Next Article in Journal
Efficacy of an Immunotherapy Combining Immunogenic Chimeric Protein Plus Adjuvant and Amphotericin B against Murine Visceral Leishmaniasis
Next Article in Special Issue
World Spread of Tropical Soda Apple (Solanum viarum) under Global Change: Historical Reconstruction, Niche Shift, and Potential Geographic Distribution
Previous Article in Journal
Compositional Structure of the Genome: A Review
Previous Article in Special Issue
Distribution, Effect, and Control of Exotic Plants in Republic of Korea
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biology
Volume 12
Issue 6
10.3390/biology12060850
biology-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Aliens on the Road: Surveying Wildlife Roadkill to Assess the Risk of Biological Invasion

by
Andrea Viviano
1,
Marcello D’Amico
2,* and
Emiliano Mori
1,3
1
Istituto di Ricerca sugli Ecosistemi Terrestri, Consiglio Nazionale delle Ricerche, 50019 Sesto Fiorentino, Italy
2
Department of Conservation Biology and Global Change, Doñana Biological Station, Spanish National Research Council (CSIC), 41092 Seville, Spain
3
National Biodiversity Future Center, 90133 Palermo, Italy
*
Author to whom correspondence should be addressed.
Biology 2023, 12(6), 850; https://doi.org/10.3390/biology12060850
Submission received: 3 March 2023 / Revised: 29 May 2023 / Accepted: 9 June 2023 / Published: 13 June 2023
(This article belongs to the Special Issue Risk Assessment for Biological Invasions)
Browse Figures
Review Reports Versions Notes

Abstract

:

Simple Summary

Monitoring non-native species is important to assess their invasion risk. Here, we carried out a literature review on roadkill studies to investigate geographical patterns of biological invasions. We believe that roadkill data from published literature can be a valuable resource for people dealing with biological invasions, especially when field surveys cannot be performed. We retrieved 2314 studies published until January 2022, but only 40 of them (in addition to our own unpublished field data) included a full list of road-killed species (with a number of affected individuals for each species), so we were able to include them in our analyses. We classified all road-killed species from retrieved studies as native or introduced (domestic, introduced in historical times or recently released). We found that a higher number of introduced species would be recorded among roadkill in Mediterranean and Temperate regions with respect to Tropical and Desert areas. This result is in line with the current knowledge on non-native species distribution at the global scale, thus confirming that roadkill datasets can be used beyond the study of road impacts, such as for an assessment of different levels of biological invasions among different countries.

Abstract

Monitoring the presence and distribution of alien species is pivotal to assessing the risk of biological invasion. In our study, we carried out a worldwide review of roadkill data to investigate geographical patterns of biological invasions. We hypothesise that roadkill data from published literature can turn out to be a valuable resource for researchers and wildlife managers, especially when more focused surveys cannot be performed. We retrieved a total of 2314 works published until January 2022. Among those, only 41 (including our original data) fitted our requirements (i.e., including a total list of roadkilled terrestrial vertebrates, with a number of affected individuals for each species) and were included in our analysis. All roadkilled species from retrieved studies were classified as native or introduced (domestic, paleo-introduced, or recently released). We found that a higher number of introduced species would be recorded among roadkill in Mediterranean and Temperate areas with respect to Tropical and Desert biomes. This is definitely in line with the current knowledge on alien species distribution at the global scale, thus confirming that roadkill datasets can be used beyond the study of road impacts, such as for an assessment of different levels of biological invasions among different countries.

