A methodological procedure for the estimation of ecological value applied to a neotropical cloud forest.

Ecological value (EV) refers to the intrinsic values of a landscape based on the assessment of five criteria: biodiversity, vulnerability, fragmentation, connectivity, and resilience. While many studies use remote sensing for EV assessment, few incorporate fieldwork data. In our research, we present a novel methodology involving field data collection to quantify each criterion. Additionally, we propose a numerical procedure to aggregate rankings and determine EV. Our study took place in a highly biodiverse neotropical cloud forest in western Mexico. Biodiversity was assessed through evenness, vulnerability by counting threatened species, fragmentation based on tree functional traits, connectivity by tree dispersal syndrome and successional behavior, and resilience from tree species' information and material legacies. To reveal similarity patterns among plots regarding the criteria and EV, we used nonmetric multidimensional scaling with permutational multivariate analysis of variance. To assess EV estimation reliability, we used altitude and azimuth as predictors through generalized additive models. The methodology unveiled that plots with the highest EV do not necessarily possess superior ecological properties (biodiversity and/or vulnerability) or structural and functional features (fragmentation, connectivity, and/or resilience), thus demonstrating the importance of including all criteria in the assessment and avoiding the use of a single criterion. Results showed that cloud forest plots with the highest EV were at an altitude of 1900–2200 m asl, facing southeast and northwest orientations. These plots were characterized by a high number of threatened species, low fragmentation, and high levels of connectivity and resilience. [ABSTRACT FROM AUTHOR]