Combining Satellite Remote Sensing and Climate Data in Species Distribution Models to Improve the Conservation of Iberian White Oaks (Quercus L.)

Linking functional composition moments of the sub-Mediterranean ecotone with environmental drivers

Introduction

Functional trait-based approaches are extensively applied to the study of mechanisms governing community assembly along environmental gradients. These approaches have been classically based on studying differences in mean values among species, but there is increasing recognition that alternative metrics of trait distributions should be considered to decipher the mechanisms determining community assembly and species coexistence. Under this framework, the main aim of this study is to unravel the effects of environmental conditions as drivers of plant community assembly in sub-Mediterranean ecotones.

Methods

We set 60 plots in six plant communities of a sub-Mediterranean forest in Central Spain, and measured key above- and belowground functional traits in 411 individuals belonging to 19 species, along with abiotic variables. We calculated community-weighted mean (CWM), skewness (CWS) and kurtosis (CWK) of three plant dimensions, and used maximum likelihood techniques to analyze how variation in these functional community traits was driven by abiotic factors. Additionally, we estimated the relative contribution of intraspecific trait variability and species turnover to variation in CWM.

Results and discussion

The first three axes of variation of the principal component analyses were related to three main plant ecological dimensions: Leaf Economics Spectrum, Root Economics Spectrum and plant hydraulic architecture, respectively. Type of community was the most important factor determining differences in the functional structure among communities, as compared to the role of abiotic variables. We found strong differences among communities in their CWMs in line with their biogeographic origin (Eurosiberian vs Mediterranean), while differences in CWS and CWK indicate different trends in the functional structure among communities and the coexistence of different functional strategies, respectively. Moreover, changes in functional composition were primarily due to intraspecific variability.

Conclusion

We observed a high number of strategies in the forest with the different communities spreading along the acquisitive-conservative axis of resource-use, partly matching their Eurosiberian-Mediterranean nature, respectively. Intraspecific trait variability, rather than species turnover, stood as the most relevant factor when analyzing functional changes and assembly patterns among communities. Altogether, our data support the notion that ecotones are ecosystems where relatively minor environmental shifts may result in changes in plant and functional composition.

A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling

Plant species diversity (PSD) has always been an essential component of biodiversity and plays an important role in ecosystem functions and services. However, it is still a huge challenge to simulate the spatial distribution of PSD due to the difficulties of data acquisition and unsatisfactory performance of predicting algorithms over large areas. A surge in the number of remote sensing imagery, along with the great success of machine learning, opens new opportunities for the mapping of PSD. Therefore, different machine learning algorithms combined with high-accuracy surface modeling (HASM) were firstly proposed to predict the PSD in the Xinghai, northeastern Qinghai-Tibetan Plateau, China. Spectral reflectance and vegetation indices, generated from Landsat 8 images, and environmental variables were taken as the potential explanatory factors of machine learning models including least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The prediction generated from these machine learning methods and in situ observation data were integrated by using HASM for the high-accuracy mapping of PSD including three species diversity indices. The results showed that PSD was closely associated with vegetation indices, followed by spectral reflectance and environmental factors. XGBoost combined with HASM (HASM-XGBoost) showed the best performance with the lowest MAE and RMSE. Our results suggested that the fusion of heterogeneous data and the ensemble of heterogeneous models may revolutionize our ability to predict the PSD over large areas, especially in some places limited by sparse field samples.

The Ant-like Tachydromia Complex in the Iberian Peninsula-Insights from Habitat Suitability Modelling for the Conservation of an Endemism (Diptera: Hybotidae)

Simple Summary

The ant-like flies are a group of nine species of flightless Tachydromia, with their distribution restricted to the Iberian Peninsula. Severe knowledge gaps regarding their distribution and ecological requirements hinder conservation assessments. To improve this scenario, an ensemble of 9 different species distribution models was applied to unveil habitat suitability and to provide guidelines for future studies. The most important factors influencing habitat suitability are climate-related, followed by forest type and structure, with well-defined biogeographic gradients. T. lusitanica and T. ebejeri are adapted to the mild temperature and high-humidity environments, typical of the Temperate–Eurosiberian life zone. T. semiaptera and T. iberica are adapted to progressively drier and hotter central and southern parts of the Iberian Peninsula, connected to transitional Temperate–submediterranean areas. Ant-like flies’ distribution overlaps with deciduous/ marcescent oak species, which can effectively indicate their presence in Iberia. Additionally, southern marcescent forests emerge as “islands” with particular interest for future fieldwork. Ant-like flies are threatened by several factors such as climate change and habitat destruction, including urbanization and forest fires. This study provides vital tools to better assess the ant-like flies’ conservation status and to manage their habitat.

