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Keywords:

  • BACI study design;
  • biodiversity hierarchy;
  • interdisciplinary collaboration;
  • landscape genetics;
  • noninvasive genetic sampling

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Road effects on wildlife
  5. Review of studies using genetic data to assess roads effects
  6. Towards molecular road ecology
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

Transportation infrastructures such as roads, railroads and canals can have major environmental impacts. Ecological road effects include the destruction and fragmentation of habitat, the interruption of ecological processes and increased erosion and pollution. Growing concern about these ecological road effects has led to the emergence of a new scientific discipline called road ecology. The goal of road ecology is to provide planners with scientific advice on how to avoid, minimize or mitigate negative environmental impacts of transportation. In this review, we explore the potential of molecular genetics to contribute to road ecology. First, we summarize general findings from road ecology and review studies that investigate road effects using genetic data. These studies generally focus only on barrier effects of roads on local genetic diversity and structure and only use a fraction of available molecular approaches. Thus, we propose additional molecular applications that can be used to evaluate road effects across multiple scales and dimensions of the biodiversity hierarchy. Finally, we make recommendations for future research questions and study designs that would advance molecular road ecology. Our review demonstrates that molecular approaches can substantially contribute to road ecology research and that interdisciplinary, long-term collaborations will be particularly important for realizing the full potential of molecular road ecology.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Road effects on wildlife
  5. Review of studies using genetic data to assess roads effects
  6. Towards molecular road ecology
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

Transportation infrastructures such as roads, railroads and canals have become omnipresent features of contemporary landscapes. While they cover seemingly small proportions of the land surface, their ecological impacts reach far. For example, the ∼6.3 million kilometres (km) of roads in the US cover only about 1% of the land, yet they affect an estimated 20% of the landscape (Forman 2000; Forman et al. 2003). A similar percentage of the Netherlands is impacted by roads (Reijnen & Foppen 2006) and road densities in many other developed countries, such as the United Kingdom, Germany and Japan are 2.5–4 times higher than that in the USA (Forman et al. 2003). Jaeger et al. (2007) found that road development has decreased unfragmented areas larger than 100 km2 to about 2% of the overall landscape in a West German state, and Riiters & Wickham (2003) estimated that only about 17% of US lands are more than a kilometre from the closest road. Even in developing countries, transportation networks can impact substantial percentages of the landmass (Kirsten 2006). With an estimated 28 million km of roads worldwide (CIA 2005), transportation infrastructures play a prominent role in shaping the environment, and only few areas on earth remain truly unaffected by roads.

Transportation infrastructures (hereafter: roads) affect the environment in various ways, with tremendous ecological implications. These direct and indirect road effects have been recognized and described for more than 60 years (e.g. Huey 1941; Hodson 1966; Ellenberg et al. 1981; Mader 1984) and they include the alteration of habitat, the interruption of ecological flows and increased erosion and pollution. The increasing need to understand and respond to the specific ecological impacts of roads ultimately led to the emergence of a new scientific discipline, entitled road ecology (Forman et al. 2003). Road ecology provides an integrated and solution-oriented framework for addressing the environmental effects of roads. Particularly, the goal of road ecology is to provide planners with practical advice on how to avoid, minimize or mitigate negative environmental impacts of transportation.

The emergence of road ecology has been paralleled by an increased interest in the application of molecular genetic approaches in ecology and natural resource management (e.g. DeYoung 2007; Geffen et al. 2007; Schwartz et al. 2007). Genetic approaches are highly valuable for applied wildlife management and general ecological research, and genetic applications for road ecology research have been proposed for several years (e.g. Hardy et al. 2003; Clevenger 2005; Shepard et al. 2008). However, molecular ecologists are not always aware of all possible road effects on animals and they often only assess whether roads affect genetic diversity and structure. On the other hand, many road ecologists are unfamiliar with recent advances in molecular ecology and often cannot evaluate how genetic approaches can help address a broader range of research questions in road ecology.

Our goal in this review is to bridge the gap between molecular ecology and road ecology by exploring the potential of genetic approaches to contribute to road ecology. Specifically, we highlight that current genetic studies often only assess local road effects on genetic population connectivity, even though many other road effects could be detected using genetic approaches. We begin by summarizing the general findings of road ecology research and then briefly review published studies that use genetic approaches to evaluate road effects. Finally, we outline molecular approaches for assessing additional road effects on all levels of biodiversity and provide recommendations for improved study designs in molecular road ecology.

Road effects on wildlife

  1. Top of page
  2. Abstract
  3. Introduction
  4. Road effects on wildlife
  5. Review of studies using genetic data to assess roads effects
  6. Towards molecular road ecology
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

Roads impact individual organisms, populations, species, ecosystems and landscapes in various ways. In this article, we summarize the main road effects on wildlife and highlight information that might be particularly novel and interesting for molecular ecologists. We refer readers to other studies for more general reviews of environmental road effects (e.g. Andrews 1990; Bennett 1993; Forman & Alexander 1998; Spellerberg 1998; Trombulak & Frissell 2000; Seiler 2001; Carr et al. 2002; Forman et al. 2003; Coffin 2007).

The effects of roads can be direct or indirect, short-term or permanent and apply to spatially restricted locations or affect extensive areas. First, roads lead to an immediate loss of suitable habitat. These losses are often much greater than the actual area covered by roads, because of road-zone effects that make areas close to roads less suitable for certain species (e.g. due to noise, Parris & Schneider 2009; Parris et al. 2009; or artificial lighting, Outen 2002). However, roads can also create and enhance habitat for some species, particularly small mammals. Similarly, roads can serve as movement and dispersal corridors and thus increase movement rates and gene flow over long distances. Roads can also increase movement rates of (feral) predators, and contribute to the spread of infectious diseases and exotic species. More commonly, roads serve as barriers to individual movements, for example, through behavioural road avoidance, or when roads and fences along roads present physical obstacles. These barrier effects can make certain resources (e.g. mates, food, breeding sites) inaccessible for animals, thus affecting individual fitness and overall habitat quality. Road mortality has a similar barrier effect, but can additionally reduce population sizes directly. Finally, the road network increases landscape fragmentation, resulting in small and more or less isolated habitat patches.

In combination, road-induced habitat loss, barrier effects, mortality and landscape fragmentation can lead to increased extinction risks for wildlife populations (Fig. 1, Fahrig 2002; Jaeger 2004). In addition, these road effects can also lead to increased genetic structure and decreased genetic diversity, which further reduce population viability. Of these two components, genetic structure (i.e. the distribution of genetic variation) will usually respond faster to habitat fragmentation than genetic diversity (i.e. the amount of genetic variation; Keyghobadi 2007; Lowe et al. 2004).

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Figure 1.  Overview of major road impacts (left column) and their ecological (centre column) and genetic (right column) consequences. Adapted from Fahrig (2002) and Jaeger (2004).

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While most molecular ecologists are likely familiar with these general road effects, it is important to highlight that the different effects are highly intertwined and their relative magnitude depends on a multitude of factors, such as road-, species- and landscape-specific characteristics. Generally, wider roads with greater volumes of high-speed traffic affect wildlife populations more strongly than small, less travelled roads (e.g. Clevenger et al. 2001; Jaarsma et al. 2006). For example, traffic noise can substantially reduce breeding bird densities in proximity to busy highways (Reijnen & Foppen 2006; see also Parris & Schneider 2009; Slabbekoorn & Ripmeester 2008). However, McGregor et al. (2008) and Fahrig & Ford (2008) concluded that small mammals avoid crossing roads because of the road surface itself, and not because of associated traffic or noise. Also, Alexander et al. (2005) suggest that road mortalities increase up to a certain traffic threshold, beyond which only a few animals still attempt to cross the roads, so that road-kill frequencies decrease. While road mortalities and road avoidance can both lead to a decrease in (genetic) connectivity, distinguishing them is important for practical conservation. If connectivity is decreased because of road mortality, building exclusion fences is a suitable mitigation measure. However, fences can further exacerbate the situation if only a few animals attempt to cross the road in the first place (i.e. road avoidance; Jaeger et al. 2005; Jaeger & Fahrig 2004).

