Different Modelling Approaches to Determine Suitable Areas for Conserving Egg-Cone Pine (Pinus oocarpa Schiede) Plus Trees in the Central Part of Mexico

Abstract

Various spatial modelling methods and tools have been used in ecology and biogeography. The application of these options serves a dual function: first, they offer information about the potential distribution of species to understand the richness and diversity of unassessed areas. Second, spatial modelling methods employ these predictions to select relevant sites to determine natural conservation areas. In this study, we compared three methods for modelling the spatial distribution of Egg-cone Pine (Pinus oocarpa Schiede), an important non-timber pine in Mexico. The final goal is to estimate suitable areas for the conservation and reproduction of superior individuals (plus trees) of P. oocarpa as a conservation strategy outside the known distribution since this species possesses a high ecological and economic value. The model used were a generalised linear model (GLM) as a parametric regression method, random forest (RF) as a machine-learning method, and the MaxEnt model, a standard procedure, implemented using the Kuenm R package. The results suggest that the models used performed well since the AUROC was between 0.95 and 0.98 in all cases. MaxEnt and random forest approaches provided more conservative predictions for the distribution of suitable areas of plus trees of P. oocarpa than the generalised linear model, but the random forest algorithm achieved the best performance. The results of the study allowed the determination of ex situ conservation areas for P. oocarpa plus trees outside of their known distribution.

Key factors for species distribution modeling in benthic marine environments

“…Similarly, MAXENT outperformed other algorithms, such as a variant of RF, BRT, and MARS, in predictions involving 171 species in both random and spatial partitioning (Valavi et al, 2023). However, a number of previous studies have reported different outcomes, which are consistent with (Barker and MacIsaac, 2022;Hysen et al, 2022;Romero-Sanchez et al, 2022). Additionally, the smaller number of areas with high residuals and lower uncertainty in the predicted habitat suitability of RF and BRT, compared to those of MARS and MAXENT in M. oculata prediction, also indicates the higher accuracy of predictions using RF and BRT.…”

“…For instance,Barker and MacIsaac (2022) found BRT to outperform RF, MARS, and MAXENT in predictions involving virtual species;Romero-Sanchez et al (2022) found RF to achieve better performance than MAXENT in predictions for plus trees; andHysen et al (2022) found RF to perform better than MARS and MAXENT in nest predictions…”

Geographical and Climatic Variation of <em>Pinus oocarpa</em> Resin in Southern Mexico Provenances

“…(58.9 kg ha-1 per month) [15]. Therefore, in 2019, the National Institute of Forestry, Agriculture, and Livestock Research initiated a broader research project to improve the resin production of this species in Mexico, through the selection of high-yield resin provenances and phenotypes [16,17].…”

Geographic and Climatic Variation in Resin Components and Quality of Pinus oocarpa in Southern Mexico Provenances

“…[21]. Therefore, in 2019, the National Institute of Forestry, Agriculture, and Livestock Research initiated a broader research project to improve the resin production of this species in Mexico, through the selection of high-yield resin provenances and phenotypes [22,23].…”