Forest fires are the result of poor land management and climate change. Depending on the type of the affected eco-system, they can cause significant biodiversity losses. This study was conducted in the Amazonas department in Peru. Binary data obtained from the MODIS satellite on the occurrence of fires between 2010 and 2022 were used to build the risk models. To avoid multicollinearity, 12 variables that trigger fires were selected (Pearson ≤ 0.90) and grouped into four factors: (i) topographic, (ii) social, (iii) climatic, and (iv) biological. The program Rstudio and three types of machine learning were applied: MaxENT, Support Vector Machine (SVM), and Random Forest (RF). The results show that the RF model has the highest accuracy (AUC = 0.91), followed by MaxENT (AUC = 0.87) and SVM (AUC = 0.84). In the fire risk map elaborated with the RF model, 38.8% of the Amazonas region possesses a very low risk of fire occurrence, and 21.8% represents very high-risk level zones. This research will allow decision-makers to improve forest management in the Amazon region and to prioritize prospective management strategies such as the installation of water reservoirs in areas with a very high-risk level zone. In addition, it can support awareness-raising actions among inhabitants in the areas at greatest risk so that they will be prepared to mitigate and control risk and generate solutions in the event of forest fires occurring under different scenarios.

This study examines the use of a spatial multi-criteria approach based on GIS and AHP techniques to model landslide risk in the Utcubamba river basin, Peru. The methodology consisted of selecting twelve triggering variables: slope angle, geology, precipitation, distance to faults, drainage density, TWI, relative relief, profile curve, land use, elevation, distance to roads, and distance to population centers. These variables were then analyzed using the AHP method and then integrated into a GIS environment, where the weighted linear combination (WLC) method was used to map landslide risk. The risk was categorized into five classes, ranging from very low (1) to very high (5). The main results indicate that 32.81% of the area analyzed in the Utcubamba river basin presents a high and very high risk of landslides. The high-risk areas are mainly located in the southern part of the basin and coincide with areas with steep slopes, high rainfall, and proximity to population centers or communication routes. The model generated was highly accurate (AUC of 0.82), confirming that the integration of the AHP method with GIS allows for the precise identification of critical areas, which is useful for territorial planning, the prioritization of interventions, and emergency management, making it a reliable and replicable methodology in other parts of Peru.

Dengue, a febrile disease that has caused epidemics and deaths in South America, especially Peru, is vectored by the Aedes aegypti mosquito. Despite the seriousness of dengue fever, and the expanding range of Ae. aegypti, future distributions of the vector and disease in the context of climate change have not yet been clearly determined. Expanding on previous findings, our study employed bioclimatic and topographic variables to model both the present and future distribution of the Ae. aegypti mosquito using the Maximum Entropy algorithm (MaxEnt). The results indicate that 10.23% (132,053.96 km²) and 23.65% (305,253.82 km²) of Peru’s surface area possess regions with high and moderate distribution probabilities, respectively, predominantly located in the departments of San Martín, Piura, Loreto, Lambayeque, Cajamarca, Amazonas, and Cusco. Moreover, based on projected future climate scenarios, it is anticipated that areas with a high probability of Ae. aegypti distribution will undergo expansion; specifically, the extent of these areas is estimated to increase by 4.47% and 2.99% by the years 2070 and 2100, respectively, under SSP2-4.5 in the HadGEM-GC31-LL model. Given the increasing dengue epidemic in Peru in recent years, our study seeks to identify tools for effectively addressing this pressing public health concern. Consequently, this research serves as a foundational framework for assessing areas with the highest likelihood of Ae. aegypti distribution in response to projected climate change in the second half of the 21st century.

This study represents the first comprehensive characterization of fish biodiversity in the Sonche Canyon (northeastern Peru) using environmental DNA (eDNA), revealing a community of 19 taxa whose detection depended critically on a multi-marker approach. The analysis demonstrated that the use of both Metafish and Mifish-U PCR primer pairs generated a high complementarity index (0.53), allowing the capture of divergent biological signals; Metafish was essential for identifying characiformes; while Mifish-U revealed the presence of specific gymnotiformes and loricariids. The findings, supported by rarification curves confirming sampling saturation. Principal Component Analysis (PCA) revealed co-variation between the biodiversity “hotspot” in Tingorbamba and elevated nitrate concentration (6.1 mg/L), as well as the ubiquitous presence of the invasive species Oncorhynchus mykiss. In conclusion, the utility of the molecular markers and the observed co-variation between physicochemical variables and species distributions underscore the need for standardized protocols for the monitoring and conservation of fauna in the vulnerable lotic systems of the Peruvian Andes.

Climate change is expected to reshape the climatic suitability of Neotropical plant taxa, particularly in topographically complex regions such as the tropical Andes, yet no nationwide assessment exists for the genus Clusia L. (Clusiaceae) in Peru. We modeled its current and future potential distribution using MaxEnt, based on 245 spatially thinned occurrence records (GBIF) and bioclimatic variables from WorldClim 2.1 selected through Pearson correlation and variance inflation factor filtering. Model complexity was tuned with ENMeval using spatial block cross-validation, and future projections were generated under the SSP2-4.5 and SSP5-8.5 scenarios for 2041–2060, 2061–2080, and 2081–2100 across three Global Climate Models. The model showed acceptable, stable performance (training AUC = 0.824, test AUC = 0.825), with elevation and precipitation seasonality (Bio15) as the leading predictors, together with Bio19 and Bio3 accounting for over 80% of the total contribution. Currently, only 16.61% of Peru is climatically suitable (medium–high) for the genus, concentrated discontinuously along Andean slopes and inter-Andean valleys. Both future scenarios project progressive contraction and fragmentation of suitable habitat, most severe under SSP5-8.5, where high-loss areas reach 50.43% of the evaluated territory by 2081–2100; these projections should be interpreted as plausible scenarios rather than validated forecasts, given the absence of independent data for future periods. Habitat gains were restricted to higher elevations, consistent with an altitudinal range shift. These results provide a coarse-scale spatial baseline that highlights broad patterns of climatic vulnerability for the genus, intended to guide exploratory conservation attention and future species-level research in the Andean region, rather than site-specific management decisions.

Climate change poses an increasing threat to crop suitability and food security, particularly for varieties of great cultural and economic importance, such as Peruvian starchy maize (Zea mays L.), whose optimal growing areas remain poorly characterized at the national level. This study presents the first comprehensive geospatial assessment of current and future land suitability for starchy corn in Peru, integrating bioclimatic, edaphic, and topographic variables using a machine learning approach. A dataset of georeferenced accessions, combined with pseudo-absence data, was used to train and validate a model using the Random Forest algorithm based on 29 bioclimatic, edaphic, and topographic predictors. The model demonstrated high statistical reliability (AUC-training = 0.98; AUC-validation = 0.99; precision = 0.971; recall = 0.962; F1 score = 0.966; accuracy = 0.966), with temperature-related variables (BIO09, BIO05, BIO15, and annual mean temperature) identified as the most important predictors. Projections made using the MIROC6 general circulation model under four shared socio-economic scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8. 5) for the periods 2061–2080 and 2081–2100 indicate a net expansion of highly suitable areas ranging from 20.76% to 45.46%, depending on the scenario. The most optimistic scenario (SSP1-2.6) projects the greatest gain (approximately 45% by 2070), while the most pessimistic (SSP3-7.0) shows an initial contraction followed by a marked recovery by 2100. These findings provide a robust geospatial framework for climate-smart agricultural planning, breeding programs, and sustainable land-use policies for Peruvian starchy maize in the context of future climate change.