Clearly, species characteristics influence if and how road effects occur (Carr & Fahrig 2001; Aresco 2005; Ford & Fahrig 2007; Eigenbrod et al. 2008b). For example, Kerth & Melber (2009) found that roads are effective barriers for Bechstein’s bats (Myotis bechsteinii), while barbastelle bats (Barbastella barbastellus) frequently cross motorways. These findings can be explained by differences in foraging behaviour and wing morphology between these two species. Such species-specific road effects are also important for conservation planning, because different crossing structures are required to mitigate road effects on different species (Clevenger & Waltho 2005; Ascensao & Mira 2007; Grilo et al. 2008; Mata et al. 2008).

Characteristics of the surrounding landscape also influence the response of wildlife to roads (van der Grift & Pouwels 2006; Huijser & Clevenger 2006; Ng et al. 2008; Langen et al. 2009). For example, Grilo et al. (2009) found that road-kill rates for a variety of species were highest in areas with high quality habitat that experienced little human disturbance, because animals tend to cross roads more frequently if habitat on both sides is favourable. This also means that crossing structures imbedded in a suitable habitat usually receive greatest use by wildlife species (e.g. Ng et al. 2004; Clevenger & Waltho 2005).

Understanding secondary road effects can be particularly challenging. For example, Rytwinski & Fahrig (2007) found a positive relationship between abundance of white-footed mice (Peromyscus leucopus) and road densities, even though movement of the species is inhibited by roads. Rytwinski & Fahrig (2007) attribute this to negative effects of high road densities on the abundance of predator species, or possibly to positive (but unknown) road effects on habitat quality for mice. Similarly, Bissonette & Rosa (2009) did not detect any clear relationships between distance-to-roads and abundance or diversity of small mammal communities, probably because roads not only act as movement barriers, but also create suitable micro-habitats for the studied species (see also Fahrig & Rytwinski 2009).

Many road effects are also only detectable after a certain time lag. Generally, effects of habitat loss and degradation are detectable earliest, followed by the effects of road-kills and landscape fragmentation (Forman et al. 2003). However, it is also possible that the magnitude of different road effects varies over time. For example, Shepard et al. (2008) suggest that snake densities could initially be decreased by road mortalities, because snakes attempt to cross newly constructed roads. Over time, behavioural adaptation should lead to fewer road crossing attempts and stable densities, and reduced connectivity across roads. Road effects can also be dependent on seasonal variations in animal behaviour, for example, when movement rates increase during breeding seasons (e.g. van Wieren & Worm 2001; Bond & Jones 2008).

Accounting for these potentially confounding factors in (molecular) road ecology is crucial for improving our understanding of road effects and the effectiveness of mitigation measures. For example, Corlatti et al. (2009) note that incongruent results have been obtained from studies that evaluated the ability of wildlife overpasses to provide genetic connectivity. This is mainly because of a lack of long-term studies that combine different data types and account for confounding variables (Corlatti et al. 2009). Overall, analysing and interpreting road effects is a complex task, and predicting the exact mechanistic response of wildlife to roads and road mitigation measures can be challenging.

While many of these challenges also apply to research on general habitat loss and fragmentation, roads have certain unique characteristics that influence research in road ecology (Table 1). Roads lead to relatively little direct habitat loss, but the modification of habitat is extreme and it leads to extraordinarily sharp edges. This means that assessing edge and road-zone effects on habitat quality is particularly important. Also, many types of habitat conversion only lead to direct mortality during the actual conversion phase (e.g. during clear-cutting), while road-kills impact mortality rates indefinitely. As road characteristics influence how certain species respond to roads, it is also necessary to measure, report and analytically separate the different factors. Thus, simply distinguishing habitat from nonhabitat—as is commonly done in general fragmentation studies—seems particularly unsuitable for road ecology. As road networks cover large spatial areas, they are also more likely to have broad-scale effects than many other types of habitat loss and fragmentation. Furthermore, roads are likely to have indirect, long-term effects caused by road-related activities. Therefore, the overall virtual footprint of roads can be assessed only through long-term studies that focus on more than just local and direct road effects. Finally, road ecology can potentially have a more direct impact on practical conservation planning, because of increased agency efforts to include environmental considerations into road construction and mitigation (TRB 2002; Donaldson & Bennett 2004; Dolan et al. 2006; Karani 2008; Thorne et al. 2009). Overall, while theories and concepts derived from general research on habitat loss and fragmentation can apply to road effects, road ecology is unique in certain aspects (Table 1). This uniqueness of road ecology also offers many opportunities to address important questions with novel research approaches. In the next section, we briefly review studies that have used genetic data to assess road effects, and point out limitations of current studies in molecular road ecology.

Table 1.   Unique characteristics of road effects and road ecology research
Unique characteristics of roads and road effectsRelevance for road ecologyImplication for studies in (molecular) road ecology.
  1. [Correction added after online publication 25 September 2009: the table had its headings put in bold text and periods added after each entry.]

Roads lead to relatively little direct habitat loss, but habitat modification is extreme.Roads lead to extraordinarily sharp edges in the landscapes, and road-zone effects on habitat quality can potentially be more pronounced than edge effects caused by general habitat loss and fragmentation.In addition to assessing local (barrier) effects of roads, studies should evaluate how (genetic) estimates of habitat quality (e.g. population sizes, reproductive rates) change with varying proximity to roads.
Roads are associated with wildlife-traffic mortalities.In addition to the indirect mortality effects of general habitat loss and fragmentation, roads can directly impact mortality rates.Studies should attempt to determine the relative influence of road mortality vs. other road effects on observed (genetic) patterns.
Roads are associated with varying degrees of traffic, pollution, artificial lighting and noise.Specific road characteristics will influence how certain species are affected by roads.Studies should not treat roads as binary features that are either present or not. Instead, it is desirable to sample roads with a wide variety of characteristics and statistically evaluate the relative influence of different factors on observed (genetic) patterns.
Roads stretch over very large spatial distances.Roads are more likely to have broad-scale (e.g. landscape-level) effects than many other causes of habitat loss and fragmentation.Studies should specifically assess how roads/road densities affect wildlife species across broad spatial regions.
New roads enable humans and invasive species to access formerly remote areas.The cumulative effect of roads and road-related activities may be even stronger than the overall effect of general habitat loss and fragmentation.Studies should not only focus on direct, short-term and local road effects, but attempt to assess the virtual footprint of roads on population viability and species persistence.
From a policy and management perspective, there is an increased interest/need to mitigate ecological impacts of existing and future roads.Studying road effects and the effectiveness of mitigation measures can potentially have a more direct influence on conservation planning than research on general habitat loss and fragmentation.Studies should be designed and conducted in collaboration with transportation planners and focus on research questions of high relevance for practical transportation planning.

Review of studies using genetic data to assess roads effects

  1. Top of page
  2. Abstract
  3. Introduction
  4. Road effects on wildlife
  5. Review of studies using genetic data to assess roads effects
  6. Towards molecular road ecology
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

Our review of the literature identified 33 studies that used genetic data to detect effects of roads or urbanization (i.e. including road effects; see Table S1). We refer readers to Keyghobadi (2007) for a review of studies investigating the genetic effects of habitat fragmentation in general. The identified studies were mostly conducted on mammals (N = 15) and amphibians (N = 10), but also include one study on a reptile, and seven studies on different invertebrates. Sample sizes varied greatly across studies, ranging from 18 individuals (Zachos et al. 2007) to 1456 individuals (Johansson et al. 2005). Of the 33 studies, 25 used nuclear DNA microsatellite data, five used allozyme markers, five used mitochondrial DNA (mtDNA) sequences and one study used random amplified polymorphic DNA markers. Three studies combined multiple marker systems in their analyses.

The studies used a wide variety of analytical approaches, reflecting the great diversity of available analysis options for genetic data. Twenty studies conducted traditional (i.e. population-level) analyses (e.g. FST); eight studies used individual-based analysis approaches (e.g. clustering and spatial autocorrelation approaches) and five studies combined population- and individual-based analyses. Despite these differences, several general conclusions can be drawn from the reviewed studies.

