Examinando por Autor "Cotrina Sanchez, Alexander"

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Agro-environmental vulnerability and ecosystem sustainability in Peruvian family farming: integrating survey data, spatial modeling, and remote sensing
(MDPI (Multidisciplinary Digital Publishing Institute), 2026-01-30) Pizarro, Samuel; Ccopi Trucios, Dennis; Otoya Barrenechea, José; Romero Vasquez, Juan; Tolentino Soriano, María; Cotrina Sanchez, Alexander; Barboza, Elgar
Subsistence family farming in Peru is increasingly constrained by ecosystem degradation, climate variability, and limited access to productive services, particularly where environmental exposure is high. This study develops an Agro-productive and Territorial Vulnerability Index (IVAPT) to evaluate environmental, ecosystem, and socioeconomic vulnerability of subsistence agriculture at the district level nationwide. The index integrates district-level agricultural survey data (ENA-2024) with multi-temporal MODIS NDVI series (2000–2024) and comprehensive climatic, topographic, land-cover, and accessibility indicators, processed through multivariate statistics. Three objective weighting schemes (ENTROPY, CRITIC, PCA) construct thematic sub-indices of Environmental Exposure (EnvExp), Ecosystem Condition (EcoCond), and Socioeconomic Capacity (SocioCap). Results show more than half of Peru's 1552 districts fall within moderate to very high vulnerability, with highest concentration in the Amazon region (Loreto, Ucayali, Madre de Dios), Andean-Amazonian transitions, and highland districts (Huancavelica, Apurímac, Ayacucho, Puno) where biophysical constraints, ecosystem pressure, and socioeconomic isolation converge. Dimensional spatial complementarity EnvExp peaking on coast, EcoCond in Amazon, SocioCap in Andes demonstrates effective vulnerability reduction requires dimension-specific interventions. Despite divergent weighting schemes, spatial patterns remained consistent, validating identified hotspots. IVAPT provides a reproducible framework supporting evidence-based territorial planning and targeted investments in water infrastructure, ecosystem restoration, and climate adaptation.
An ecological modelling approach to support Peru wildlife conservation planning based on geospatial datasets and remote sensing information
(John Wiley & Sons Ltd., 2026-05-05) Cotrina Sanchez, Alexander; Rojas Briceño, Nilton; Guzman Valqui, Betty Karina; Valentini, Riccardo; Vaglio Laurin, Gaia
Peru, a megadiverse country, has developed conservation plans for some threatened wildlife species. This study produced spatially explicit data integrating Species Distribution Models (SDMs) into a geospatial analysis of connectivity within the protected areas (PAs) network. In addition, a deforestation analysis around selected PAs was performed evaluating the related conservation implications. The use of lidar-derived vegetation vertical structure metrics from the spaceborne Global Ecosystem Dynamics Investigation (GEDI) mission was tested as an innovative data source to support ecological modelling. This country-level analysis is a useful approach to support conservation in high-biodiversity areas. Location: Peru. Methods: Occurrence data of seven threatened wildlife species were used to compute SDMs in MaxEnt using three variable sets: (i) bioclimatic and topographic, (ii) GEDI vegetation structure metrics joined with Normalized Difference Vegetation Index (NDVI), and (iii) a combination of both. MaxEnt was explicitly calibrated by testing 126 candidate models per species across feature-class and regularization multiplier combinations. SDMs combined with auxiliary data were used to identify core areas, then connected through main ecological corridors (ECs) using geospatial analysis. Deforestation rates were computed in the buffer zones (BZ) of Protected Natural Areas (PNAs) identified as core areas. GEDI lidar-derived data were also used to compare forest degradation between two PNAs and their BZ. Results: This ecological modelling effort identified several core conservation areas, as well as the main ecological corridors interconnecting them. The study showed that highly suitable habitats are currently poorly represented by the present Peru protected areas network, particularly for primates. Test Area Under Curve (AUC) values ranged from 0.867 to 0.995; the Biotopveg set, integrating bioclimatic, topographic, GEDI, and NDVI variables was optimal for three species and the bioclimatic-topographic set for four, suggesting a species-specific contribution of vegetation structural data. GEDI data were used to detect forest degradation gradients, in accordance with known anthropogenic impacts. Deforestation analysis showed that even if indirect use protected areas resulted in less affected by deforestation in their surroundings, notable exceptions occur, calling for additional measures to support human-wildlife coexistence. Main Conclusions: Ecological modelling based on SDMs and spatial analyses can support species conservation plans and landscape connectivity at broader planning scales. GEDI provides valuable data as input in SDMs and supports detecting forest degradation.
Integrating agroecological suitability of cacao (Theobroma cacao L.) with biodiversity and land-use constraints in Peru
(Elsevier Ltd., 2026-01-29) Cotrina Sanchez, Alexander; Guzman Valque, Betty Karina; Barboza, Elgar; Oliva, Manuel; Huaman Pilco, Angel Fernando; Rojas Briceño, Nilton B.