Graphical Abstract

1. Introduction

In recent decades, the percentage of the planet covered by roads has increased worldwide [1,2,3], together with their impacts on biodiversity and ecosystems [4,5,6]. Fragmentation, light, noise and chemical pollution, as well as direct human disturbance, are among the most well-recognised road impacts [7,8,9]. Other impacts have also been described, such as roads promoting biological invasions, especially for plants [10,11,12], but also for some animal species [13,14,15]. However, wildlife-vehicle collisions are the most evident road impact, as well as the most investigated one in scientific literature [16,17,18].
Roadkill is so well studied because it can affect the persistence of animal populations [18,19,20], mainly by limiting dispersal possibilities and then disrupting metapopulations, thus triggering local extinction vortices [21,22,23]. Such extinctions are often a relevant issue also for economic reasons because, in some cases, they concern species providing valuable ecosystem services [24,25,26]. Furthermore, roadkill is so well studied because they represent a major and often overlooked issue for road safety, especially when involving large vertebrates [27,28,29].
Roadkill observations and even surveys are increasingly used to monitor and update species distribution, e.g., [30,31,32,33,34], by means of citizen science, e.g., [35,36,37]. Individual hobbyists and wildlife enthusiasts have greatly increased in the last 30 years, and the majority of them upload opportunistic records to online platforms [38,39,40], including projects on road mortality [35,41,42]. Similarly, the low cost of detecting and uploading roadkill to online databanks (e.g., iNaturalist, GBIF) has also been used to monitor biological invasions, particularly at early invasion stages [43,44,45]. Immediately after release or escape, invasive alien species are present in new areas at low densities, which may prevent their detection through standardised monitoring, therefore relying on accidental observations, such as roadkill as first detection [46,47,48]. For instance, in Central Italy, the monitoring of the invasive population of the Northern raccoon Procyon lotor started after the first detections of several roadkilled individuals [49]. Furthermore, given that often roadkilled individuals are juveniles in dispersal [50,51,52], the detection of invasive alien species may also provide evidence of the potential range of expansion of established populations [53,54,55].
The effectiveness of detecting invasive alien species or monitoring their distribution through roadkill has never been quantified, although field evidence of the successfulness of this method has been suggested [34,56,57]. Using roadkill to study alien species represents a very novel approach to biological invasions, and to the best of our knowledge, there are no studies on roadkill of alien species comparing different locations. With this work, we aimed to fill this gap, and we hypothesised that the percentage of roadkilled allochthonous species varies among regions, being higher in the Mediterranean biome (first prediction), especially in Italy (second prediction). In fact, the Mediterranean biome is one of the world’s biodiversity hotspots, but it is also threatened by a high number of alien species [58,59,60], probably because it includes, at the same time, the highest biodiversity but also the highest levels of human pressure and trade [61,62,63]. In the Mediterranean biome, Italy is reported to be one of the richest countries in animal diversity [62,64,65], as well as one of the richest in alien species (nearly 17% of all mammals and over 57% of freshwater fish species are alien in Italy [66,67]). This is probably because of the intense trade pressure which has occurred in this area for millennia [68,69,70].
In this work, we carried out a roadkill survey focused on terrestrial vertebrates in our study area in a Mediterranean region of Central Italy (i.e., Tuscany), and then we compared our list of alien vs. native roadkilled species with all published and comparable lists from other regions of the world (including both Mediterranean and other biomes), which we obtained by a literature review. Into the framework of such comparisons, we tested if the percentage of roadkilled introduced species varied among biomes (and then countries), always considering several possible confounding factors at the same time, such as the latitude, Gross Domestic Product (GDP hereafter), and the number of neighbouring countries. High temperatures extend the available time for growth and reproduction, so low latitudes can comparatively host higher numbers of introduced species [71,72,73]. GDP has also been described as increasing the number of introduced species, probably because of the combined effects of trade, consumerism and urbanization [74,75,76]. Finally, the number of introduced species is usually higher in connected countries with established trade and tourism along shared infrastructures, such as neighbouring countries [74,77,78]. In order to investigate the reliability of roadkill data for investigating biological invasions at a macro-ecological scale, we also carried out an additional analysis comparing the whole lists of alien vs. native species inhabiting all the countries considered in the first analysis, always including the same approach and candidate predictors. This study represents a first example of how we can investigate patterns of biological invasions by analysing roadkill datasets, which can become a valuable resource for researchers and wildlife managers, especially for first assessments when more focused surveys cannot be performed.

2. Materials and Methods

2.1. Study Area (Fieldwork Survey)

Our fieldwork was conducted in Southern Tuscany (Provinces of Grosseto and Siena, Central Italy). We travelled once a week along the road linking the village of Prata (Province of Grosseto) to Grosseto and Siena for three years (2011–2013), for a total of about 101 km/week (Figure 1).
The road skirts a high number of habitat types, including dense deciduous and mixed woodlands (about 27%), farmlands (about 43%), scrublands with hedgerows (16%), human settlements (9%) and fallows (5%). The whole road was paved, and an allowed speed limit was 70–90 km/h throughout most road sections, apart from 16 km (in total) of the beltway (speed limit: 110 km/h) in the surroundings of the main cities (Siena and Grosseto). The study area is characterised by the presence of at least 38 mammal species (represented among others by the wild boar Sus scrofa, the roe deer Capreolus capreolus, the red fox Vulpes vulpes, the European badger Meles meles and the crested porcupine Hystrix cristata), about 100 bird species (also including the common pheasant Phasianus colchicus, the red-legged partridge Alectoris rufa and over 15 raptor species), 16 species of reptiles (e.g., the asp viper Vipera aspis and the Italian wall lizard Podarcis siculus) and eight species of Amphibians (e.g., the common toad Bufo bufo and the Italian crested newt Triturus carnifex) [79,80,81]. The local community of terrestrial vertebrates mainly consists of common species, but some threatened species are also present, such as the European turtle-dove Streptopelia turtur (globally listed as Vulnerable; [82]).