First, the review illustrates that modern molecular approaches have high power to detect genetic and ecological road impacts, with all but four studies reporting significant road effects. Investigated roads had been constructed as little as c. 20 years ago, demonstrating that genetic analyses can address road-related research questions over relatively fine temporal scales. The relatively high proportion of studies reporting significant road effects may be a result of a simple publication bias. However, most studies did not solely investigate road effects on genetic variation, but instead included roads as one of several landscape features that could potentially affect genetic diversity and structure. Thus, most of the studies probably would have been published even if a greater percentage of them had not found significant effects of roads.

The reviewed studies also show that genetic data can be used to obtain detailed information about road effects. For example, various studies reported scale-, species- and sex-specific responses to roads (e.g. Reh & Seitz 1990; Mills & Conrey 2003; Proctor et al. 2005). Also, the studies demonstrate that genetic data can be used to evaluate the relative importance of road characteristics (size, age, traffic volume) in gene flow and resulting genetic structures (e.g. Gerlach & Musolf 2000; Vos et al. 2001a; Keller & Largiader 2003). Furthermore, it is possible to distinguish genetic road effects from other, confounding landscape influences, such as spatial, elevation or habitat gradients. Accounting for such confounding factors is possible through study design (e.g. Marsh et al. 2008) and sophisticated statistical analyses (e.g. Cushman et al. 2006). Also, several studies provide examples for distinguishing influences of historic and recent landscape patterns on observed genetic structures (Holzhauer et al. 2006; Ficetola et al. 2007; Vandergast et al. 2007). Finally, empirical studies also demonstrate that genetic effective population size influences the magnitude and detectability of genetic road effects. For example, Gauffre et al. (2008) demonstrated through simulations that genetic barrier effects are difficult to detect in species with large effective population sizes. On the other hand, Epps et al. (2005) used simulations to demonstrate that human infrastructures led to a rapid increase of genetic differentiation in desert bighorn sheep (Ovis canadensis nelsoni) because local populations had small effective population sizes. Thus, genetic diversity and structure are not only influenced by gene flow across roads, but also by the effective size of local populations within the road network.

All of these examples illustrate that genetic data can be used to assess population genetic consequences of roads, and more importantly, can increase our understanding of the underlying mechanisms. This is particularly true for studies that combine genetic analyses with nongenetic (i.e. field-based) approaches. For example, Riley et al. (2006) used radio-telemetry and genetic assignment methods to evaluate movement and gene flow of bobcats (Lynx rufus) and coyotes (Canis latrans) across a California freeway. Even though radio-telemetry suggested that individuals of both species were able to move across the road, the genetic data showed that the freeway had become a considerable impediment to gene flow. Riley et al. (2006) attribute this to road effects on home-range boundaries, and territory pile-up along the freeway, which made it difficult for crossing individuals to reproduce successfully. Thus, combining various data sources and research approaches can help clarify the exact response of individuals and populations to roads.

Limitations of reviewed studies

Overall, molecular approaches have been successfully applied to investigate certain road effects, and they are highly flexible with respect to study questions, sampling designs and analytical techniques. However, the review also identified several limitations that currently restrict the utility of molecular approaches in road ecology (Table S2). While 28 of the studies assessed barrier effects on genetic structure, only less than half of the reviewed studies (N = 16) also evaluated genetic diversity in relation to roads, and only four studies assessed road effects on effective population size. Furthermore, only six studies evaluated the relative importance of various road characteristics for observed genetic patterns, or accounted for potentially confounding (landscape or historical) factors. Also, only 12 studies used a study design in which results obtained in roadless areas could be compared with those obtained in areas with roads (‘Control-Impact’ study design). No study compared genetic variation before and after road construction, and only one study conducted multi-year monitoring to investigate long-term effects of roads (Tamura & Hayashi 2007). Only a single study explicitly addressed road-zone effects (Lesbarreres et al. 2003). Less than a quarter of the studies (N = 6) combined genetic and nongenetic data for their analyses. Finally, with the exception of two studies (Mills & Conrey 2003; Riley et al. 2006), all studies focused on a single species.

In sum, current studies address only a few of the many road effects described earlier and they often use suboptimal study and sampling designs. In the next section, we outline genetic approaches for assessing additional road effects on all levels of biodiversity, and give recommendations for future studies in molecular road ecology.

Towards molecular road ecology

  1. Top of page
  2. Abstract
  3. Introduction
  4. Road effects on wildlife
  5. Review of studies using genetic data to assess roads effects
  6. Towards molecular road ecology
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

Our review clearly demonstrates that molecular genetics can substantially contribute to road ecology research. However, instead of focusing primarily on barrier effects of roads on local genetic connectivity, molecular approaches should target a greater variety of road effects.

Road effects on different levels of biodiversity

Biodiversity can be measured at multiple levels of organization, for example, at the level of genes, population-species, community–ecosystems and landscapes (Noss 1990). At each level, compositional, structural and functional components can be distinguished. Compositional components describe the identity and variation of measured elements, for example, allelic or species richness. Structural components describe the spatial or temporal patterns of biodiversity, for example, species distributions or genetic structures. Finally, functional components include the ecological and evolutionary processes through which the different levels of organization interact with each other, and with their environment.

Modern laboratory techniques can be used to quantify genetic diversity and structure, or to identify species, sexes and individuals. This information can help investigate a large variety of road effects on biodiversity (Table 2):

Table 2.   Overview of potential molecular applications for assessing road effects on different levels and components of biodiversity
Level of organizationBiodiversity componentAttribute(s) of biodiversity componentMolecular application(s) to assess road effect
  1. [Correction added after online publication 25 September 2009: the table had its headings put in bold text.]

a) GeneticCompositionalPopulation-specific genetic diversityEstimates of genetic diversity (e.g. allelic richness, heterozygosity)
a) GeneticStructuralGenetic structurePopulation-specific FST, assignment and clustering analyses; analyses based on genetic distances among individuals, incl. spatial autocorrelation statistics
a) GeneticFunctionalGene flow; genetic effective population sizeEstimates of gene flow, genetic differentiation (e.g. FST), number of effective migrants (Nm), genetic effective population size (Ne)
b) Population-  speciesCompositionalSpecies presence/absenceSpecies identification via (noninvasive) genetic sampling (single species);
b) Population-  speciesStructuralAbundance/population size; sex-ratioNoninvasive genetic mark–recapture for identification of individuals; gender identification via (noninvasive) genetic sampling
b) Population-  speciesFunctionalPopulation growth; survival, reproduction; (sex-specific) movement/dispersalNoninvasive genetic mark–recapture for identification and temporal tracking of individuals and genetic effective and census population sizes; parentage and kinship analysis; endocrine analyses to determine pregnancy rates; assignment and clustering analyses; analyses based on genetic distances among individuals, incl. spatial autocorrelation statistics
c) Community–ecosystemCompositionalSpecies composition/communitiesSpecies identification via (noninvasive) genetic sampling (multiple species)
c) Community–ecosystemStructural/functional(Distribution of) Functional groupsSpecies identification via (noninvasive) genetic sampling (multiple species)
d) LandscapeStructural/functionalSpecies-specific landscape characteristics (e.g. habitat patches and fragmentation)Landscape genetics; genetically-scaled indices of landscape fragmentation
d) LandscapeFunctional(Meta-)Population connectivityAssignment, clustering, and admixture analyses; ‘traditional’ estimates of genetic differentiation such as Nm, FST, FIS, etc.

(a) Genetic level road effects: As reviewed in the previous section, molecular approaches are well-suited to study road effects on compositional and structural components at the genetic level (i.e. road effects on genetic diversity and structure, respectively). In addition, genetic data can also be used to assess road effects on functional components of genetic variation, for example, by estimating genetic effective population sizes, or number of effective migrants (e.g. Wang & Whitlock 2003; Palstra & Ruzzante 2008).

