http://hdl.handle.net/11606/1 Publicaciones científicas sobre investigaciones hechas en el Área de Conservación Guanacaste Sun, 26 Jul 2026 16:19:58 GMT 2026-07-26T16:19:58Z http://hdl.handle.net/11606/2495 Temperature, precipitation and soil characteristics of Volcan Cacao - Area de Conservacion Guanacaste (ACG), Costa Rica Smith, Alexander I have monitored temperature and precipitation at eight locations across a 1500m elevation gradient along Volcan Cacao in the Area de Conservacion Guanacaste (ACG) in northwestern Costa Rica since 2013 (January 2026 inclusive). This deposition includes air temperature data collected between March 2013 and September 2023. Air temperatures were recorded using either a HOBO RG3M Temperature and Rain Gauge Data Logger or an Onset HOBO UA-001-64 Pendant Temperature Data Logger or an Onset HOBO MX2202 Pendant Wireless Temperature Data Logger that recorded temperature every15 minutes each day. Datalogger is approximately 1.5 m above the ground. This deposition includes precipitation data collected between March 2013 and January 2026 as recorded using a HOBO RG3M Temperature and Rain Gauge Data Logger where each event logged was 0.2 ml. The deposition includes soil temperature data collected between June 2021 and January 2026. Temperature was recorded using an Onset HOBO MX2202 Pendant Wireless.Temperature Data Logger that recorded temperature every15 minutes each day. Datalogger is buried approximately 10-15 cm below the surface. This deposition includes measurements of soil chemistry as recorded in the field by an HH2 Moisture Meter Wet Sensor (Wet-2) (Delta T Devices, Cambridge, England) in February and August 2014. Inorganic elemental analysis was completed at the University of Guelph Laboratory Services by inductively coupled plasma optical emission spectrometry (ICP-OES) for: Calcium, Magnesium, Phosphorous, Potassium, Sodium, Sulphur, and Iron. A second analysis of elements in soil was conducted at SGS Argifood Laboratories (ug/g) by ICP using ammonium acetate extraction for P, K, Mg and Na. (2026-02-18) Mon, 01 Jun 2026 00:00:00 GMT http://hdl.handle.net/11606/2495 2026-06-01T00:00:00Z http://hdl.handle.net/11606/2494 Improving the statistical reporting of hatching success data: the case of sea turtles Santidrián Tomillo, Pilar; Martínez-Abraín, Alejandro; Valverde, Verónica; Spotila, James R.; Paladino, Frank V. Estimating hatching success of egg clutches is essential for quantifying reproductive success in sea turtles. Thus, proper reporting is necessary to provide meaningful information for knowledge acquisition and management. Here we review how hatching success has been reported in the scientific literature and use our own multi-annual multi-species datasets to explore the best ways for describing hatching success data. Despite non normality, the central tendency of hatching success data was most often described using arithmetic means. Only 17 out of 203 (8%) studies reported the median, compared to 192 (95%) that reported the mean (6 studies reported both). In 24% of studies, a dispersion metric was not provided. In our comparison, the arithmetic mean was only a good predictor of central tendency in leatherback turtles (Dermochelys coriacea), with the median (0.45) being only slightly above the mean (0.43). In leatherbacks, hatching success was characterized by high variability, and not by a consistently low hatching success, as indicated by the low skewness and large spread of data. On the contrary, hatching success data were strongly skewed and skewed toward high values in green turtles (Chelonia mydas) (25% and 75% percentiles: 0.88 and 0.98) and olive ridley turtles (Lepidochelys olivacea) (25% and 75% percentiles: 0.75 and 0.97) respectively, with presence of outliers in both cases. Basic statistics, appropriate for characterizing non-normal distributions such as the median, skewness or kurtosis, together with boxplots, provided accurate description of hatching success data. Using these straightforward statistics would greatly improve the ecological understanding of hatching success in sea turtles. Tue, 01 Jul 2025 00:00:00 GMT http://hdl.handle.net/11606/2494 2025-07-01T00:00:00Z http://hdl.handle.net/11606/2493 Zero‐shot shark tracking and biometrics from aerial imagery Lalgudi, Chinmay K.; Leone, Mark E.; Clark, Jaden V.; Madrigal‐Mora, Sergio; Espinoza, Mario he recent widespread adoption of drones for studying marine animals provides opportunities for deriving biological information from aerial imagery. The large scale of imagery data acquired from drones is well suited for machine learning (ML) analysis. Development of ML models for analysing marine animal aerial im- agery has followed the classical paradigm of training, testing and deploying a new model for each dataset, requiring significant time, human effort and ML expertise. 2. We introduce Frame-­ Level Alignment and Tracking (FLAIR), which leverages the video understanding of Segment Anything Model 2 (SAM 2) and the vision-language capabilities of Contrastive Language-­ Image Pre-­ training (CLIP). FLAIR takes a drone video as input and outputs segmentation masks of the species of interest across the video. Notably, FLAIR leverages a zero-­ shot approach, elimi- nating the need for labelled data, training a new model or fine-­ tuning an existing model to generalize to other species. 3. We trained state-­ of-­ the-­ art object detection and instance segmentation models on a new dataset of Pacific nurse sharks. We show that FLAIR massively outper- forms these methods and performs competitively against two human-­ in-­ the-­ loop approaches for prompting SAM 2, achieving a Dice score of 0.8. FLAIR readily generalizes to other shark species without additional human effort and can be combined with custom heuristics to automatically extract relevant information including length and tailbeat frequency. 4. FLAIR has significant potential to accelerate aerial imagery analyses, requir- ing markedly less human effort and expertise than traditional machine learning workflows, while achieving superior accuracy and generalization performance. By reducing the effort required for aerial imagery analysis, FLAIR allows scien- tists to spend more time interpreting results and deriving insights about marine ecosystems. Mon, 01 Sep 2025 00:00:00 GMT http://hdl.handle.net/11606/2493 2025-09-01T00:00:00Z http://hdl.handle.net/11606/2492 Mass stranding of Pelagic Seasnakes, <i>Hydrophis platurus</i> (Squamata: Hydrophiidae),in Guanacaste, Costa Rica Solórzano, Alejandro; Sasa, Mahmood Wed, 22 Jan 2025 00:00:00 GMT http://hdl.handle.net/11606/2492 2025-01-22T00:00:00Z