Diversity

The Krabi mouth-brooding fighting fish, Betta simplex Kottelat, 1994, is a critically endangered and endemic fish species in Krabi province, Southern Thailand. Little information is available on its reproductive ecology and early developmental morphology, which are essential for studying its conservation. Generally, B. simplex is considered an adaptable animal that can tolerate lower alkalinity and higher hardness compared to its natural environment conditions. In this study, wild broodstocks of B. simplex were collected from the reported type localities and bred in captivity under laboratory conditions for size-series collection. Some biological aspects of B. simplex in its natural environmental conditions were determined. We found that its flaring and mating behavior was similar to those of bubble-nesting fighting fish but did not involve bubble-nest building. The fertilized eggs and pre-flexion larvae were nurtured in the mouth cavity of parental males within 11–12 (mode = 11) days after fertilization (DAF). The first-release offspring developed to the post-flexion stage with a body size of 4.39 ± 0.01 mm of standard length (SL; n = 6) and then to the juvenile stage within 30 days after release with 11.72 ± 0.62 mm SL (n = 4). Thus, we propose the following linear regression equation for growth prediction by age (DAF) and body size (SL; mm): age = 0.2425 SL + 1.7036 (r² = 0.9549). The findings of this study will deepen our knowledge of the reproduction and ontogeny of B. simplex and contribute to its future conservation and management.

The genus Pavona includes massive to submassive hermatypic corals and represents one of the main reef builders of the coral reefs in the Eastern Tropical Pacific (ETP). However, its development and specific ecological role, particularly on offshore reefs (e.g., oceanic Atolls), remain poorly understood. This study aims to determine the sclerochronological characteristics of the four Pavona species (Pavona duerdeni, Pavona clavus, Pavona maldivensis, and Pavona varians) in Clipperton Atoll, and their contributions to reef maintenance. Using the optical densitometry technique, Pavona annual growth parameters were obtained, showing that skeletal density (1.26 ± 0.23 g cm⁻³), extension rate (0.94 ± 0.31 cm year⁻¹), and calcification rate (1.17 ± 0.36 g cm⁻² year⁻¹) were consistent with previous data from the ETP. However, differences at the species level showed that P. duerdeni, P. varians, and P. maldivensis invested their calcification resources into building denser skeletons, demonstrating the morphological plasticity of the genus, likely driven by local factors, such as strong hydrodynamics and depth, rather than regional conditions (e.g., El Niño-Southern Oscillation events). Pavona’s growth strategies contribute to the preservation potential on a geological timescale of Clipperton Atoll, highlighting their importance as one of the main reef builders at a massive coral-dominated reef.

The re-identification of endangered felines is critical for species conservation and biodiversity assessment. This paper proposes the Pose-Guided Network with the Adaptive L2 Regularization (PGNet-AL2) framework to overcome key challenges in wild feline re-identification, such as extensive pose variations, small sample sizes, and inconsistent image quality. This framework employs a dual-branch architecture for multi-level feature extraction and incorporates an adaptive L2 regularization mechanism to optimize parameter learning, effectively mitigating overfitting in small-sample scenarios. Applying the proposed method to the Amur Tiger Re-identification in the Wild (ATRW) dataset, we achieve a mean Average Precision (mAP) of 91.3% in single-camera settings, outperforming the baseline PPbM-b (Pose Part-based Model) by 18.5 percentage points. To further evaluate its generalization, we apply it to a more challenging task, snow leopard re-identification, using a dataset of 388 infrared videos obtained from the Wildlife Conservation Society (WCS). Despite the poor quality of infrared videos, our method achieves a mAP of 94.5%. The consistent high performance on both the ATRW and snow leopard datasets collectively demonstrates the method’s strong generalization capability and practical utility.