Abstract

Ant-like flies comprise nine Iberian endemic species of flightless Tachydromia. Severe knowledge gaps on distribution and ecological requirements hinder conservation assessments. Species distribution models were applied to unveil habitat suitability and to provide guidelines for future studies. An ensemble modeling approach combining ten different techniques was implemented with the biomod2 package. Occurrence data was partitioned into six sets, including two multi-species groups and four species. The most relevant drivers of habitat suitability are climate-related, followed by forest type and structure, according to well-defined biogeographic gradients. T. lusitanica and T. ebejeri are adapted to mild temperatures and high-humidity environments. Their distribution is connected to the Temperate–Eurosiberian life zone. T. semiaptera and T. iberica are adapted to progressively drier and hotter central and southern parts of the Iberian Peninsula, connected to transitional Temperate–submediterranean areas. Ant-like fly’ ranges overlap with deciduous/marcescent oak species, acting as suitable indicators of their presence in Iberia. Southern marcescent forests emerge as “islands” with particular interest for future prospections. Ant-like flies are threatened by several factors such as climate change and habitat destruction, including urbanization and forest fires. This study provides vital tools to better assess the ant-like flies’ conservation status and to manage their habitat.

Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping

In the light of the “Biological Diversity” concept, habitats are cardinal pieces for biodiversity quantitative estimation at a local and global scale. In Europe EUNIS (European Nature Information System) is a system tool for habitat identification and assessment. Earth Observation (EO) data, which are acquired by satellite sensors, offer new opportunities for environmental sciences and they are revolutionizing the methodologies applied. These are providing unprecedented insights for habitat monitoring and for evaluating the Sustainable Development Goals (SDGs) indicators. This paper shows the results of a novel approach for a spatially explicit habitat mapping in Italy at a national scale, using a supervised machine learning model (SMLM), through the combination of vegetation plot database (as response variable), and both spectral and environmental predictors. The procedure integrates forest habitat data in Italy from the European Vegetation Archive (EVA), with Sentinel-2 imagery processing (vegetation indices time series, spectral indices, and single bands spectral signals) and environmental data variables (i.e., climatic and topographic), to parameterize a Random Forests (RF) classifier. The obtained results classify 24 forest habitats according to the EUNIS III level: 12 broadleaved deciduous (T1), 4 broadleaved evergreen (T2) and eight needleleaved forest habitats (T3), and achieved an overall accuracy of 87% at the EUNIS II level classes (T1, T2, T3), and an overall accuracy of 76.14% at the EUNIS III level. The highest overall accuracy value was obtained for the broadleaved evergreen forest equal to 91%, followed by 76% and 68% for needleleaved and broadleaved deciduous habitat forests, respectively. The results of the proposed methodology open the way to increase the EUNIS habitat categories to be mapped together with their geographical extent, and to test different semi-supervised machine learning algorithms and ensemble modelling methods.

The Modeling and Forecasting of Carabid Beetle Distribution in Northwestern China

Simple Summary

The relationship between species and environment are an important basis for the study of biodiversity. Most researchers have found the distribution of indicator insects such as carabid beetle at the local community scale; however, a few studies on the distribution of indicator insects in grassland in China. Here, we used Generalized Additive Models (GAM) to predict temperate steppe of northwestern China carabid beetle species richness distribution, and to determine the possible underlying causal factors. Predicted values of beetle richness ranged from 3 to 12. The diversity hotspots are located in the southwest, south and southeast of the study area which have moist environment, the carabid beetle is mainly influenced by temperature and precipitation. The results underline the importance of management and conservation strategies for grassland and also provides evidence for assessing beetle diversity in temperature steppe.

Abstract

Beetles are key insect species in global biodiversity and play a significant role in steppe ecosystems. In the temperate steppe of China, the increasing degeneration of the grasslands threatens beetle species and their habitat. Using Generalized Additive Models (GAMs), we aimed to predict and map beetle richness patterns within the temperate steppe of Ningxia (China). We tested 19 environmental predictors including climate, topography, soil moisture and space as well as vegetation. Climatic variables (temperature, precipitation, soil temperature) consistently appeared among the most important predictors for beetle groups modeled. GAM generated predictive cartography for the study area. Our models explained a significant percentage of the variation in carabid beetle richness (79.8%), carabid beetle richness distribution seems to be mainly influenced by temperature and precipitation. The results have important implications for management and conservation strategies and also provides evidence for assessing and making predictions of beetle diversity across the steppe.