2.2. Data Collection and Analyses (Fieldwork Survey and Literature Review)

During our fieldwork survey, every time a roadkilled animal was detected, it was georeferenced and classified (when possible) at the species level. We also recorded whether the species was native or introduced (see Supplementary Materials S1). Using the list of roadkilled species resulting from our fieldwork survey, we calculated the percentage of introduced species on the total of roadkilled species in order to compare this percentage to the same value from all published and comparable lists from other regions of the world. Regarding introduced species, we also specified the percentage of recently introduced, paleo-introduced and domestic species (we considered all the species introduced before 1500 AD as paleo-introduced [83,84]).
We performed our first literature review by searching those lists of roadkilled terrestrial vertebrates on the Web of Science (WOS; [85]) by implementing the following search string: (“roadkill*” OR “road-kill*” OR (“collision” AND “road”) AND (“vertebrate*” OR “wildlife” OR “animal*”). We retained articles focusing on the whole community of terrestrial vertebrates and including an explicit list of roadkilled species. Once we obtained the final list of retained articles, we extracted the study areas in order to perform a second literature review aimed at obtaining the percentage of introduced species on the total of species inhabiting those countries (always focusing on terrestrial vertebrates). We completed this second review also with information proceeding from institutional websites such as www.iucngisd.org, www.iucnredlist.org, www.griis.org (all accessed on 12 February 2023), and several national websites providing inventories of introduced and native species.
For each study area mentioned in those articles, we extracted the country, the biome (as defined by [86,87]), and three explanatory variables potentially affecting the number of introduced species in a given area, such as latitude, GDP, and the number of neighbouring countries, in addition to the aforementioned percentage of roadkilled introduced species. Considering that the analysis would probably have relatively little data, we modified some of the explanatory variables in order to have fewer levels within each of them. More concretely, we combined some biomes (i.e., “Temperate grasslands, savannas, and shrublands” and “Temperate broadleaf and mixed forests” in “Temperate biomes”, “Tropical and subtropical grasslands, savannas, and shrublands” and “Tropical and subtropical moist broadleaf forests” in “Tropical biomes”), we implemented the latitude as a continuous variable without decimals, we transformed the GDP in an ordinal variable with four levels (i.e., less than USD one billion, between USD one and two billion, between USD two and three billion, and more than USD three billion), and we also transformed the number of neighbouring countries into an ordinal variable with four levels (i.e., without neighbouring countries, with one/two neighbouring countries, or with many neighbouring countries).
In order to test our first prediction (i.e., more introduced species in Mediterranean biomes), we used generalised linear mixed models (GLMMs; negative binomial error distribution and log link function) and evaluated their performance using Akaike Information Criterion (AIC). For each model, the response variable was the percentage of introduced species on the total of roadkilled species, considering the country of each retained study as a random factor. We compared all the possible univariate models, including the considered explanatory variables, with the full model and also the null model (i.e., only including the random factor). We selected the most supported model using AICc (i.e., AIC for small sample sizes) and calculated Akaike weights (wAICc) to estimate the relative support for every model (ranging from 0 to 1, with larger numbers indicating greater support; [88,89,90]). We also provided R2 for each considered model. Additionally, we repeated exactly the same AIC model selection of the same GLMMs, but this time implementing as a response variable the percentage of introduced species on the total of existing species.
In order to test our second prediction (i.e., more introduced species in Italy than in other countries of Mediterranean biomes), we performed a generalised linear model (GLM) in which the response variable was again the percentage of introduced species on the total of roadkilled species, and the explanatory variable was the country of each retained study. We again used negative binomial error distribution and log link function. We evaluated model performance by comparing the AIC of the considered model with the AIC of the related null model.

