Optimization of species distribution models using a genetic algorithm for simulating climate change effects on Zagros forests in Iran

Climatic variables are more effective on the spatial distribution of oak forests than land use change across their historical range

The current research is conducted to model the effect of climate change and land use change (LUC) on the geographical distribution of Quercus brantii Lindl. (QB) forests across their historical range. Forecasting was done based on six general circulation models under RCP 2.6 and RCP 8.5 future climate change scenarios for the future years 2050 and 2070. In order to model the species distribution, different modeling methods were used. The results indicated that, in general, climatic variables had a higher influence on the distribution of QB than land use-related attributes. The mean diurnal range (bio2), the precipitation seasonality (bio15), and the mean temperature of the driest quarter (bio9) were the main predictors in the distribution of QB forests, while land use variables were less important in oak species distribution. The GBM, MaxEnt, and RF had higher accuracy and performance in modeling species distribution. The outputs also showed that in the current climate circumstances, 97,608.81 km2 of the studied area has high desirability for the presence of QB, and by 2070, under the pessimistic scenario, 96.29% of these habitats will be lost under the concomitant effect of LUC and climate change. By using the results of this research, it is possible to predict and identify the effective factors in changing the habitat of this oak species with more certainty. Based on the insights obtained from the results of such studies, the protection and restoration planning of the habitat of this key species, which supports diverse species, will be provided more efficiently.

Modelling the effects of topographic heterogeneity on distribution of Nitraria tangutorum Bobr. species in deserts using LiDAR-data

Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. Many hot issues can be investigated in desert ecosystems, including the relationship between species distribution and environmental gradients (e.g., elevation, slope, topographic convergence index, and solar insolation). Species Distribution Models (SDMs) can be used to understand these relationships. We used data acquired from the important desert plant Nitraria tangutorum Bobr. communities and desert topographic factors extracted from LiDAR (Light Detection and Ranging) data of one square kilometer in the inner Mongolia region of China to develop SDMs. We evaluated the performance of SDMs developed with a variety of both the parametric and nonparametric algorithms (Bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, Generalized Linear Model, Generalized Additive Model, Random Forest (RF), and Support Vector Machine). The area under the receiver operating characteristic curve was used to evaluate these algorithms. The SDMs developed with RF showed the best performance based on the area under curve (0.7733). We also produced the Nitraria tangutorum Bobr. distribution maps with the best SDM and suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to southern part with 0-20 degree slopes at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on the desert species distribution on a small scale. The presented SDMs can have important applications for predicting species distribution and will be useful for preparing conservation and management strategies for desert ecosystems on a small scale.

Comparison between optimized MaxEnt and random forest modeling in predicting potential distribution: A case study with Quasipaa boulengeri in China

Random forest (RF) and MaxEnt models are shallow machine learning approaches that perform well in predicting species' potential distributions. RF models can produce robust results with the default automatic configuration in most cases, but it is necessary for MaxEnt to optimize the model settings to improve the performance, and the predictive performance difference between optimized MaxEnt and RF is uncertain. To explore this issue, the potential distribution of the endangered amphibian Quasipaa boulengeri in China was predicted using optimized MaxEnt and RF models. A total of 408 occurrence data were selected, 1000 locations were generated as pseudo-absence data by the geographic distance method, and 10,000 sites were selected as background data by creating a bias file. Partial ROC at different thresholds and success rate curves were used to compare the predictive performances between optimized MaxEnt and RF. Our results showed that the RF and optimized MaxEnt models both had good performance in predicting the potential distribution of Q. boulengeri, with the RF model performing slightly better whether based on partial ROC or success rate curves. Furthermore, the core suitable habitat regions of Q. boulengeri identified by RF and MaxEnt were similar and were all located in the Sichuan, Chongqing, Hubei, Hunan, and Guizhou provinces. However, the RF model produced a habitat suitability map with higher discrimination and greater heterogeneity. Temperature annual range, mean temperature of the driest quarter, and annual precipitation were the vital environmental variables limiting the distribution of Q. boulengeri. The RF model is the stronger machine learner. We believe it may be more applicable in predicting the native potential distributions of species with sufficient occurrence data, given the additional predictive detail, the simplicity of use, the computational time involved, and the operational complexity.

Modeling the effects of climate change on the potential distribution of the rangeland species Gymnocarpus decander Forssk (case study: Arid region of southeastern Iran)

The phenomenon of climate change is the biggest environmental challenge in the world. Climate is a determinant factor in species distribution, and climate change will affect the species' abilities to occupy geographic regions. In this study which was conducted in May of 2019, spatio-temporal changes in potential habitats of Gymnocarpus decander were assessed using the MRI-CGCM3 climate change model for RCP2.6 and RCP8.5 scenarios for the near future (2041-2061) and far future (2061-2080) periods for this purpose, climatic variables of 24 synoptic stations across a case study, bio-climatic data and vegetation cover maps of G. decander were used. First, using the factor analysis process, the dimensions of the station-observed climatic variables were reduced to five factors with a total variance of 88.3%. Then, the region was divided into five homogeneous climatic regions using partitional clustering analysis. In this study by using the logistic regression modeling technique, the probability of the presence of the desired species for two groups of independent variables including climatic factors and bioclimatic variables in each of the groups was modeled. The results showed that the best models for determining the potential habitats of G. decander are logistic regression models in groups with independent bioclimatic variables. According to the results obtained from both scenarios, the habitats of G. decander species will decrease in the future. In the most optimistic case, about 8% of G. decander habitats will be lost by 2060 and about 12% by 2080. According to modeling results, currently, 48.2% total area of the region under study has a high potential for the presence of G. decander. Also, results indicate that region number 4 in this study with an altitude range of about 800-1250 m, 16 °C average temperature in the growing season and annual precipitation around 150-170 mm is the major habitat for G. decander. According to climate change under the RCP2.6 scenario, the area of potential habitats of G. decander will decrease to 40% in the near future and 36.4% in the far future; and according to climate change under the RCP8.5 scenario, the area of potential habitats of G. decander will decrease to 23.9% in the near future and 32.5% in the far future. In the far future, because of the increase in total precipitation, some of the lost potential habitats during the near future will be suitable again for G. decander. Due to its stability in harsh environmental conditions, G. decander appears as a type-forming species in a wide range of natural habitats in the study area and is therefore important in terms of soil protection and forage production.