Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Abstract

Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, such as climatic variables, also affect spectral signals simultaneously. Further, climatic variables are the major drivers of species distribution at macroscales. Therefore, the objective of this study is to determine if species distribution can be modeled using an indirect link to climate and remote sensing data (MODIS NDVI time series). We used plant occurrence data in the US states of North Carolina and South Carolina and 19 climatic variables to generate floristic and climatic gradients using principal component analysis, then we further modeled the correlations between floristic gradients and NDVI using Partial Least Square regression. We found strong statistical relationship between species distribution and NDVI time series in a region where clear floristic and climatic gradients exist. If this precondition is given, the use of niche-based proxies may be suitable for predictive modeling of species distributions at regional scales. This indirect estimation of vegetation patterns may be a viable alternative to mapping approaches using biochemistry-driven spectral signature of species.

Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy

“…Model accuracy was on average higher in FR than in CH, whereas the topographical and spectral ranges observed in CH were much narrower than in FR (electronic supplementary material, S1 and figures S2, S4, S5). This agrees with previous studies where accuracy of SDMs derived from satellite images increased with steepness of ecological gradients [6]. Unlike vegetation indices, we found that importance of spectral bands differed between sites.…”

“…Spectral information provided by remotely sensed reflectance is influenced by phenology, variations in morphological, structural and biochemical properties of species [3], as well as by local environmental conditions (e.g. hydric stress, soil properties or productivity [4,5]) that determine species habitat suitability [6]. Nevertheless, previous attempts to predict species distributions with hyperspectral data have generated mixed results [7,8].…”

Modeling the potential climate change- induced impacts on future genus Rhipicephalus (Acari: Ixodidae) tick distribution in semi-arid areas of Raya Azebo district, Northern Ethiopia

“…The contribution of variables to the model accuracy was determined according to Young et al (2011) who stated that "the higher the variable scored the percentage of contribution, the more impact that particular variable has on predicting the occurrence of the species". Considering this statement, temperature annual range (Bio7) and annual precipitation (Bio12) are the most important variables to explain the cattle tick distribution (Feilhauer et al, 2012). Thus, annual temperature range and precipitation were capable of predicting the current and future distribution of cattle ticks in the district.…”

Mapping Tree Density in Forests of the Southwestern USA Using Landsat 8 Data

“…The "Normalized Difference Vegetation Index" (NDVI) is perhaps the most widely used proxy [24,25]. Because of the differential nature of the reflectance of vegetation in the near infrared (NIR) and red bands of the electromagnetic spectrum, this index has been used to predict species diversity [26][27][28][29], changes in species composition [30][31][32], species distribution [33,34], the measurement of net primary productivity [35,36], and the quantification of land cover changes [37,38].…”