3. Results

3.1. Fieldwork Survey

In Central Italy, we collected a total of 511 roadkill specimens belonging to 89 species (Figure 1; Supplementary Materials S1). Most roadkill belonged to Mammals (N = 273, for a total of 22 species), followed by Birds (N = 137, for a total of 53 species), Reptiles (N = 53, for a total of ten species) and Amphibians (N = 48, for a total of four species). Introduced species included two domestic (domestic cat Felis catus and domestic pigeon Columba livia domestica), four paleo-introduced (fallow deer Dama dama, crested porcupine Hystrix cristata, black rat Rattus rattus, and common pheasant Phasianus colchicus) and two recently introduced species (Iberian pond turtle Mauremys leprosa and coypu Myocastor coypus). Two further species (i.e., red-legged partridge Alectoris rufa and grey partridge Perdix perdix), although native to our study area, are present today as the result of releases for hunting purposes. Among the mammals, the most detected roadkilled species were the European hedgehog Erinaceus europaeus (73 roadkilled individuals), the European badger Meles meles (37 roadkilled individuals), the red fox Vulpes vulpes (35 roadkilled individuals) and the crested porcupine Hystrix cristata (27 roadkilled individuals). For birds, the most detected roadkill were the Eurasian jay Garrulus glandarius (13 roadkilled individuals), the common pheasant Phasianus colchicus (12 roadkilled individuals) and the European blackbird Turdus merula (ten roadkilled individuals). For reptiles, the most detected roadkill were the Western green lizard Lacerta bilineata (nine roadkilled individuals), the Aesculapian ratsnake Zamenis longissimus (nine roadkilled individuals) and the green whip snake Hierophis viridiflavus (eight roadkilled individuals). To conclude, the most detected amphibians were the common toad Bufo bufo (31 roadkilled individuals), the agile frog Rana dalmatina (nine roadkilled individuals) and the green toad Bufotes balearicus (six roadkilled individuals). Roadkill rates for each recorded species are provided in Supplementary Material S1.

3.2. Literature Review and Analysis

In our first literature review, we retrieved a total of 2314 works from WOS. Among those, only 41 (including our original data) fitted our requirements (i.e., including a total list of roadkilled terrestrial vertebrates, including the explicit numbers of dead individuals for each species) and were included in our analysis. Retained studies included up to 20.00% of introduced species (mean ± SD = 5.30 ± 5.69%). Most retained studies were conducted in tropical and subtropical areas (i.e., 20 studies from 11 countries), with a peak in American countries (i.e., ten studies from three countries; Figure 2). All retained studies included 5434 species (mean ± SD per study = 153.53 ± 432.36) for a total of 276,960 roadkilled individuals (mean ± SD per study = 7177.80 ± 25,451.81). Among those species, up to 17.85% were domestic (mean ± SD = 3.03 ± 4.45%), up to 4.62% were paleo-introduced (mean ± SD = 0.86 ± 1.56%) and up to 12.90% (mean ± SD = 1.24 ± 2.49%) were recent introductions. Roadkill rates for each retained study are provided in Supplementary Materials S2. As a result of our second literature review, we focused on the countries where the 40 retained studies were carried out, obtaining the percentage of introduced species on the total of existing species for all of them (Supplementary Material S2).
Concerning our first hypothesis, the biome was one of the most relevant explanatory variables in both model selections. In fact, the univariate model including this factor obtained plausible support in the roadkill model selection (according to [90]) and was the only supported model in the all-species model selection (Table 1). Overall, in both model selections, the percentage of introduced species in Mediterranean and Temperate biomes was higher than in Tropical and Desert biomes (Figure 3). On the other hand, other variables were also supported in the roadkill model selection, especially the latitude, which was the best-supported model (Table 1; with more introduced species at higher latitudes, roughly corresponding to Mediterranean and Temperate biomes; Supplementary Material S3). The number of neighbouring countries obtained plausible support in the roadkill model selection (according to [90]), being the second supported model (Table 1; with more introduced species in island countries than in nations having neighbouring countries).
Concerning the analysis focusing on Mediterranean countries (i.e., Italy, Spain, Portugal, and Tunisia), we retained only seven studies (three for Spain, two for Italy, one for Portugal, and one for Tunisia). No differences were observed in the percentage of introduced species on the total of roadkilled species among most of them, but Tunisia did show higher values (i.e., Figure 4). The country model was more supported than the null one (Supplementary Materials S4).

