connectivity can be detrimental in some cases, such as that of invasive species,
a theme that is discussed at length in Chap. 5.
(3) Computer Simulations. These tools have the potential to play a major role in
understanding the impact of railways in wild animal populations and, accordingly, in planning new railway networks or developing mitigation measures.
However, to the best of our knowledge, there are only a few simulation studies
that specifically target the impact of the fragmentation caused by railways.
Simulations can be used before and after railway construction. Before the
construction phase, an impact assessment is desirable to compare alternatives.
In particular, it is important to avoid cutting through areas of great natural
value, but if this is unavoidable, then it is necessary to identify the sectors most
affected in order to implement mitigation measures. In such situations, a
region-wide focus that includes future projections is the most suitable approach.
For these purposes, graph theory is being increasingly used in conservation
biology, as graph models provide simplified representations of ecological
networks with flexible data requirements (Urban et al. 2009). For instance,
Clauzel et al. (2013) (see also Chap. 13) combined graph-based analysis and
species distribution models to assess the impact of a railway line on the future
distribution of the European tree frogs (Hyla arborea) in France. This study
was able to identify—among potential routes—the railway line with the lowest
impact on the species distribution.
Mateo-Sánchez et al. (2014) conducted computer simulations to assess the
degree of connectivity of two populations of the endangered brown bear in
north-western Spain. They used a multi-scale habitat model to predict the
presence of bears as a function of habitat suitability, combined with a factorial
least-cost path density analysis. With this model, the authors identified possible
corridors that could connect the two populations and the locations that should
be prioritized in order to ameliorate the permeability of the local railways (and
roads). In a study to identify the most suitable corridor in a future railway line
in Sweden, Karlson et al. (2016) integrated models with ecological and geological information by using spatial multi-criteria analysis techniques to generate a set of potential railway corridors, followed by the application of the
lowest cost path analysis in order to find the corridor with the best environmental performance within the set.
Much of what has been learned from simulations applied to the impact of roads
(e.g., Roger et al. 2011; Borda-de-Água et al. 2011) can also be used in railway
ecology. Among the techniques used, we highlight the individuals-based
models, (hereafter “IBM”) (e.g., Lacy 2000; Jaeger and Fahrig 2004;
Kramer-Schadt et al. 2004; Grimm et al. 2006). While more traditional simulation approaches use variables to study the collective behavior of certain
entities (for instance, an entire population could be characterized by a single
variable describing the total number of individuals), IBMs explicitly simulate
all individuals as separate entities, each with its own set of characteristics, and
interacting among them and with the environment. The main advantage of
4 Railways as Barriers for Wildlife: Current Knowledge
53
a theme that is discussed at length in Chap. 5.
(3) Computer Simulations. These tools have the potential to play a major role in
understanding the impact of railways in wild animal populations and, accordingly, in planning new railway networks or developing mitigation measures.
However, to the best of our knowledge, there are only a few simulation studies
that specifically target the impact of the fragmentation caused by railways.
Simulations can be used before and after railway construction. Before the
construction phase, an impact assessment is desirable to compare alternatives.
In particular, it is important to avoid cutting through areas of great natural
value, but if this is unavoidable, then it is necessary to identify the sectors most
affected in order to implement mitigation measures. In such situations, a
region-wide focus that includes future projections is the most suitable approach.
For these purposes, graph theory is being increasingly used in conservation
biology, as graph models provide simplified representations of ecological
networks with flexible data requirements (Urban et al. 2009). For instance,
Clauzel et al. (2013) (see also Chap. 13) combined graph-based analysis and
species distribution models to assess the impact of a railway line on the future
distribution of the European tree frogs (Hyla arborea) in France. This study
was able to identify—among potential routes—the railway line with the lowest
impact on the species distribution.
Mateo-Sánchez et al. (2014) conducted computer simulations to assess the
degree of connectivity of two populations of the endangered brown bear in
north-western Spain. They used a multi-scale habitat model to predict the
presence of bears as a function of habitat suitability, combined with a factorial
least-cost path density analysis. With this model, the authors identified possible
corridors that could connect the two populations and the locations that should
be prioritized in order to ameliorate the permeability of the local railways (and
roads). In a study to identify the most suitable corridor in a future railway line
in Sweden, Karlson et al. (2016) integrated models with ecological and geological information by using spatial multi-criteria analysis techniques to generate a set of potential railway corridors, followed by the application of the
lowest cost path analysis in order to find the corridor with the best environmental performance within the set.
Much of what has been learned from simulations applied to the impact of roads
(e.g., Roger et al. 2011; Borda-de-Água et al. 2011) can also be used in railway
ecology. Among the techniques used, we highlight the individuals-based
models, (hereafter “IBM”) (e.g., Lacy 2000; Jaeger and Fahrig 2004;
Kramer-Schadt et al. 2004; Grimm et al. 2006). While more traditional simulation approaches use variables to study the collective behavior of certain
entities (for instance, an entire population could be characterized by a single
variable describing the total number of individuals), IBMs explicitly simulate
all individuals as separate entities, each with its own set of characteristics, and
interacting among them and with the environment. The main advantage of
4 Railways as Barriers for Wildlife: Current Knowledge
53