Prediction of Iberian lynx road–mortality in southern Spain: a new approach using the MaxEnt algorithm

Abstract

Prediction of Iberian lynx road-mortality in southern Spain: a new approach using the MaxEnt algorithm. In recent years, the Iberian lynx (Lynx pardinus) has experienced a significant increase in the size of its population and in its distribution. The species currently occupies areas in which it had been extinct for decades and new road mortality black spots have been identified. Its conservation requires an intensive risk assessment of road-deaths in potential future distribution areas. Using the MaxEnt algorithm we aimed to identify the roads where there is a greater risk of road collision for the Iberian lynx. More than 1,150 stretches of road were evaluated in Andalusia (southern Spain). Both road-related and habitat variables were included in the model. A total of 1,395 km of the 7,384 km evaluated (18.9 %) were classified as high risk road. Our results could help plan future conservation strategies. To our knowledge, this is the first time that the MaxEnt algorithm has been used to provide spatially-explicit predictions about wildlife road mortality.

Informing snake roadkill mitigation strategies in Taiwan using citizen science

“…MaxEnt is a machine‐learning approach that optimizes species‐environment associations using multiple function types including quadratic and product functions (Merow et al ) and is thus especially useful in mapping ecological phenomena that may have complex, non‐linear correlations, such as roadkill patterns. It has been shown to outperform alternative methods especially when there is a sufficient sample size and wide species distribution (Kasampalis et al , Duque‐Lazo et al ), and has been used in analyzing citizen science data (Crall et al , Fournier et al ) and mapping roadkill hotspots (Ha and Shilling , Garrote et al ). Because absence data are unavailable, MaxEnt compares presence points with a sample of points, known as background or pseudo‐absence points, from the study area of interest.…”

Where to start? Development of a spatial tool to prioritise retrofitting of power line poles that are dangerous to raptors

“…Highlighted among these processes is the development of predictive models. This approach has been widely applied to a set of processes that are closely tied to conservation biology (Franklin, 2010;Garrote, Fernández-López, López, Ruiz, & Simón, 2018). These applications include biodiversity assessment (Gaikwad, Wilson, & Ranganathan, 2011;López-López, Ferrer, Madero, Casado, & McGrady, 2011), biological reserve design (Ferrier, 2002), impact assessment and resource management (Wisz et al, 2008), invasion risk of alien species (Hernández-Lambraño, González-Moreno, & Sánchez-Agudo, 2017) and effects of global warming on biodiversity and ecosystems (Austin & van Niel, 2011).…”

“…A less widespread application of this approach has been its use to optimise the design of predictive risk models for wildlife (Pérez-García, DeVault, Botella, & Sánchez-Zapata, 2017). Until now, few studies have focused on the mapping of wildlife risks with wind turbines, roads, overhead power lines and illegal poisoning (Dwyer et al, 2016;Garrote et al, 2018;Mateo-Tomás, Olea, Sánchez-Barbudo, & Mateo, 2012;Pérez-García et al, 2017;Santos, Rodrigues, Jones, & Rebelo, 2013;Silva et al, 2014). In the case of the interactions between fauna and power lines, mortality caused by electrocution due to poorly designed poles significantly affects bird populations, particularly in rare species with low density or limited distribution (Lehman, Kennedy, & Savidge, 2007;López-López et al, 2011;Mojica, Dwyer, Harness, Williams, & Woodbridge, 2018).…”

Mammal-vehicle collisions on toll roads in São Paulo State: implications for wildlife, human safety and costs for society

“…These tools include predictive roadkill models (MALO et al, 2004 -Chapter 3) to identify variables (e.g. landscape, road design, road traffic), that are associated with collision locations for specific species SAEKI & MACDONALD, 2004;DUSSAULT et al, 2006;GARROTE et al, 2018;. Predictable models can be useful for environmental and transportation agencies because they allow for the identification of areas where mitigation measures are needed most (e.g.…”

“…Essas ferramentas incluem modelos de predição de atropelamentos (MALO et al, 2004 -Capítulo 3) para identificar variáveis (e.g. paisagem, design da rodovia, tráfego), que são associadas com locais de colisões para espécies específicas SAEKI & MACDONALD, 2004;DUSSAULT et al, 2006;GARROTE et al, 2018;.…”