(b) Species-population level applications: Noninvasive genetic sampling (i.e. sampling genetic material without directly capturing or observing animals; Long et al. 2008; Taberlet et al. 1999; Waits & Paetkau 2005; Waits 2004) can be used to assess species presence/absence, and can thus help to evaluate road and road-zone effects on species-specific habitat suitability. For certain species (e.g. many carnivores), noninvasive genetic sampling can greatly increase detection rates and thus lead to more accurate inferences about species distribution and abundance (Solberg et al. 2006; Long et al. 2008). Genetic data can also be analysed within a mark–recapture framework, to identify individual animals and estimate population sizes and densities (e.g. Bellemain et al. 2005; Leberg 2005; Piggott et al. 2006; Kendall et al. 2008). Furthermore, such genetic mark–recapture analyses can track individual movements and provide estimates of survival rates (e.g. Flagstad et al. 2004; Piggott et al. 2006; Squires et al. 2007). Sex-determination from genetic data can be used to evaluate sex-specific barrier effects and also to detect changes in sex-ratios caused by roads (Aresco 2005). Genetic parentage and kinship analyses can provide insights into road-induced changes of reproductive rates and mating schemes (e.g. Wilson et al. 2002; Sorin 2004). Such road effects on reproduction could also be assessed via protein or hormone analyses that determine pregnancy rates (e.g. Czekala et al. 1994; Drew et al. 2001). Hormone analyses could also help to determine stress levels (e.g. Koren et al. 2002; Schwartz & Monfort 2008), which may be increased near roads because of noise and lighting (Outen 2002). Molecular approaches can also illuminate the spread of infectious diseases and invasive species that can be facilitated by roads (Pauchard & Alaback 2004; Hansen & Clevenger 2005; Miura 2007; Archie et al. 2009). Stable isotopes could also help to assess such road effects by tracking wildlife movement and migration patterns (Hobson & Wassenaar 2008). Finally, analysing genetic patterns of pathogens could help reveal road effects on animal movement and social interactions at very fine temporal scales (e.g. Biek et al. 2006).

The suggested molecular applications for species-population level analyses go far beyond most current applications for analysing road-effects on genetic variation. Instead, the applications use molecular genetic methods to address specific ecological research questions and hypotheses. Thus, they can lead to the same kind of information that can be obtained via traditional population assessment, but can also address questions that are very difficult or impossible to address without molecular genetic data. Addressing these questions can also yield the kind of information that is needed for practical transportation planning and conservation. As pointed out by Roedenbeck et al. (2007), road ecology currently has comparatively little impact on transportation planning and should therefore target research questions of high practical relevance, for example, with respect to road effects on population persistence or regarding the functionality of mitigation measures. Genetic data can be used to address these questions, because they are closely tied to successful reproduction and population viability and because the relative ease of data collection for certain species allows researchers to compare the effectiveness of different mitigation measures at many locations within a single study (e.g. Kuehn et al. 2007).

(c) Community–ecosystem level applications: Genetic identification of multiple target species can also assess road-related changes in species composition. This seems particularly important for achieving the goals of multi-species conservation approaches (e.g. Barrows et al. 2005; Early & Thomas 2007; Noon et al. 2008). Such multi-species approaches could potentially target entire guilds or functional groups (Blondel 2003; Bishop & Myers 2005) and thus allow inferences about road effects on ecosystem functions. Multi-species studies could also be conducted with nongenetic data, but molecular approaches may greatly facilitate them, at least for certain species (Waits 2004; Long et al. 2008). The emerging field of ecosystem and community genetics and genomics may also provide novel opportunities to study the effects of road-induced changes in genetic variation on species interactions and community structures (Whitham et al. 2006, 2008).

(d) Landscape-level applications: At the landscape level, biodiversity indicators include measures of patch type and distribution, fragmentation indices or rates of transfer among communities and patches in the landscape (Noss 2006). Ideally, many of these indicators should be scaled to the specific characteristics of different species. For example, a certain landscape may be highly fragmented from the perspective of one species, yet be entirely connected for another. Similarly, defining an ecologically meaningful patch depends on species-specific habitat relationships and area requirements. Molecular approaches can help derive meaningful indicators of landscape-level biodiversity, for example, by determining levels of functional (i.e. genetic) connectivity for various species inhabiting a certain landscape. Landscape genetics provide novel and improved statistical methods for analysing and interpreting genetic data over large spatial scales and for linking observed genetic patterns to landscape features (Manel et al. 2003; Storfer et al. 2007; Holderegger & Wagner 2008). Thus, landscape genetic methods are especially well-suited for landscape-level applications in molecular road ecology. For instance, genetic data can be used to estimate dispersal distances, which are needed to parameterize ecologically scaled landscape indices (e.g. Vos et al. 2001b; Moilanen & Nieminen 2002). Such indices are often used to determine whether specific patches of suitable habitat within a landscapes are isolated from other patches because of matrix effects, and to evaluate the importance of specific patches to (meta-)population connectivity and viability (e.g. James et al. 2005; Pascual-Hortal & Saura 2006; Minor & Urban 2007). Finally, genetic data can be collected over large spatial extents, and sample sizes are often in the hundreds or even thousands (Table S1). This makes genetic approaches especially interesting for broad-scale analyses that are necessary for assessing landscape-level road effects on biodiversity. Such broad-scale, landscape-level applications of molecular road ecology are also very valuable for practical transportation planning. Indeed, landscape genetic approaches have already been used to identify optimal placement for wildlife movement corridors to mitigate (road-induced) landscape fragmentation (Epps et al. 2007; Beier et al. 2008; Cushman et al. 2009).

Inferential strength and study design in molecular road ecology

The inferential strength of genetic studies generally depends on optimal choice of markers and loci, sound laboratory protocols, sufficient sample sizes and adequate data analysis. While these issues have been discussed in detail in the molecular ecology literature (e.g. Taberlet et al. 1999; Kalinowski 2002, 2005; McKelvey & Schwartz 2004; Paetkau 2004; Balkenhol et al. in press), other considerations have received far less attention.

First, inferential strength of a study increases with the number of competing hypotheses tested because data obtained from a certain study always could have resulted from the influences of multiple factors, each reflecting a different hypothesis. As demonstrated by some of the reviewed studies, molecular approaches are well-suited for teasing apart the complex and inter-related impacts of multiple landscape characteristics on genetic patterns (e.g. Reh & Seitz 1990; Cushman et al. 2006; Arens et al. 2007). Understanding the relative importance of these various road characteristics will require such research that specifically targets a wide range of road characteristics within multi-factorial study designs. Genetic data also provide opportunities to test for historical influences, for example, through the combined use of molecular markers with varying temporal resolutions (e.g. mtDNA for assessing more historic broad-scale influences; nuclear microsatellites for assessing more recent fine-scale influences).

Second, data in road ecology should be gathered and analysed within a before-after-control-impact (BACI) study design, whenever possible. In this study design, data are gathered before and after roads or road-mitigation measures are constructed. Also, data from areas with roads or mitigation measures (impact) are compared with data obtained from areas without such structures (control). This study design may be difficult to implement in practice, but it greatly increases the inferential strength of road ecological research. When a full BACI study is not feasible, before-after (BA) or control-impact (CI) study designs are often possible. While not as optimal as the full BACI design, studies following a BA or CI design are still suitable for addressing research questions in road ecology, and Roedenbeck et al. (2007) discuss their respective advantages and limitations in detail.

Finally, the inferential strength of molecular applications in road ecology will be particularly high in combination with other field-based research approaches. Combining different approaches and techniques to address the same research questions can substantially increase the certainty of inferences and yield an in-depth understanding of affected processes (e.g. Tallmon et al. 2002; Cullingham et al. 2008). For example, two populations that have recently become isolated from another by a road may counterbalance decreased immigration rates with increased reproduction rates. In this case, a conventional field census may not detect any road effects, because local population sizes seem unaffected. However, a genetic study could detect a change in the number of effective breeders and also estimate the number of effective migrants via assignment tests. Thus, conducting a traditional population census in conjunction with genetic applications can identify cryptic population responses to roads, which may be important for long-term conservation. Similarly, genetics can determine if individuals move and reproduce across roads, but it does not necessarily provide the resolution to identify exact locations or timings of crossings. Furthermore, because of lack of spatio-temporal detail, genetic data do not improve our understanding of individual behavioural responses to roads (e.g. Graves et al. 2007; Lewis 2007). Thus, genetic and tracking data (e.g. obtained from GPS-collars) should be combined more often to understand and mitigate road effects better.

Recommendations for future research

Overall, we suggest the following research strategies for future efforts in molecular road ecology.