CONTEXT: Cacao cultivation is vital for rural economies in Peru, but its expansion often overlaps with sensitive ecosystems, raising concerns for biodiversity conservation. Despite international commitments to deforestation-free supply chains, integrated analyses combining agroecological suitability with land-use constraints remain scarce in Peru. OBJECTIVES: This study aims to identify suitable areas for cacao cultivation under multiple exclusion scenarios, evaluate conflicts with biodiversity and conservation areas, and quantify degraded lands that could provide opportunities for agroforestry-based restoration. METHODS: Cacao suitability was modelled with an ensemble of nine machine-learning algorithms using bioclimatic, edaphic, and topographic predictors. Outputs were filtered to exclude biophysical barriers and overlaid with national-scale layers of species richness, protected areas, forest cover, and degraded lands through GIS-based spatial analysis to evaluate exclusion scenarios and trade-offs. RESULTS AND CONCLUSIONS: The ensemble achieved high predictive power, with Random Forest (AUC = 0.997) and XGBoost (AUC = 0.972) performing best. Highly suitable areas were concentrated in the Andean-Amazon transition, especially in San Martín, Cusco, Huánuco, and Junín departments, where they overlapped with biodiversity hotspots and legally protected areas. Degraded yet suitable lands highlighted opportunities to expand cacao through agroforestry systems, reducing forest pressure and enhancing ecological restoration. SIGNIFICANCE: By integrating suitability modelling with national-scale geospatial layers, this study delivers a framework linking crop suitability with land-use constraints. The findings support national-scale planning while remaining adaptable to local contexts. They also align with international policy frameworks such as the European Deforestation Regulation (EUDR), promoting sustainable cacao production, biodiversity conservation, and long-term rural development in Peru.
Mapping current and future coffee suitability in Peru under climate change: implications for restoration and deforestation-free development
(Frontiers Media S.A, 2026-04-20) Zabaleta Santisteban, Jhon A.; Rojas Briceño, Nilton B.; Silva López, Jhonsy O.; Medina Medina, Angel J.; Tuesta Trauco, Katerin M.; Rivera Fernandez, Abner S.; Silva Melendez, Teodoro B.; Grandez Alberca, Marlen A.; Puscan Rojas, Julio; Salas López, Rolando; Oliva Cruz, Manuel; Cotrina Sanchez, Alexander; Gómez Fernández, Darwin; Barboza, Elgar
Coffee cultivation is central to rural livelihoods and Andean–Amazonian landscapes in Peru; however, it faces increasing pressure from climate change and land-use restrictions. This study aimed to assess the current and future ecological suitability of Coffea arabica at the national scale. A Maximum Entropy (MaxEnt) modeling framework was applied, integrating high-resolution bioclimatic, topographic, and edaphic variables. Model performance was robust (mean AUC = 0.858), and variable importance was evaluated using jackknife tests and contribution metrics. Elevation, precipitation of the driest quarter (bio17), soil nitrogen content, and bulk density were identified as the main determinants of habitat suitability. Under current climatic conditions, highly suitable areas cover 42,322.95 km2 (3.3% of Peru), mainly along the eastern Andean slopes. Spatial exclusion scenarios revealed a pronounced funnel effect in effective land availability, with reductions exceeding 80% when forest-cover constraints were applied. Approximately 39.8% of highly suitable areas overlap with degraded lands, highlighting opportunities for productive restoration through agroforestry systems. Future projections under SSP1–2.6 to SSP5–8.5 scenarios indicate consistent contractions of highly suitable areas (–23% to –42%) and an upslope shift toward higher elevations, while unsuitable areas expand by 4%–5% nationally. These findings provide spatially explicit evidence to support climate-smart territorial planning, restoration prioritization, and sustainable coffee development under accelerating climate change.
Modeling the current and future habitat suitability of Neltuma pallida in the dry forest of northern Peru under climate change scenarios to 2100
(John Wiley & Sons Inc., 2024-08-27) Barboza Castillo, Elgar; Bravo Morales, Nino; Cotrina Sanchez, Alexander; Salazar Coronel, Wilian; Gálvez Paucar, David; Gonzales, Jhony; Saravia Navarro, David; Valqui Valqui, Lamberto; Cárdenas Rengifo, Gloria Patricia; Ocaña Reyes, Jimmy Alcides; Cruz Luis, Juancarlos; Arbizu Berrocal, Carlos Irvin
The development of anthropic activities and climate change effects impact worldwide species' ecosystems and habitats. Habitats' adequate prediction can be an important tool to assess current and future trends. In addition, it allows strategies development for their conservation. The Neltuma pallida of the forest region in northern Peru, although very significant, has experienced a decline in recent years. The objective of this research is to evaluate the current and future distribution and conservation status of N. pallida in the Peruvian dry forest under climate change (Location: Republic of Peru). A total of 132 forest presence records and 10 variables (bioclimatic, topographic, and soil) were processed and selected to obtain the current and future distribution for 2100, using Google Earth Engine (GEE), RStudio, and MaxEnt. The area under the curve values fell within the range of 0.93–0.95, demonstrating a strong predictive capability for both present and future potential habitats. The findings indicated that the likely range of habitats for N. pallida was shaped by factors such as the average temperature of wettest quarter, maximum temperature of warmest month, elevation, rainfall, and precipitation of driest month. The main suitable areas were in the central regions of the geographical departments of Tumbes, Piura, and Lambayeque, as well as in the northern part of La Libertad. It is critical to determine the habitat suitability of plant species for conservation managers since this information stimulates the development of policies that favor sustainable use programs. In addition, these results can contribute significantly to identify new areas for designing strategies for populations conserving and recovering with an ecological restoration approach.