Journal of Zoological and Botanical Gardens

Species of the genus Opuntia exhibit considerable variation in chromosome number and ploidy level, making cytogenetic information essential for studies on evolution, taxonomy, and genetic improvement. However, cytogenetic information on several species of this genus remains limited. Therefore, the objectives of this study were to determine chromosome numbers and ploidy levels in seven Opuntia species and to characterize the karyotype of Opuntia microdasys as a representative diploid species. A total of 69 accessions representing seven species were collected from wild and cultivated populations in southeastern Coahuila, Mexico. Somatic cells obtained from root apices developed from cladodes were used to analyze chromosome numbers. Cytological preparations included determination of mitotic activity, pretreatment with p-dichlorobenzene, enzymatic digestion with pectolyase and cellulase, and acetocarmine staining, followed by chromosome counting and karyotype analysis of O. microdasys. The highest mitotic activity was recorded between 10:00 and 11:00 h. Chromosome counts revealed diploid (2n = 22), tetraploid (2n = 44), hexaploid (2n = 66), and octoploid (2n = 88) cytotypes. Notably, O. engelmannii exhibited intraspecific cytotypic variation, with both hexaploid and octoploid populations. The karyotype of O. microdasys was highly symmetrical, with a karyotype formula of 10m + 1sm, small chromosomes, and low asymmetry indices (A1 = 0.15; A2 = 0.106). These findings reveal substantial ploidy diversity among the seven Opuntia species analyzed and provide cytogenetic information relevant to the characterization, management, and conservation of Opuntia genetic resources, including their potential use in ex situ conservation and botanical collections.

Behavioral observation is central to animal welfare assessment in zoological institutions, but manual observation is time-consuming and difficult to apply continuously. We developed and applied a LabGym-based workflow for automated behavioral monitoring of captive lions (Panthera leo). Development footage from three Danish zoological institutions was used to train a lion detector and a seven-class behavior categorizer; video collected on 22–28 December 2025 was temporally held out from model development and used for applied validation. Detector performance was moderate (bounding-box AP 47.1%; segmentation AP 41.6%), while the internal categorizer validation reached 0.62 accuracy and weighted F1. In the held-out validation, 52.6% of manually annotated lion-time received a retained pipeline prediction. Fine-scale end-to-end accuracy was 0.231 and increased to 0.440 when calculated only on covered lion-time. Grouping behaviors as active, inactive/maintenance, and pacing-like locomotion increased these values to 0.317 and 0.602, respectively. Coverage varied substantially among institutions (33.8–59.8%) and was particularly low for cubs and nighttime footage. Although model macro-F1 exceeded simple majority-class baselines, end-to-end accuracy did not. The workflow demonstrates the potential of computer-vision approaches for scalable behavioral quantification and future welfare assessment in zoological settings, while highlighting the importance of accounting for prediction coverage and classification performance when interpreting automated behavioral data.

Against the backdrop of the coordinated development of biodiversity conservation, the preservation of ethnic cultures, and public environmental education, the traditional environmental education model in national forest parks—which primarily focuses on showcasing natural landscapes and one-way knowledge dissemination—struggles to fully address the practical needs of dynamic ecosystem conservation and the sustainable preservation of local cultures. This paper focuses on the objectives, content, and implementation mechanisms of environmental education in national forest parks that integrate ethnic cultures. It analyzes the intrinsic connections among digital technology, community collaboration, traditional ecological knowledge, and hands-on environmental education and constructs a “Hub” model centered on the integration of digital knowledge and collaborative governance. This model employs digital technology, community collaboration, and hands-on environmental education as its three primary implementation pathways. Through a cyclical mechanism of “knowledge collection—digital integration—community validation—educational transformation—ecological practice—feedback optimization,” it connects ethnic cultural knowledge, modern ecological science, and public participatory conservation actions. In this model, digital technology primarily serves to record, integrate, and disseminate ecological data and ethnic cultural archives; community collaboration emphasizes the leading role of ethnic communities in knowledge interpretation, curriculum design, ecological practices, and conservation decision-making; and environmental field education promotes the transformation of learners from ecological awareness and cultural identity toward environmental responsibility and conservation behavior through nature observation, cultural experiences, ecological field studies, and community participation. This paper argues that this model can drive the transformation of environmental education in national forest parks from static displays and one-way communication toward knowledge co-creation, scenario-based learning, and adaptive governance, thereby providing a theoretically grounded framework and practical pathway—tailored to local contexts—for achieving the coordinated development of biodiversity conservation, the preservation of ethnic cultural heritage, and the enhancement of public ecological literacy.