  • 1
    Assess road effects on all levels of biodiversity—Future studies in molecular road ecology should target road effects at all levels and components of biodiversity. Also, researchers should design studies that specifically evaluate road impacts on multiple species, or even on entire guilds and functional groups.
  • 2
    Focus on research questions with direct relevance to transportation planning—Future studies should also attempt to provide planners with practical advice on how to avoid or mitigate negative road effects on biodiversity. More specifically, researchers should correspond with relevant decision makers and discuss what kind of information would be most useful from a practical planning standpoint.
  • 3
    Account for confounding factors—To separate true road effects from other (e.g. landscape and historical) influences, future studies should pay greater attention to choosing appropriate control areas, multi-factorial study designs and multivariate analytical approaches.
  • 4
    Assess landscape-scale road effects—In addition to analysing local road impacts, increased efforts for evaluating broad-scale road effects are needed. Such studies would ideally use landscapes as the sampling unit, and should quantify road-induced fragmentation within each sampled landscape, for example, by relating estimates of genetic structure and (effective) population sizes to road densities, effective mesh sizes (Jaeger 2000; Moser et al. 2007) or accessible habitat (Eigenbrod et al. 2008a).
  • 5
    Measure road characteristics—Broad-scale sampling will also enable researchers to include roads with various characteristics (e.g. various widths, ages, traffic volumes) in their studies. Future research should actively attempt to sample these different road attributes and statistically evaluate their relative importance.
  • 6
    Use landscape genetic approaches—Landscape genetic approaches are particularly well-suited for accomplishing recommendations 3–5 and should therefore receive increased attention in molecular road ecology.
  • 7
    Assess both within- and between-population road effects—Local population viability is influenced by within- and between-patch processes, but current studies seldom assess patch-specific influences and instead focus on barrier effects of roads. Future genetic studies should quantify population-specific responses to roads (e.g. census and effective population sizes; number of effective breeders) in addition to estimating connectivity.
  • 8
    Combine molecular and field-based approaches—Utilizing multiple research approaches (e.g. genetics, hormone analyses, telemetry, mark–recapture) will increase our understanding of the consequences and underlying (behavioural) mechanisms of road impacts. This will also lead to more appropriate mitigation measures.
  • 9
    Conduct long-term studies—Ideally, the effects of roads and mitigation measures should be evaluated over extensive time periods, in which population parameters are repeatedly measured and analysed with the same (i.e. genetic and nongenetic) techniques (Schwartz et al. 2007). Such monitoring is necessary to understand fully the effects of roads on long-term population persistence.

We acknowledge that it would be very challenging to incorporate all of these suggestions in a single project and several of the recommendations may even be mutually exclusive for practical reasons. Nevertheless, many of our recommendations could be realized through collaborative research efforts in which multiple scientists work together under a common, road-related research theme. These projects could either use different research approaches for analysing road effects in the same area or use the same research approaches in different areas, and for different species. Clearly, such meta-projects would require a substantial amount of communication and coordination among all involved parties and a comparison and synthesis of derived information. However, many of the most interesting and most important research questions related to road effects warrant such a synthesis of projects that encompass a wide variety of circumstances and species (Roedenbeck et al. 2007).

Conclusions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Road effects on wildlife
  5. Review of studies using genetic data to assess roads effects
  6. Towards molecular road ecology
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

In sum, molecular approaches can be highly informative for road ecological research, but their full potential is yet to be realized. We do not argue that genetics is suitable for all research questions in road ecology or that it should replace other scientific approaches. Instead, we propose that genetics can fill in some pieces of the larger puzzle and that it should be combined with the many other research approaches for investigating road effects. The potential contributions of genetic approaches to road ecology research and transportation planning should be carefully evaluated and discussed among road ecologists, geneticists and transportation planners before designing road ecological studies for each particular project.

Vernesi et al. (2008) recently called for a greater use of genetic approaches in practical conservation and highlighted the need for applying molecular techniques to real-life problems. As illustrated throughout this review, molecular road ecology provides an exciting opportunity for conducting genetic research with high relevance for practical biodiversity conservation. We believe that creative and innovative thinking, combined with a sound understanding of road ecology relationships and their genetic signatures, will be vital to the future development of molecular road ecology. Interdisciplinary collaborations are necessary to ensure that genetic techniques are used correctly and efficiently in road ecology, and to improve current study designs and analytical approaches. We hope that this review will stimulate discussion among road ecologists, transportation planners and geneticists, and that future developments in molecular road ecology will help find feasible and effective solutions for environmentally sustainable transportation planning.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Road effects on wildlife
  5. Review of studies using genetic data to assess roads effects
  6. Towards molecular road ecology
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

We would like to thank Robert Long, Kelly McAllister, Peter Singleton, and the other members of the Washington Wildlife Habitat Connectivity Working Group for providing us with the initial motivation to explore molecular approaches in road ecology. This manuscript was substantially improved by comments from the Waits lab group, three anonymous reviewers and Louis Bernatchez. Also many thanks to Rolf Holderegger for his valuable support and feedback. [Correction added after online publication 25 September 2009: the Acknowlegments Section was included.]

