Natural resources conservation

Uganda which constitutes one of the world's most fragile and conflict affected regions. This paper develops an integrated conflict systems framework that maps cascading interactions among six drivers of regional fragility: climate stress, governance fragility, resource competition, hydro-politics, recognition politics, and external intervention. Using comparative case study methodology across Somalia, Ethiopia, Sudan, and Eritrea as primary cases, the analysis traces six cascading pathways linking climate induced livelihood collapse to resource competition, governance overload, hydro-political failure, recognition disputes, and external intervention. An expanded hydro-politics analysis examines the Grand Ethiopian Renaissance Dam dispute, Somalia's dependence on the Shabelle and Jubba rivers, the absence of Ethiopia Somalia water agreements, the Baardhere Dam dispute, and the 2021 ICJ Somalia Kenya maritime ruling within the framework of international water law. The paper introduces an original composite Horn of Africa Fragility Index and concludes with policy recommendations addressing governance, climate adaptation, transboundary water cooperation, sovereignty disputes, and the management of external intervention.

This paper contextualizes the economic environment of the productive sectors that depend on the intensive use of water resources in the Colombian Amazon basin (it is composed of the departments of Amazonas, Caquetá, Guainía, Guaviare, Putumayo, Vaupes, La Bota Caucana and southern Nariño) through the collection and organization of information from official entities and consultations in the region carried out by the Amazon Scientific Research Institute (ASRI). Macroeconomic indicators of the Amazonian departments in each of the different sectors were analyzed considering the added value exposed in the annual average of the Gross Domestic Product (2000–2012 in constant values) and contrasted with the sectoral demand for water and the growth projection, both of the human population and the growth trends of the economic activities. As a relevant result, it was found that the departments with a mining-energy tradition base their economic growth on the intensive use of water (greater pres...

The recent progress in data science, along with the transformation in digital and satellite technology, has enhanced the capacity for artificial intelligence (AI) applications in the forestry and wildlife domains. Nevertheless, the swift proliferation of developmental projects, agricultural, and urban areas pose a significant threat to biodiversity on a global scale. Hence, the integration of emerging technologies such as AI in the fields of forests and biodiversity might facilitate the efficient surveillance, administration, and preservation of biodiversity and forest resources. The objective of this paper is to present a comprehensive review of how AI and machine learning (ML) algorithms are utilized in the forestry sector and biodiversity conservation worldwide. Furthermore, this research examines the difficulties encountered while implementing AI technology in the fields of forestry and biodiversity. Enhancing the availability of extensive data pertaining to forests and biodiver...

The Epitonium Supplement 1
Occasional Papers of The Houston Conchology Society.

There are numerous papers and reports on invasive Applesnails in Texas spanning 20 years. This history has been wrought with much confusion as to the species identification. Now, with genomic cladistics analysis (Hayes et al. 2012) the species identity has been determined, and the parameters narrowed to one species of invasive Applesnail in Texas (excluding Pomacea bridgesii), Pomacea maculata, which is a species of South American origin.

This paper seeks to provide a succinct rendering of relevant data from those papers. Historical background, general educational and other extraneous data deemed relevant to the regional reporting is included herein. Major portions of this paper are transcribed directly from “Gonzales 2010”.

Food and feeding habits of the three tuna species landed in Ghana were investigated for a period of six (6) months from October 2019 to March 2020. The species are Thunnus albacares, Katsuwonus pelamis, and Thunnus obesus. The study sampled a total of 210 individuals of the three tuna species, out of which 90 individuals were Katsuwonus pelamis, 60 individuals of Thunnus albacares, and 60 individuals of Thunnus obesus. The study sampled 90 specimens for Katsuwonus pelamis because they are of high abundance and very common in Ghanaian waters than Thunnus albacares and Thunnus obesus. These tuna species were sampled for stomach content analysis, to determine which type of prey item is most important and preferred as food for these three tuna species. The samples were transported to the Fisheries Scientific Survey Division (FSSD) laboratory in Tema, under the Ministry of Fisheries and Aquaculture Development for analysis. At the laboratory, some morphometric measurements of the samples were taken. The fish samples were dissected and their guts were removed for examination. Gut content analysis was conducted on each of the fish sampled. The gut fullness was determined on a scale of 0/4 to 4/4 by visual inspection. The commonest prey items discovered in the stomachs during the studies were Engraulis encrasicolus, Exocoetus spp, Penaeus kerathurus, Sepia hierredda, Loligo spp, and Metapenaeus monoceros. Engraulis encrasicolus was found to be the most preferred food item accounting for 58% occurrence by number, with Diplodus spp being the least preferred food item accounting for 2% for Thunnus albacares. For Katsuwonus pelamis, Penaeus kerathurus was foundto be the most preferred food item accounting for 57% occurrence by number, with Sepia hierredda being the least preferred food item accounting for 10% of total prey items found. Exocoetus spp was also found to be the most preferred food item accounting for 86% occurrence by number, with Metapenaeus monoceros being the least preferred food item accounting for 12% for Thunnus obesus. Similarly, the most important food items identified using Index of relative importance were Engraulis encrasicolus, Penaeus kerathurus and Exocoetus spp with Diplodus spp, Sepia hierredda and Metapenaeus monoceros recording the least important for Thunnus albacares, Katsuwonus pelamis and Thunnus obesus respectively. There was a significant difference among these three tuna species with respect to Engraulis encrasicolus, Penaeus kerathurus Exocoetus spp, Sardinella aurita, Caranx crysos, Sepia hierredda, Loligo spp, and Brachyuran spp. Continuous research on food and feeding habits of these Tuna species to cover all the yearly seasons was recommended owing to their commercial importance to Ghana and the international community.

The analytical techniques, machine learning, and big data technologies in data sciences have proven many paths to improve environmental monitoring more than ever. Data becomes a must these days because critical environmental problems like climate change, pollution, and resource depletion have made it essential. One can understand the intricate ways the environment functions and make intelligent decisions through data. Huge data streams from different sources-satellite imagery, weather patterns, sensors, social media, etc.-would make it possible for data scientists to analyze patterns in trends and models that show how humans impact ecosystems. Such predictive and analytic process across geospatial data examines and identifies environmental risks, often through a machine learning algorithm for the purposes of more effective and sustainable practices in industry-typically agriculture and energy, and waste management-along with optimizing management of resources. True also, by quickly sensing environmental changes that could impact conservation efforts and disasters, rapid detection and responses can occur through real-time monitor systems using IoT (Internet of Things) devices and sensor networks. In truth, therefore, one of the contributions of Data Science to environmental sustainability is that it exposes environmental states as well as facilitates viable policies, business strategies, and community-based approaches toward data-informed decisions that would further reduce ecological footprints, promote conservation, and develop resilience in the ecosystems over time. Apparently, as environmental data will continue increasing in volume, as well as in diversity, so will the integration of data science into environmental monitoring play transformative roles in achieving sustainability goals for the global population.