Data Fast growing urban areas as drivers of sea turtle nest predation (The Leatherback Trust ©)

Description

With the constant urban growth, food subsidies production increases, consequently attracting opportunistic wildlife species. Food subsidies favor opportunistic species proliferation, resulting in strong competition and/or predation on other species. High availabilitity of these resources near protected areas, can threaten the target species conservation. In Costa Rica, within Parque Nacional Marino Las Baulas buffer zone, urban development has growth exponentially in recent years, resulting in Northern Raccon population growths. Raccoons started predation on endangered sea turtles within the National Park, predating 100% of all Olive Ridley (Lepidochelys olivacea) nest in a single season. Given these negative effects, we used camera traps to assess diferent trash containers in the buffer zone of the National Park and determine their effect on wildlife species attraction. Also, we assess the behaviour of these species in relation to trash collection dynamics to generate management recommendations. Our hipothesis was that depending on the desing/material of the trash container and trash dynamics, wildlife species can increase their access success and feed on Food Subsidies inside the containers. Also, due to the effects of urbanization and PAFS availability on wildlife species, negative effects due to predation are expected to be evidence in PNMB This data base contents the raw data on the observations made with camera traps during the study period, and nest predation-urban growth data in in Parque Nacional Marino Las Baulas.

Steps to reproduce

To identify and characterize opportunistic predator behavior, we deployed camera traps in 24 distinct garbage containers, between July and October 2021. Each camera trap was attached to a tree at a height of ~40 cm pointing toward the garbage container. Camera traps were set to be active via passive infrared sensor and video mode, with a minimum delay of 30 seconds between consecutive triggers. Camera traps were only set to be active at night (between 17:00-06:00). After one week of data collection around a particular container, the camera trap was moved to a different location. We identified species that interacted with garbage and the times during the night when they were actively foraging. We estimated access success to the container by dividing the number of times the animal successfully entered the garbage container by the total number of attempts it made to get in or if they removed garbage from the containers. We considered each video as one detection, independently of the group size. To ensure that each video corresponded to a different event, we considered triggers within 60 s of each other as one detection, following Kays et al. (2020). We assessed the influence of location, day of the week, and type of container on group size (number of individuals of a species per group) and number of independent detections per night. To assess the influence of location, day of the week, and type of container on the trash access success for all opportunistic species, we used a GLM with binomial error distribution. Success was treated as binary data (success/no success). I All statistical tests were conducted with an α = 0.05 in R Statistical Software (v4.3.2; R Core Team, 2023). Urban growth analysis To assess the increase in urban development, we used satellite imagery of Landsat (5, 7, 8, 9) and Sentinel 2 collections. Satellite images were downloaded from Google Earth Engine and retrieved from January 1990 to May 2024. Only images with low cloud cover were selected. We accounted for the total number of building units per year (houses, hotels, restaurants, and other commercial buildings properties) within the National Park and the buffer area. High-definition satellite imagery from Maxar technologies (Maxar, 2024) was used as control point images in 2003, 2005, 2010, 2016, 2019, 2021, 2022, and 2023, to verify that building unit counting was correct. In-situ nest predation Night patrols were conducted between 2011/12 and 2018/19 nesting seasons. After collecting data on nesting olive ridley turtles, their nest position was marked by triangulation to monitor them throughout the incubation period. The percentage of predated in situ nests was estimated by dividing the number of predated marked nests by the total number of marked nests per season. We estimated the percentage of relocated nests to the hatchery per season by dividing the number of relocated nests by the total number of identified body pits on the beach when the hatchery was in operation

Categories

Funding

Licence