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Road effects on wildlife
  5. Review of studies using genetic data to assess roads effects
  6. Towards molecular road ecology
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information
  • Alexander SM, Waters NN, Paquet PC (2005) Traffic volume and highway permeability for a mammalian community in the Canadian Rocky Mountains. Candian Geographer, 49, 321331.
  • Andrews A (1990) Fragmentation of habitat by roads and utility corridors: a review. Australian Zoologist, 26, 130141.
  • Archie EA, Luikart G, Ezenwa VO (2009) Infecting epidemiology with genetics: a new frontier in disease ecology. Trends in Ecology and Evolution, 24, 2130.
  • Arens P, Van Der Sluis T, Van’t Westende WPC et al. (2007) Genetic population differentiation and connectivity among fragmented Moor frog (Rana arvalis) populations in The Netherlands. Landscape Ecology, 22, 14891500.
  • Aresco MJ (2005) The effect of sex-specific terrestrial movements and roads on the sex ratio of freshwater turtles. Biological Conservation, 123, 3744.
  • Ascensao F, Mira A (2007) Factors affecting culvert use by vertebrates along two stretches of road in southern Portugal. Ecological Research, 22, 5766.
  • Balkenhol N, Waits LP, Dezzani R (in press) Statistical approaches in landscape genetics: An evaluation of methods for linking landscape and genetic data. Ecography. doi: DOI: 10.1111/j.1600-0587.2009.05807.x.
  • Barrows CW, Swartz MB, Hodges WL et al. (2005) A framework for monitoring multiple-species conservation plans. Journal of Wildlife Management, 69, 13331345.
  • Beier P, Majka DR, Spencer WD (2008) Forks in the road: Choices in procedures for designing wildland linkages. Conservation Biology, 22, 836851.
  • Bellemain E, Swenson JE, Tallmon D, Brunberg S, Taberlet P (2005) Estimating population size of elusive animals with DNA from hunter-collected feces: four methods for brown bears. Conservation Biology, 19, 150161.
  • Bennett AF (1993) Roads, roadsides and wildlife conservation: a review. In: Nature Conservation 2: The Role of Corridors (eds SaundersDA, HobbsRJ), pp. 99118. University of Minnesota Press, Minneapolis, MN.
  • Biek R, Drummond AJ, Poss M (2006) A virus reveals population structure and recent demographic history of its carnivore host. Science, 311, 538541.
  • Bishop JA, Myers WL (2005) Associations between avian functional guild response and regional landscape properties for conservation planning. Ecological Indicators, 5, 3348.
  • Bissonette JA, Rosa SA (2009) Road zone effects in small-mammal communities. Ecology and Society, 14, 27. Available from http://www.ecologyandsociety.org/vol14/iss1/art27/.
  • Blondel J (2003) Guilds or functional groups: does it matter? Oikos, 100, 223231.
  • Bond AR, Jones DN (2008) Temporal trends in use of fauna-friendly underpasses and overpasses. Wildlife Research, 35, 103112.
  • Carr LW, Fahrig L (2001) Effect of road traffic on two amphibian species of differing vagility. Conservation Biology, 15, 10711078.
  • Carr LW, Fahrig L, Pope SE (2002) Impacts of landscape transformation by roads. In: Applying Landscape Ecology in Biological Conservation (ed. GutzwillerKJ), pp. 225243. Springer, New York.
  • CIA (2005) The World Factbook: 2005. Central Intelligence Agency/Potomac Books Inc., Dulles, VA, USA.
  • Clevenger AP (2005) Conservation value of wildlife crossings: measures of performance and research directions. GAIA, 14, 124129.
  • Clevenger AP, Waltho N (2005) Performance indices to identify attributes of highway crossing structures facilitating movement of large mammals. Biological Conservation, 121, 453464.
  • Clevenger AP, Chruszcz B, Gunson K (2001) Drainage culverts as habitat linkages and factors affecting passage by mammals. Journal of Applied Ecology, 38, 13401349.
  • Coffin AW (2007) From roadkill to road ecology: a review of the ecological effects of roads. Journal of Transport Geography, 15, 394396.
  • Corlatti L, Hackländer K, Frey-Roos F (2009) Ability of wildlife overpasses to provide connectivity and prevent genetic isolation. Conservation Genetics, 23, 548556.
  • Cullingham C, Pond B, Kyle C et al. (2008) Combining direct and indirect genetic methods to estimate dispersal for informing wildlife disease management decisions. Molecular Ecology, 17, 48744886.
  • Cushman SA, McKelvey KS, Hayden J, Schwartz MK (2006) Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. The American Naturalist, 168, 486499.
  • Cushman SA, McKelvey KS, Schwartz MK (2009) Use of empirically derived source-destination models to map regional conservation corridors. Conservation Biology, 23, 368376.
  • Czekala NM, Durrant BS, Callison L, Williams M, Millard S (1994) Fecal steroid hormone analysis as an indicator of reproductive function in the cheetah (Acinonyx jubatus). Zoo Biology, 13, 119128.
  • DeYoung RW (2007) Genetics and applied management: using genetic methods to solve emerging wildlife management problems. In: Frontiers in Wildlife Science: Linking Theory and Management Application (eds FulbrightTE, HewittDG), pp. 315334. CRC Press, Boca Raton, FL.
  • Dolan LMJ, Van Bohemen H, Whelan P et al. (2006) Towards the sustainable development of modern road ecosystems. In: The Ecology of Transportation: Managing Mobility for the Environment (eds DavenportJ, DavenportJL), pp. 255–274. Springer, Dordrecht, The Netherlands.
  • Donaldson A, Bennett AF (2004) Ecological Effects of Roads. Implications for the Internal Fragmentation of Australian Parks and Reserves. Parks Victoria Technical Series. Parks Victoria, Melbourne.
  • Drew ML, Bleich VC, Torres SG, Sasser RG (2001) Early pregnancy detection in mountain sheep using a pregnancy-specific protein B assay. Wildlife Society Bulletin, 29, 11821185.
  • Early R, Thomas CD (2007) Multispecies conservation planning: identifying landscapes for the conservation of viable populations using local and continental species priorities. Journal of Applied Ecology, 44, 253262.
  • Eigenbrod F, Hecnar SJ, Fahrig L (2008a) Accessible habitat: an improved measure of the effects of habitat loss and roads on wildlife. Landscape Ecology, 23, 159168.
  • Eigenbrod F, Hecnar SJ, Fahrig L (2008b) The relative effects of road traffic and forest cover on anuran populations. Biological Conservation, 141, 3546.
  • Ellenberg H, Muller K, Stottele T (1981) Straßen-Ökologie: Auswirkungen von Autobahnen und Strasse auf Ökosysteme deutscher Landschaften. Broschürenreihe der Deutschen Strassenliga, Ausgabe 3, Bonn, Germany.
  • Epps C, Palsboll P, JD W et al. (2005) Highways block gene flow and cause rapid decline in genetic diversity of desert bighorn sheep. Ecology Letters, 8, 10291038.
  • Epps C, Wehausen JD, Bleich VC, Torres SG, Brahsares JS (2007) Optimizing dispersal and corridor models using landscape genetics. Journal of Applied Ecology, 44, 714724.
  • Fahrig L (2002) Animal populations and roads. In: Proceedings of the International Conference on Ecology and Transportation and the Environment (eds EvinkGL, GarretP, ZeiglerD), pp. 911. The Center for Transportation and the Environment at North Carolina State University, Raleigh, VA.
  • Fahrig L, Ford AT (2008) Movement patterns of eastern chipmunks (Tamias striatus) near roads. Journal of Mammalogy, 89, 895903.
  • Fahrig L, Rytwinski T (2009) Effects of roads on animal abundance: an empirical review and synthesis. Ecology and Society, 14, 21. Available from http://www.ecologyandsociety.org/vol14/iss1/art21/.
  • Ficetola GF, Garner TWJ, De Bernardi F (2007) Genetic diversity, but not hatching success, is jointly affected by postglacial colonization and isolation in the threatened frog, Rana latastei. Molecular Ecology, 16, 17871797.
  • Flagstad O, Hedmark E, Landa A et al. (2004) Colonization history and noninvasive monitoring of a reestablished wolverine population. Conservation Biology, 18, 676688.
  • Ford AT, Fahrig L (2007) Diet and body size of North American mammal road mortalities. Transportation Research Part D, 12, 498505.
  • Forman RTT (2000) Estimate of the area affected ecologically by the road system in the United States. Conservation Biology, 14, 3135.
  • Forman RTT, Alexander LE (1998) Roads and their major ecological effects. Annual Review of Ecology and Systematics, 29, 207231.
  • Forman RTT, Sperling D, Bissonette JA et al. (2003) Road Ecology. Science and Solutions. Island Press, Washington, DC.
  • Gauffre B, Estoup A, Bretagnolle V, Cosson JF (2008) Spatial genetic structure of a small rodent in a heterogeneous landscape. Molecular Ecology, 17, 46194629.
  • Geffen E, Luikart G, Waples RS (2007) Impacts of modern molecular genetic techniques on conservation biology. In: Key Topics in Conservation Biology (eds MacdonaldDW, ServiceK), pp. 4663. Blackwell Publishing, Oxford, UK.
