The increasing demand for tropical timber from natural forests has reduced the population sizes of native species such as Cedrela spp. because of their high economic value. To prevent the decline of population sizes of the species, all Cedrela species have been incorporated into Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). The study presents information about the modeled distribution of the genus Cedrela in Peru that aims to identify potential habitat distribution of the genus, its availability in areas protected by national service of protected areas, and highlighted some areas because of their conservation relevance and the potential need for restoration. We modeled the distribution of the genus Cedrela in Peru using 947 occurrence records that included 10 species (C. odorata, C. montana, C. fissilis, C. longipetiolulata, C. angustifolia, C. nebulosa, C. kuelapensis, C. saltensis, C. weberbaueri, and C. molinensis). We aim to identify areas environmentally suitable for the occurrence of Cedrela that are legally protected by the National Service of Protected Areas (PAs) and those that are ideal for research and restoration projects. We used various environmental variables (19 bioclimatic variables, 3 topographic factors, 9 edaphic factors, solar radiation, and relative humidity) and the maximum entropy model (MaxEnt) to predict the probability of occurrence. We observed that 6.7% (86,916.2 km²) of Peru presents a high distribution probability of occurrence of Cedrela, distributed in 17 departments, with 4.4% (10,171.03 km²) of the area protected by PAs mainly under the category of protection forests. Another 11.65% (21,345.16 km²) of distribution covers areas highly prone to degradation, distributed mainly in the departments Ucayali, Loreto, and Madre de Dios, and needs immediate attention for its protection and restoration. We believe that the study will contribute significantly to conserve Cedrela and other endangered species, as well as to promote the sustainable use and management of timber species as a whole.

The spectacled, or Andean, bear (Tremarctos ornatus) is classified as vulnerable by the IUCN due to climate change and human-induced habitat fragmentation. There is an urgent need for the conservation of spectacled bear at real time. However, the lack of knowledge about the distribution of this species is considered as one of the major limitations for decision-making and sustainable conservation. In this study, 92 geo-referenced records of the spectacled bear, 12 environmental variables and the MaxEnt entropy modelling have been used for predictive modelling for the current and future (2050 and 2070) potential distribution of the spectacled bear in Amazonas, northeastern Peru. The areas of “high”, “moderate” and “low” potential habitat under current conditions cover 1.99% (836.22 km²), 14.46% (6081.88 km²) and 20.73% (8718.98 km²) of the Amazon, respectively. “High” potential habitat will increase under all climate change scenarios, while “moderate” and “low” potential habitat, as well as total habitat, will decrease over the time. The “moderate”, “low” and total potential habitat are distributed mainly in Yunga montane forest, combined grasslands/rangelands and secondary vegetation and Yunga altimontane (rain) forest, while “high” potential habitat is also concentrated in the Jalca. The overall outcome showed that the most of the important habitats of the spectacled bear are not part of the protected natural areas of Amazonas, under current as well as under future scenarios.

Aim: 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.

En la presente investigación se ha evaluado la calidad del agua de la cuenca del río Utcubamba utilizando diatomeas (Bacillariophyceae) como parámetro biológico. Para ello, se realizó dos muestreos, en época seca (octubre 2014) y húmeda (marzo 2015), en 43 estaciones ubicadas a lo largo de la cuenca del río, situadas entre 386 y 2 834 msnm. Se dividió la cuenca en alta, media y baja según el piso altitudinal. Se usó como herramienta principal el índice de diversidad de Shannon y Wienner (H') para diatomeas. En la época seca, se encontró niveles de contaminación moderada para el tramo alto de la cuenca y contaminación leve en los tramos medio y bajo, con valores entre 1 981 y 2 533 bits cel -1. Para la época húmeda se registraron los mismos datos interpretativos, pero con valores entre 1 890 y 2 004 bits cel -1 .