Past, current, and future trends of red spiny lobster based on PCA with MaxEnt model in Galapagos Islands, Ecuador

Abstract

In order to enhance in terms of accuracy and predict the modeling of the potential distribution of species, the integration of using principal components of environmental variables as input of maximum entropy (MaxEnt) has been proposed in this study. Principal components selected previously from the principal component analysis results performed in Arc GIS in the environmental variables was used as an input data of MaxEnt instead of raw data to model the potential distribution of red spiny lobster from the year 1997 to 2015 and for three different future scenarios 2020, 2050, and 2070. One set of six original environmental variables pertaining to the years 1997–2015 and one set of four variables for future scenarios were transformed independently into a single multiband raster in Arc GIS in order to select the variables whose eigenvalues explains more than 5% of the total variance with the purpose to use in the modeling prediction in MaxEnt. The years 1997 and 1998 were chosen to compare the accuracy of the model, showing better results using principal components instead of raw data in terms of area under the curve and partial receiver operating characteristic as well as better predictions of suitable areas. Using principal components as input of MaxEnt enhances the prediction of good habitat suitability for red spiny lobster; however, future scenarios suggest an adequate management by researches to elaborate appropriate guidelines for the conservation of the habitat for this valuable specie with face to the climate change.

Predicting the Suitable Geographical Distribution of Sinadoxa Corydalifolia under Different Climate Change Scenarios in the Three-River Region Using the MaxEnt Model

“…There are several reports on the prediction of suitable species distributions under climate change using the MaxEnt model. These reports mainly focus on energy plants, medicinal plants, invasive species, endangered species, and the migration routes of species [ 4 , 38 , 39 , 42 , 45 , 46 , 47 , 48 ]. Different plants have different requirements for their growth environments, resulting in obvious differences in suitable distributions.…”

Factors affecting relative abundance of low-mobility fishing resources: spiny lobster in the Galapagos Marine Reserve

“…Temperature also plays an important role as a determining factor in survival rate, intermolt period, and growth rates in adult lobsters (William, Lellis & Russell, 1990). These biological processes could influence other aspects such as recruitment patterns in the fishing zones, which in turn could modify the species’ distribution and abundance (Atema & Cobb, 1980; Reck, 1983; Toral-Granda et al., 2002; Daw, 2008; Defeo et al, 2013; Moya, Jacome & Yoo, 2017). All these factors can impact resource availability and catchability, and hence the CPUE values.…”

“…As suggested by Anderson (2007), we defined a priori 15 candidate models including meaningful variables according to the information reported in the literature, to offer theoretical support regarding the selected components (Defeo et al, 2013; Edgar et al, 2004; Hearn et al, 2004; Moya, Jacome & Yoo, 2017). The implicit assumption we made was that fishers could modify the fishing operations in terms of time and space, given the abundance of the resources, access to those resources, fishers’ experience, and preferences of the fishers for some fishing areas, as well as environmental conditions.…”

A new GIS model for ecologically suitable distributions of medicinal plants

“…However, inputting a large dataset of relevant ecoclimatic covariates often lead to multicollinearity, a mathematical problem defined as a high degree of correlation amongst covariates in nonexperimental situations. PCA, a multivariate technique tool of ArcGIS 10.4, is used to reduce multiple data dimensions into a smaller number of analytical variables, which conserve a high proportion of the original information [18, 19]. This tool may be used to analyse the ecoclimatic variables in environmental layers.…”