  • Gerlach G, Musolf K (2000) Fragmentation of landscape as a cause for genetic subdivision in bank voles. Conservation Biology, 14, 10661074.
  • Graves TA, Farley S, Goldstein M, Servheen C (2007) Identification of functional corridors with movement characteristics of brown bears on the Kenai Peninsula, Alaska. Landscape Ecology, 22, 765772.
  • Van Der Grift EA, Pouwels R (2006) Restoring habitat connectivity across transport corridors: identifying high-priority locations for de-fragmentation with the use of an expert-based model. In: The Ecology of Transportation: Managing Mobility for the Environment (eds DavenportJ, DavenportJL), pp. 205232. Springer, Dordrecht, The Netherlands.
  • Grilo C, Bissonette JA, Santos-Reis M (2008) Response of carnivores to existing highway culverts and underpasses: implications for road planning and mitigation. Biodiversity and Conservtion, 17, 16851699.
  • Grilo C, Bissonette JA, Santos-Reis M (2009) Spatial-temporal patterns in Mediterranean carnivore casualties: consequences for mitigation. Biological Conservation, 142, 301313.
  • Hansen MJ, Clevenger AP (2005) The influence of disturbance and habitat on the presence of non-native plant species along transportation corridors. Biological Conservation, 125, 249259.
  • Hardy A, Clevenger AP, Huijser M, Neale G (2003) An overview of methods and approaches for evaluating the effectiveness of wildlife crossing structures: emphasizing the science in applied science. In: Proceedings of the 2003 International Conference on Ecology and Transportation (eds IrwinCL, GarrettP, McDermottKP), pp. 319330. Center for Transportation and the Environment, North Carolina State University, Raleigh, NC.
  • Hobson KA, Wassenaar LI (2008) Tracking Animal Migration with Stable Isotopes. Elsevier, Amsterdam, The Netherlands.
  • Hodson NL (1966) A survey of road mortality in mammals (and including data for the grass snake and common frog). Journal of Zoology, 148, 576579.
  • Holderegger R, Wagner HH (2008) Landscape genetics. BioSciences, 58, 199207.
  • Holzhauer SIJ, Ekschmitt K, Sander A-C, Dauber J, Wolters V (2006) Effect of historic landscape change on the genetic structure of the bush-cricket Metrioptera roeseli. Landscape Ecology, 21, 891899.
  • Huey LM (1941) Mammalian invasion via the highway. Journal of Mammalogy, 22, 283285.
  • Huijser MP, Clevenger AP (2006) Habitat and corridor function of right-of-way. In: The Ecology of Transportation: Managing Mobility for the Environment (eds DavenportJ, DavenportJL), pp. 233254. Springer, Dordrecht, The Netherlands.
  • Jaarsma CF, Van Langevelde F, Botma H (2006) Falttened faune and mitigation: traffic victims related to road, traffic, vehicle, and species characteristics. Transportation Research Part D-Transport and Environment, 11, 264276.
  • Jaeger JAG (2000) Landscape division, splitting index, and effective mesh size: new measures of landscape fragmentation. Landscape Ecology, 15, 115130.
  • Jaeger JAG (2004) Zerschneidung der Landschaft durch Verkehrswege und Siedlungsbereiche. In: Handbuch Naturschutz und Landschaftspflege. VII-12, 14. Erg. Lfg. 12/04 (eds KonoldW, BoekerR, HampickeU), pp. 136. Wiley-VCH, Weinheim,
  • Jaeger JAG, Fahrig L (2004) Effects of road fencing on population persistence. Conservation Biology, 18, 16511657.
  • Jaeger JAG, Bowman J, Brennan J et al. (2005) Predicting when animal populations are at risk from roads: an interactove model of road avoidance behavior. Ecological Modelling, 185, 329348.
  • Jaeger JAG, Scharz-von Raumer H-G, Esswein H, Müller M, Schmidt-Lüttmann M (2007) Time series of landscape fragmentation caused by transportation infrastructure and urban development: a case study from Baden-Württemberg (Germany). Ecology and Society, 12, 22. Available from http://www.ecologyandsociety.org/vol12/iss1/art22/.
  • James P, Rayfield B, Fortin M-J, Fall A, Farley G (2005) Reserve network design combining spatial graph theory and species’ spatial requirements. Geomatica, 59, 323333.
  • Johansson M, Primmer CR, Sahlsten J, Merila J (2005) The influence of landscape structure on occurrence, abundance and genetic diversity of the common frog, Rana temporaria. Gloal Change Biology, 11, 16641679.
  • Kalinowski ST (2002) How many alleles per locus should be used to estimate genetic distance? Heredity, 88, 6265.
  • Kalinowski ST (2005) Do polymorphic loci require larger sample sizes to estimate genetic distances? Heredity, 94, 3336.
  • Karani P (2008) Impacts of Roads on the Environment on South Africa. DBSA—Development Bank of Southern Africa, Midrand, South Africa. Available from http://www.dbsa.org/Research/Pages/Articles.aspx.
  • Keller I, Largiader CR (2003) Recent habitat fragmentation caused by major roads leads to reduction of gene flow and loss of genetic variability in ground beetles. Proceedings of the Royal Society of London. Series B, 270, 417423.
  • Kendall KC, Stetz JB, Roon DA et al. (2008) Grizzly bear density in Glacier National Park, Montana. Journal of Wildlife Management, 72, 16931705.
  • Kerth G, Melber M (2009) Species-specific barrier effects of a motorway on the habitat use of two threatened forest-living bat species. Biological Conservation, 142, 270279.
  • Keyghobadi N (2007) The genetic implications of habitat fragmentation for animals. Canadian Journal of Zoology, 85, 10491064.
  • Kirsten ME (2006) DBSA Infrastructure Barometer 2006: Economic and Municipal Infrastructure in South Africa. DBSA—Development Bank of South Africa, Midrand, South Africa.
  • Koren L, Mokady O, Karaskov T et al. (2002) A novel method using hair for determining hormonal levels in wildlife. Animal Behaviour, 63, 403406.
  • Kuehn R, Hindenlang KE, Holzgang O et al. (2007) Genetic effects of transportation infrastructure on roe deer populations (Capreolus capreolus). Jounal of Heredity, 98, 1322.
  • Langen TA, Ogden KM, Schwartking LL (2009) Predicting hot spots of herpetofauna road mortality along highway networks. Journal of Wildlife Management, 73, 104114.
  • Leberg P (2005) Approaches for estimating the effective size of populations. Journal of Wildlife Management, 69, 13851399.
  • Lesbarreres D, Pagano A, Lode T (2003) Inbreeding and road effect zone in a Ranidae: the case of Agile frog, Rana dalmatina Bonaparte, 1840. C. R. Biologies, 326, S68S72.
  • Lewis JS (2007) Effects of Human Influences on Black Bear Habitat Selection and Movement Patterns. University of Idaho, Moscow, ID.
  • Long RA, MacCay P, Zielinski WJ, Ray JC (2008) Noninvasive Survey Methods for Carnivores. Island Press, Washington, DC, USA.
  • Lowe A, Harris S, Ashton P (2004) Ecological Genetics: Design, Analysis, and Application. Wiley-Blackwell, Malden, MA.
  • Mader HJ (1984) Animal habitat isolation by roads and agricultural fields. Biological Conservation, 29, 8196.
  • Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends in Ecology and Evolution, 18, 189197.
  • Marsh DM, Page RB, Hanllon TJ et al. (2008) Effects of roads on patterns of genetic differentiation in red-backed salamanders, Plethodon cinereus. Conservation Genetics, 9, 603613.
  • Mata C, Hervas I, Herranz J, Suarez F, Malo JE (2008) Are motorway wildlife passages worth building? Vertebrate use of road-crossing structures on a Spanish motorway. Journal of Environmental Management, 88, 407415.
  • McGregor RL, Bender DJ, Fahrig L (2008) Do small mammals avoid roads because of traffic? Journal of Applied Ecology, 45, 117123.
  • McKelvey KS, Schwartz MK (2004) Genetic errors associated with population estimation using non-invasive molecular tagging: problems and new solutions. Journal of Wildlife Management, 68, 439448.
  • Mills LS, Conrey RY (2003) Highways as Potential Barriers to Movement and Genetic Exchange in Small Mammals. Final Report. Montana Department of Transportation, Helena, MT.
  • Minor ES, Urban DL (2007) Graph theory as a proxy for spatially explicit population models in conservation planning. Ecological Applications, 17, 17711782.
  • Miura O (2007) Molecular genetic approaches to elucidate the ecological and evolutionary issues associated with biological invasions. Ecological Research, 22, 876883.
  • Moilanen A, Nieminen M (2002) Simple connectivity measures in spatial ecology. Ecology, 83, 11311145.
  • Moser B, Jaeger JAG, Tappeiner U, Tasser E, Eiselt B (2007) Modification of the effective mesh size for measuring landscape fragmentation to solve the boundary problem. Landscape Ecology, 22, 447459.
  • Ng SJ, Dole JW, Sauvajot RM, Riley SPD, Valone TJ (2004) Use of highway undercrossings by wildlife in southern California. Biological Conservation, 115, 499507.
  • Ng JW, Nielsen C, St. Clair CC (2008) Landscape and traffic factors influencing deer–vehicle collisions in an urban environment. Human–Wildlife Conflicts, 2, 3447.
  • Noon BR, McKelvey KS, Dickson BG (2008) Multispecies conservation planning on U.S. federal lands. In: Models for Planning Wildlife Conservation in Large Landscape (eds MillspaughJJ, ThompsonFR), pp. 5183. Elsevier, Amsterdam, The Netherlands.
