High-resolution topographic variables accurately predict the distribution of rare plant species for conservation area selection in a narrow-endemism hotspot in New Caledonia

Abstract

Species distribution models (SDMs) represent a widely acknowledged tool to identify priority areas on the basis of occurrence data and environmental factors. However, high levels of topographical and climatic micro-variation are a hindrance to reliably modelling the distribution of narrow-endemic species when based on classic occurrence and climate datasets. Here, we used high-resolution environmental variables and occurrence data obtained from dedicated field studies to produce accurate SDMs at a local scale. We modelled the potential current distribution of 23 of the 25 rarest species from Mount Kaala, a hotspot of narrow-endemism in New Caledonia, using occurrence data from two recent sampling campaigns, and eight high-resolution (10 m and 30 m) environmental predictors in a Species Distribution Modelling framework. After a first sampling operation, we surveyed six additional areas containing, overall, 13 of the 20 species modelled at this stage, to validate our projections where the highest species richness levels were predicted. The ability of our method to define conservation areas was largely validated with an average 84% of predicted species found in the validation areas, and additional data collected enabling us to model three more species. We therefore identified the areas of highest conservation value for the whole of Mount Kaala. Our results support the ability of SDMs based on presence-only data such as MaxEnt to predict areas of high conservation value using fine-resolution environmental layers and field-collected occurrence data in the context of small and heterogeneous systems such as tropical islands.

Remote Sensing Analysis of the Changes in Vegetated Coastland: The Case of Southeastern Europe

“…Topographic abiotic factors are commonly used in vegetation analysis. Normalised Difference Vegetation Index (NDVI) is a biotic factor to be derived from satellite images (e.g., Lannuzel et al 2021).…”

“…A set of surveys can be conducted to collect data about driving factors and pressures of DPRS framework (e.g. Gedefaw et al 2020), and to collect in situ data of plants for conservation (see Lannuzel et al 2021). Important Plant Areas that are hotspots of plant (and trees) conservation identified by International Union for Conservation of Nature (IUCN) can be potential areas for in situ plant data collection in the study region.…”

Here be dragons: important spatial uncertainty driven by climate data in forecasted distribution of an endangered insular reptile

“…Those species are difficult to model mostly because of data scarcity and statistical constrains related to low sample sizes and the lack of spatial coverage in environmental gradients. The recent availability of high-resolution environmental data along with methodological advances help filling this gap of knowledge (e.g., Dubos et al, 2021; Lannuzel et al, 2021). Forecasting models are useful to anticipate conservation management in the face of climate change.…”

Advantage and Disadvantage of Global and Local Climate Datasets on Modeling Species Distribution at Continental and Landscape Scales

“…Alternatively, few SDM studies had used high-resolution environmental data to predict species distribution range in mountainous areas (Lannuzel et al 2021; Raes et al 2009; Tomlinson et al 2020), whereas these models used topographical elements that was not directly related to ecological processes constrained species distribution (Tomlinson et al 2020). Therefore, uncertainties still exist in the correlation of species distribution with climate factors when the downscaled climate data from the WolrdClim dataset or topographical elements had been applied to model species distribution at ne scale (Lannuzel et al 2021;Tomlinson et al 2020). In order to model species distribution range and correlate species distribution with climate factors at landscape scale, a previous study had proposed an interpolation method to generate gridded climate dataset with high spatial resolution of 50 × 50 m 2 from daily data of local meteorological stations (Liao & Chen 2021).…”

“…Such local special ecological requirements can be di cult to distinguish in the broader-scale climate dataset (Guisan et al 2007). Alternatively, few SDM studies had used high-resolution environmental data to predict species distribution range in mountainous areas (Lannuzel et al 2021; Raes et al 2009; Tomlinson et al 2020), whereas these models used topographical elements that was not directly related to ecological processes constrained species distribution (Tomlinson et al 2020). Therefore, uncertainties still exist in the correlation of species distribution with climate factors when the downscaled climate data from the WolrdClim dataset or topographical elements had been applied to model species distribution at ne scale (Lannuzel et al 2021;Tomlinson et al 2020).…”