**1. Introduction**

Forest covers have been reduced drastically in the Peruvian Amazon region over the last few decades as a result of agricultural expansion and livestock activities, deforestation, mining, and urban expansion [1,2]. In Peru, 2,433,314 ha of Amazonian forests have been lost during 2001–2019 [3]. Although the tropical Amazon forest covers about 60% of Peru [4], it has now been highly fragmented because of the forest harvesting activities. The need for more agricultural land also promoted heavy migratory agricultural practices, [5]

**Citation:** Cotrina Sánchez, A.; Rojas Briceño, N.B.; Bandopadhyay, S.; Ghosh, S.; Torres Guzmán, C.; Oliva, M.; Guzman, B.K.; Salas López, R. Biogeographic Distribution of *Cedrela* spp. Genus in Peru Using MaxEnt Modeling: A Conservation and Restoration Approach. *Diversity* **2021**, *13*, 261. https://doi.org/10.3390/ d13060261

Academic Editors: Maurizio Rossetto and Michael Wink

Received: 1 May 2021 Accepted: 7 June 2021 Published: 10 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

eliminating approximately 0.5 ha of forest cover for crop production [6,7]. As a result of such growing land-use changes induced by migratory agriculture and cattle ranching, many native species, including genus *Cedrela*, are now experiencing massive destruction of their habitats [8]. In addition, the selective falling of trees, mainly of species having high economic values, has also caused the near extinction of many vegetation species such as mahogany (*Swietenia macrophylla*) and cedar (*Cedrela odorata*) [9].

*Cedrela* is a genus of tropical trees that includes species such as *C. odorata* L. and *C. fissilis* Vell., which had been collected for wood for more than 500 years in Central and South America, with *C. odorata* being the second most demanded tropical wood [10–13]. Worldwide, this genus has 17 recognized species [13,14], out of which Peru alone has 10. Hence, Peru can be considered as a center of diversity for *Cedrela* species [15], which currently includes three endemic species with restricted distribution, i.e., *C. molinensis*, *C. longipetiolulata*, and *C. weberbaueri* [16]. However, because of the high economic value of the genus *Cedrela* species, their usage had started increasing since the end of the 1980s, mainly in Mexico, Brazil, Peru, and Bolivia [17,18]. Such overexploitation eventually resulted in the near-extinction of the *Cedrela* population and made the international conservation community call for its greater protection under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). In Peru, the National Forest and Wildlife Service (SERFOR) has also recently incorporated the populations of genus *Cedrela* (*C. odorata, C. montana, C. fissilis, C. longipetiolulata, C. angustifolia, C. nebulosa, C. kuelapensis, C. Saltensis, C. weberbaueri,* and *C. molinensis*) in Appendix II of CITES on 28 August 2020.

This alarming situation indicates a strong need for further research studies that may effectively contribute to decision making related to the sustainability and conservation of biodiversity of the *Cedrela* and its habitat. Species distribution models (SDMs) are tools that combine species presence data with factors such as bioclimatic, edaphic, topographic, etc. and allow more effective and generous support for species conservation, biogeography, and climate change actions [19–23]. SDMs have made it possible to identify the distribution of timber forest species [24,25], other endemic species [26], wildlife [27,28], etc. on a regional scale facilitating proper identification, protection, and conservation of the endangered ones [29,30]. Among all the available SDMs, the maximum entropy algorithm (MaxEnt) [31] is one of the most widely used algorithms to find out the distribution of species under current and future conditions [32,33]. This way, MaxEnt allows habitat mapping and produces credible, defensible, and repeatable information, which contributes to a structured and transparent process of sustainable natural resources management by predicting the possible degradation of potential forest areas with species under risk in the future climate change scenarios [34].

After identifying the potential distribution areas of a species, the areas having the best aptitude to carry out reforestation or recovery initiatives of degraded areas are needed to be quantified and monitored properly. Such restoration is of great interest since 13.78% (177,592.82 km2) of the Peruvian territory has been identified as degraded areas as a consequence of deforestation, livestock activities, agriculture, mining, forest fires, etc. [35]. The strategies to be implemented must be oriented to the restoration and/or conservation of threatened species that are widely distributed over the geographic spaces integrated into the territorial order using environmental services, ecotourism, management of renewable resources, and productive practices promoted through Protected Natural Areas (PNAs) initiative [36].

The study has two main objectives—firstly, to model the biogeographic distribution of 10 available species of genus *Cedrela* (i.e., *C. odorata*, *C. montana*, *C. fissilis*, *C. longipetiolulata*, *C. angustifolia*, *C. nebulosa*, *C. kuelapensis*, *C. Saltensis*, *C. weberbaueri* and *C. molinensis*) over the Peruvian territory using the MaxEnt model in a current scenario, and secondly, to identify the locations of *Cedrela* within the designated conservation areas (to evaluate its effectiveness in conserving the species' habitat) and degraded areas (to implement forest restoration practices using these species). The study considered sample location

information of the *Cedrela* species (947 geographical records) and 33 different variables (19 bioclimatic variables, 3 topographic, 9 edaphic, solar radiation, and relative humidity).