  • Noss RF (1990) Indicators for monitoring biodiversity: a hierarchical approach. Conservation Biology, 4, 355364.
  • Noss RF (2006) Essay 2.1—hierarchical indicators for monitoring changes in biodiversity. In: Principles in Conservation Biology (eds GroomMJ, MeffeGK, CarrollCR), pp. 2829. Sinauer Associates, Inc., Sunderland, MA.
  • Outen AR (2002) The ecological effects of road lighting. In: Wildlife and Roads. The Ecological Impact (eds SherwoodB, CutlerD, BurtonJA), pp. 133155. Imperial College Press, London, UK.
  • Paetkau D (2004) The optimal number of markers in genetic capture-mark-recapture studies. Journal of Wildlife Management, 68, 449452.
  • Palstra FP, Ruzzante DE (2008) Genetic estimates of contemporary effective population size: what can they tell us about the importance of genetic stochasticity for wild population persistence? Molecular Ecology, 17, 34283447.
  • Parris KM, Schneider A (2009) Impacts of traffic noise and traffic volume on birds of roadside habitats. Ecology and Society, 14, 29. Available from http://www.ecologyandsociety.org/vol14/iss1/art29/.
  • Parris KM, Velik-Lord M, North JMA (2009) Frogs call at a higher pitch in traffic noise. Ecology and Society, 14, 25. Available from http://www.ecologyandsociety.org/vol14/iss1/art25/.
  • Pascual-Hortal L, Saura S (2006) Comparison and development of new graph-based landscape connectivity indices: towards the priorization of habitat patches and corridors for conservation. Landscape Ecology, 21, 959967.
  • Pauchard A, Alaback PB (2004) Influence of elevation, land use, and landscape context on patterns of alien plant invasions along roadsides in protected areas of south-central Chile. Conservation Biology, 18, 238248.
  • Piggott MP, Banks SC, Stonr N, Banffy C, Taylor AC (2006) Estimating popullation size of endangered brush-tailed rock-wallaby (Petrogale penicillata) colonies using faecel DNA. Molecular Ecology, 15, 8191.
  • Proctor MF, McLellan BN, Strobeck C, Barclay RMR (2005) Genetic analysis reveals demographic fragmentation of grizzly bears yielding vulnerably small populations. Proceedings of the Royal Society of London. Series B, 272, 24092416.
  • Reh W, Seitz A (1990) The influence of land use on the genetic structure of populations of the common Frog Rana temporaria. Biological Conservation, 54, 239249.
  • Reijnen R, Foppen R (2006) Impact of road traffic on breeding bird populations. In: The Ecology of Transportation: Managing Mobility for the Environment (eds DavenportJ, DavenportJL), pp. 255274. Springer, Dordrecht, The Netherlands.
  • Riiters KH, Wickham JD (2003) How far to the nearest road? Frontiers in Ecology and the Environment, 1, 125129.
  • Riley SPD, Polinger JP, Sauvajot RM et al. (2006) A southern California freeway is a physical and spcial barrier to gene flow in carnivores. Molecular Ecology, 15, 17331741.
  • Roedenbeck IA, Fahrig L, Findlay CS et al. (2007) The Raischholzhausen agenda for road ecology. Ecology and Society, 12, 11. Available from http://www.ecologyandsociety.org/vol12/iss1/art11/.
  • Rytwinski T, Fahrig L (2007) Effect of road density on abundance of white-footed mice. Landscape Ecology, 22, 15011512.
  • Schwartz MK, Monfort SL (2008) Genetic and endocrine tools for carnivore surveys. In: Noninvasive Survey Methods for Carnivore (eds LongRA, MacKayP, ZielinskiWJ, RayJC), pp. 238262. Island Press, Washington, DC.
  • Schwartz MK, Luikart G, Waples RS (2007) Genetic monitoring as a promising tool for conservation and management. Trends in Ecology and Evolution, 22, 2533.
  • Seiler A (2001) Ecological Effetcs of Roads—A Review. Introductory Research Essay. SLU—Department of Conservation Biology, Uppsala, Sweden.
  • Shepard DB, Kuhns AR, Dreslik MJ, Phillips CA (2008) Roads as barriers to animal movement in fragmented landscapes. Animal Conservation, 11, 288296.
  • Slabbekoorn H, Ripmeester AP (2008) Birdsong and anthropogenic noise: implications and applications for conservation. Molecular Ecology, 17, 7283.
  • Solberg KH, Bellemain E, Drageset O-M, Taberlet P, Swenson EL (2006) An evaluation of field and non-invasive genetic methods to estimate brown bear (Ursus arctos) population size. Biological Conservation, 128, 158168.
  • Sorin AB (2004) Paternity assignment for white-tailed deer (Odocoileus virginianus): mating across age classes and multiple paternity. Jounal of Mammalogy, 85, 356362.
  • Spellerberg IF (1998) Ecological effects of roads and traffic: a literature review. Global Ecology and Biogeography Letters, 7, 317333.
  • Squires JR, Copeland JP, Ulizio TJ, Schwartz MK, Ruggiero LF (2007) Sources and patterns of Wolverine mortality in Western Montana. Journal of Wildlife Management, 71, 22132220.
  • Storfer A, Murphy MA, Evans JS et al. (2007) Putting the “Landscape” in landscape genetics. Heredity, 98, 128142.
  • Taberlet P, Waits LP, Luikart G (1999) Non-invasive genetic sampling: look before you leap. Trends in Ecology and Evolution, 14, 323327.
  • Tallmon DA, Draheim HM, Mills LS, Allendorf FW (2002) Insights into recently fragmented vole populations from combined genetic and demographic data. Molecular Ecology, 11, 699709.
  • Tamura N, Hayashi F (2007) Five-year study of the genetic structure and demography of two subpopulations of the Japanese squirrel (Sciurus lis) in a continuous forest and an isolated woodlot. Ecological Research, 22, 261267.
  • Thorne JH, Huber PR, Girvetz EH, Quinn J, McCoy MC (2009) Integration of regional mitigation assessment and conservation planning. Ecology and Society, 14, 47. Available from http://www.ecologyandsociety.org/vol14/iss1/art47/.
  • TRB (2002) Environmental Research Needs in Transportation. Transportation Research Board, Conference Proceedings 28, National Academy Press, Washington, DC.
  • Trombulak SC, Frissell CA (2000) Review of ecological effects of roads on teresstrial and aquatic communities. Conservation Biology, 14, 1830.
  • Vandergast AG, Bohonak AJ, Weissman DB, Fisher RN (2007) Understanding the genetic effects of recenty habitat fragmentation in the context of evolutionary history: phylogeography and landscape genetics of a southern California endemic Jerusalem cricket (Orthoptera: Stenopelmatidae: Stenopelmatus). Molecular Ecology, 16, 977992.
  • Vernesi C, Bruford MW, Bertorelle G et al. (2008) Where’s the conservation in conservation genetics? Conservation Biology, 22, 802804.
  • Vos CC, Antonisse-De Jong AG, Geodharts PW, Smulders MJM (2001a) Genetic similarity as a measure for connectivity between fragmented populations of the moor frog (Rana arvalis). Heredity, 86, 598608.
  • Vos CC, Verboom J, Opdam PFM, Ter Braak CJF (2001b) Toward ecologically scaled landscape indices. The American Naturalist, 183, 2441.
  • Waits LP (2004) Using non-invasive genetic sampling to detect and count rare wildlife species. In: Sampling Rare or Elusive Specie (ed. ThompsonB), pp. 211228. Island Press, Washington, DC.
  • Waits L, Paetkau D (2005) Noninvasive genetic sampling tools for wildlife biologists: a review of applications and recommendations for accurate data collection. Journal of Wildlife Management, 69, 14191433.
  • Wang J, Whitlock MC (2003) Estimating effective population size and migration rates from genetic samples over space and time. Genetics, 163, 429446.
  • Whitham TG, Bailey JK, Schweitzer JA et al. (2006) A framework for community and ecosystem genetics: from genes to ecosystems. Nature Reviews Genetics, 7, 510523.
  • Whitham TG, DiFazio SP, Schweitzer JA et al. (2008) Extending genomics to natural communities and ecosystems. Science, 320, 492495.
  • Van Wieren SE, Worm PB (2001) The use of a motorway wildlife overpass by large mammals. Netherlands Journal of Zoology, 51, 97105.
  • Wilson GA, Olson W, Strobeck C (2002) Reproductive success in wood bison (Bison bison athabascae) established using molecular techniques. Canadian Journal of Zoology, 80, 15371548.
  • Zachos FE, Althoff C, Steynitz YV, Eckert I, Hartl GB (2007) Genetic analysis of an isolated red deer (Cervus elaphus) population showing signs of inbreeding depression. European Journal of Wildlife Research, 53, 6167.

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Road effects on wildlife
  5. Review of studies using genetic data to assess roads effects
  6. Towards molecular road ecology
  7. Conclusions
  8. Acknowledgements
  9. References
  10. Supporting Information

Table S1 Summary of studies that use genetic data to assess road effects on animals

Table S2 Summary of characteristics of studies using genetic data to assess road effects on animals

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MEC_4322_sm_TablesS1andS2.doc155KSupporting info item

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