bioLEC : A Python package to measure Landscape Elevational Connectivity

In mountainous landscapes long known for playing a significant role in evolution (Elsen & Tingley, 2015; Wallace, 1860), one approach consists of quantifying and measuring abiotic landscape properties. Indeed, complex mountainous landscapes provide species with a rich variety of environmental conditions over restricted surface areas (e.g., range of temperature, solar irradiation, wind exposure, moisture and rainfall, soil thickness and composition) (Hoorn, Mosbrugger, Mulch, & Antonelli, 2013).

In mountainous landscapes long known for playing a significant role in evolution (Elsen & Tingley, 2015;Wallace, 1860), one approach consists of quantifying and measuring abiotic landscape properties.Indeed, complex mountainous landscapes provide species with a rich variety of environmental conditions over restricted surface areas (e.g., range of temperature, solar irradiation, wind exposure, moisture and rainfall, soil thickness and composition) (Hoorn, Mosbrugger, Mulch, & Antonelli, 2013).
Estimates of some of theses landscape abiotic properties are already available through standard software such as ArcGIS or QGIS (Etherington, 2016) and in more specific mountainous landscape focussed packages such as LSD Topo Tools (Clubb et al., 2017) or pyBadlands (Salles, Ding, & Brocard, 2018).As an example, these tools include functions to assess slope and slope curvature from which soil thickness can be predicted, slope azimuth (which controls solar radiation); the number of catchments and their size (landscape fragmentation), the hydrological connectivity and drainage density (transfer of water and nutrients); the hypsometry (i.e., elevation vs. surface area), or the direction and rate of divide migration.

bioLEC
In 2016, a new metric called the Landscape Elevational Connectivity (LEC) was proposed to estimate biodiversity in mountainous landscape (Bertuzzo et al., 2016).It efficiently measures the landscape resistance to migration and is able to account for up to 70% of biodiversity predicted by meta-community models (Bertuzzo et al., 2016).
bioLEC is a Python package designed to quickly calculate for any mountainous landscape surface and species niche width its associated LEC index.From an elevational fitness perspective, all migratory paths on a flat landscape are equal.This is not the case on a complex landscape where migration can only occur along a network of corridors providing species with their elevational requirements.Hence, predicting how species will disperse across a landscape requires a model of migration that takes into account the physical properties of the landscape, the species fitness range, as well as evolving environmental conditions.bioLEC measures the landscape resistance to migration by calculating the connectivity between points in the landscape based on scikit-image Dijkstra's algorithm (van der Walt et al., 2014), the higher the connectivity the lesser the landscape resistance to migration.It quantifies the closeness of any of these points to all others at similar elevation by integrating specific fitness range and represents a robust proxy for the biodiversity a landscape can support (Bertuzzo et al., 2016).It measures how easily a species living in a given patch can spread and colonise other patches.As explained above it is assumed to be elevation-dependent and it depends on how often a species adapted to a given elevation needs to travel outside its optimal elevation range when moving from its patch to any other in the landscape.bioLEC package can be used in serial or parallel to evaluate biodiversity patterns of a given landscape.It is an efficient and easy-to-use tool to quickly assess the capacity of landscape to support biodiversity and to predict species dispersal across mountainous landscapes.

Figure 1 :
Figure 1: An example of LEC map obtained for a specific elevation surface (left) illustrating the region of high and low connectivity as well as the distribution of resulting LEC values versus elevation range.