**2. Materials and Methods**

### *2.1. Study Area*

This study covers the entire territory of Peru (1,300,000 km<sup>2</sup> approx.) located between the parallels of 0◦03 00 and 18◦30 0 south and the meridians of 68◦30 00 and 81◦30 00 west, sharing borders with Ecuador and Colombia to the north, Brazil to the east, Bolivia to the southeast, Chile to the south, and the Pacific Ocean to the west. The altitudinal gradient of this region starts from 0 m above sea level (a.s.l.) in the north and goes up to 6800 m above sea level (Mataraju Mountain). Almost 60% of the study area is covered by the Amazon rainforest, which is characterized by heavy rainfall and high temperatures, except for its southernmost part, which has cold winters and seasonal rainfall. The Protected Natural Areas (PNAs) belong to the National System of Natural Areas Protected by the State (SINANPE) [36]. These broadly include regional conservation areas, private conservation areas, national sanctuary, historic sanctuary, wildlife refuge, national reserve, communal reserve, national park, and hunting and protection forest scattered all over the study region (Figure 1).

#### *2.2. Dataset and Methodological Design*

The methodological framework used in the present study has been described graphically in Figure 2. From the cartographic standardization through the rescaling in the raster calculator of Qgis 3.16, 33 variables at a spatial resolution of 250 m were derived as input for use in modeling with MaxEnt. The bioclimatic information under current conditions (average 1970–2000) with a spatial resolution of 30 s (~1 km) was obtained from Woldclim version 2.1 (https://www.worldclim.org/data/worldclim21.html; accessed on 5 January 2021) [37]. Topographic factors such as elevation, slope, slope, and ground orientation were obtained from the 90 m spatial resolution DEM generated by the Shuttle Radar Topography Mission (SRTM) [38], United States Geological Survey (USGS) (http://srtm.usgs.gov; accessed on 28 December 2020). The edaphic variables were collected from SoilGrids 0.5.3 (http://soilgrids.org; accessed on 15 January 2021) with a spatial resolution of 250 m.

Likewise, the geographic occurrence data of 10 target species of the genus *Cedrela* to be used in the MaxEnt model were obtained from GBIF's Global Biodiversity Information Service (https://www.gbif.org/; accessed on 1 February 2021) through "Species Explorer" plug-in of QGIS software. The registration information of CITES species was obtained from the Ministry of the Environment of Peru (https://geoservidor.minam.gob.pe/recursos/ intercambio-de-datos/; accessed on 18 February 2021). Finally, to identify the locations of *Cedrela* habitats within the protected areas, and the areas prone to degradation but having high suitability for genus *Cedrela* habitat, the modeled potential distribution result was overlaid with the degraded areas identified by the Ministry of Environment (MINAM) and the spaces conserved by the National Service of Natural Areas Protected by the Peruvian State (SERNANP). These degraded areas were identified by the ministry mainly based on deforestation, soil erosion, forest fires, mining, illegal logging, etc.

**Figure 1.** Study area and presence of *Cedrela* species.

**Figure 2.** Methodological process for the biogeographic modeling of the genus *Cedrela* in Peru.
