Evaluating the capacity of species distribution modeling to predict the geographic distribution of the mangrove community in Mexico

Abstract

Mangroves are highly productive ecosystems that provide important environmental services, but have been impacted massively in recent years by human activities. Studies of mangroves have focused on their ecology and function at local or landscape scales, but little has been done to understand their broader distributional patterns or the environmental factors that determine those distributions. Species distribution models (SDMs), have been used to estimate potential distributions of hundreds of species, yet no SDM studies to date have assessed mangrove community distributions in Mexico (the country with the fourth largest extent of this ecosystem). We used maximum entropy approaches to model environmental suitability for mangrove species distributions in the country, and to identify the environmental factors most important in determining those distributions. We also evaluated whether this modeling approach is adequate to estimate mangrove distribution as a community across Mexico. Best models were selected based on statistical significance (AUC ratio), predictive performance (omission error of 5%), and model complexity (Akaike criterion); after this evaluation, only one model per species met the three evaluation criteria. Environmental variable sets that included distance to coast yielded significantly better models; variables with strongest contributions included elevation, temperature of the coldest month, and organic carbon content of soil. Based on our results, we conclude that SDMs can be used to map mangrove communities in Mexico, but that results can be improved at local scales with inclusion of local variables (salinity, hydroperiod and microtopography), field validations, and remote sensing data.

Assessment of suitable habitat of mangrove species for prioritizing restoration in coastal ecosystem of Sundarban Biosphere Reserve, India

“…Soil is considered an influencing parameter along with climate for the distribution of flora at spatial scales 1 , 79 . However, few studies have considered soil variables in modeling mangrove distribution 52 . Six important variables namely soil texture, soil salinity index, soil fertility index, sediment yield factor (Ton), electronic conductivity (EC), and vegetation soil salinity index (VSSI) were selected to examine the influence of soil (Appendix 2 D).…”

“…Hu et al 4 , 51 used the maximum entropy (MaxEnt) model for assessing the habitat suitability of mangrove forests in China. Rodríguez-Medina et al 52 also used MaxEnt model for determining suitable habitat distribution mangroves in Mexico. Wang et al 53 used this method for assessing habitat suitability in Guangdong Province of coastal China.…”

“…Many scholars have utilized SDMs for mapping suitable habitats of mangroves species 1,4,24,[49][50][51][52][53] . Sarker et al 24 prepared spatial density maps of prominent mangrove species and analyzed habitat suitability in Bangladesh Sundarban using generalized additive models (GAMs).…”

Predicting Current and Future Potential Distributions of Parthenium hysterophorus in Bangladesh Using Maximum Entropy Ecological Niche Modelling

“…Of the 19 bioclimatic variables that were available, due to data quality issues, we chose not to use four of these viz. mean temperature of wettest quarter (Bio8); mean temperature of driest quarter (Bio9); precipitation of warmest quarter (Bio18); and precipitation of coldest quarter (Bio19) [38,39]. We also obtained digital-elevation model data from the WorldClim website to calculate slope and aspect factors to use in our model.…”

“…We ran a series of candidate Maxent models with eight regulation multipliers (viz. 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0) and with five feature class combinations: L, LQ, LQH, LQHP, LQHPT (L = linear; Q = quadratic; H = hinge; P = product; T = threshold) [38,43]. We selected the final model with the lowest average test-omission rate and the highest average validation AUC to break ties [44].…”