSM) to obtain daily predicted positions for each turtle, classified turtle behavior within the park as either foraging, migration, or both foraging and migration, and summarized high-use areas for each species across all months of the year. Turtles used park waters year-round, with concentrated use of deeper waters during seasonal migrations. Across all 60 turtles, 21 spent their tracking time foraging within BNP boundaries and 30 used the park as part of their migratory pathway; five turtles used the park for both foraging and migration, and the remaining four had SSM points very close to the park. Loggerhead migration occurred from February through November, whereas green turtle migration was concentrated in August. Both turtle species exhibited high overlap (i.e., usage) with seagrass habitat. These findings are relevant as managers consider strategies to minimize anthropogenic impacts to resident and migratory sea turtles using park waters.

Florida↗

USGS2022

The influence of satellite-derived environmental and oceanographic parameters on marine turtle time at surface in the Gulf of Mexico

The aftermath of the 2010 Deepwater Horizon oil spill highlighted the lack of baseline spatial, behavioral, and abundance data for many species, including imperiled marine turtles, across the Gulf of Mexico. The ecology of marine turtles is closely tied to their vertical movements within the water column and is therefore critical knowledge for resource management in a changing ocean. A more comprehensive understanding of diving behavior, specifically surface intervals, can improve the accuracy of density and abundance estimates by mitigating availability bias. Here, we focus on the proportion of time marine turtles spend at the top 2 m of the water column to coincide with depths where turtles are assumed visible to observers during aerial surveys. To better understand what environmental and oceanographic conditions influence time at surface, we analyzed dive and spatial data from 136 satellite tags attached to three species of threatened or endangered marine turtles across 10 years. We fit generalized additive models with 11 remotely sensed covariates, including sea surface temperature (SST), bathymetry, and salinity, to examine dive patterns. Additionally, the developed model is the first to explicitly examine the potential connection between turtle dive patterns and ocean frontal zones in the Gulf of Mexico. Our results show species-specific associations of environmental covariates related to increased time at surface, particularly for depth, salinity, and frontal features. We define seasonal and spatial variation in time-at-surface patterns in an effort to contribute to marine turtle density and abundance estimates. These estimates could then be utilized to generate correction factors for turtle detection availability during aerial surveys.

Gulf of Mexico↗