4. Discussion

In this work, we compared our original data collected on roadkilled terrestrial vertebrates from Central Italy with 40 analogous lists proceeding from published studies from throughout the world. This is, to the best of our knowledge, the first attempt at geographical analysis implementing only roadkill data, and in order to investigate the reliability of this kind of data, we also present an analysis implementing more general data proceeding from biological invasion datasets. We found that the percentage of introduced roadkilled species was higher in both the Mediterranean and Temperate biomes compared to other biomes. This is only a first assessment, implementing relatively simple analyses, but in our opinion, this case study can represent an interesting example of the possible value of roadkill datasets for investigating questions related to research fields other than road ecology, such as biological invasions and biogeography in this case.
Roadkill surveys usually represent a reliable information source regarding the common species that can be found in a given area [91,92,93], although remarkable exceptions have been described for species actively avoiding roads [21,94,95]. Nevertheless, invasive vertebrates are often generalist species able to use roads and roadsides as suitable habitats and to take advantage of the related resources (i.e., roadkilled carcasses, garbage; [96,97]), consequently paying the price of high roadkill rates [15,98,99]. Therefore, it is relatively safe to assume that alien species would be well represented in the roadkill records of a given area [34,100] and, consequently, that such data would be suitable for geographical analyses focused on biological invasions. According to this rationale, our analyses showed that the percentage of introduced species (on both the total of roadkilled species and the total of existing species) is higher in Mediterranean and Temperate biomes than in Tropical and Desert biomes. These findings confirm our prediction that the overall amount of introduced species is higher in the Mediterranean biome, as previously suggested by the available scientific literature [101,102,103]. However, the Temperate biome also suffered high levels of species introduction, especially in Central Europe and North America, both of which regions characterised by suitable climates for most vertebrate species and consequently also suitable for their invasions [73,104,105]. These biomes are more anthropised than others [106,107,108], probably because they are widely distributed in countries with early human presence, mostly in the Mediterranean Basin and, more generally, in Europe. In addition, early anthropization can be a major driver of the presence of established populations of introduced species, especially the paleo-introduced ones, such as the fallow deer Dama dama and the crested porcupine Hystrix cristata in our fieldwork area (e.g., [109,110]). Both the Mediterranean and Temperate biomes also include some of the countries with suitable latitudes (and consequently climates) for allowing growth and reproduction of introduced terrestrial vertebrates [72,73], such as Italy and Spain, but also USA and UK, with all of them providing published studies to our literature review.
The Mediterranean biome, spanning from coastal and interior portions of the USA, Mexico, Chile, South Africa, Australia and the Mediterranean Basin, is also recognised as one of the most imperilled in terms of biodiversity loss due to its sensitivity to biological invasions, land use and climate change [101,111,112]. In this biome, at least in the most sensitive areas such as national parks, monitoring roadkill should be a priority action, in order to detect conservation issues related to road impacts (see, for example, [113], but also [114,115] for examples in other biomes). Additionally, we showed here that these surveys can also provide valuable information about biological invasions, not only about cost-effective early detection in a given location (see a recent example regarding the American mink Neovison vison in Central Italy: [116]), but also to conduct geographical analyses and to estimate the percentage of common alien species in a given area. Government authorities and institutions (e.g., protected areas and environmental agencies) should include roadkill surveys in their long-term monitoring schemes, but citizen science can also be involved in collecting a remarkable amount of data [35,117,118]. Importantly, the use of ad hoc projects on online platforms (e.g., iNaturalist) may allow the collection of data throughout the years and from different sites [119,120,121] and to analyse temporal patterns of invasions. Finally, we would also highlight that less than 2% of roadkill studies focusing on all vertebrates (i.e., 40 on 2314) reported a full list of affected species, making it more difficult than expected to use this literature resource, so we would strongly recommend future roadkill studies to publish their species list (and data), in order to increase their interest and the possibility to be used and cited by other researchers.
Our second prediction was not supported by the specific model selection because there were no differences in the percentage of introduced species on the total of roadkilled species among most considered Mediterranean countries (i.e., Italy, Spain, and Portugal), although Tunisia showed higher values (mainly due to domestic species, possibly free-ranging more than in other Mediterranean countries; [122,123]). Thus, although Italy hosts a high number of alien species among terrestrial vertebrates [66,67,124,125], it does not seem to include a significantly higher number of alien species with respect to other Mediterranean countries included in our analysis. However, these results are probably due to the extremely small sample size (i.e., few studies in few Mediterranean countries), which may prevent achieving exhaustive conclusions, but also because the analysed studies from Italy were carried out in rural areas characterised by a relatively low amount of alien species [79,80,81,115], and are perhaps not completely representative of the whole country.
In addition to the small sample size (for both our fieldwork data and the datasets from literature reviews) and the potentially biased locations of the considered study areas, this work surely entails further limitations. Several factors have been described to drive the number of introduced species in a given country, such as the trade importance, the amount of different climatic niches and the number of potentially competing native species (e.g., [126,127,128]), but most of them were of difficult implementation considering our relatively small sample size. However, although we acknowledge these study limitations, we believe they do not affect the main idea of this work, which is that roadkill datasets can be used beyond the study of road impacts, and in this case, for a first assessment of biological invasions among different countries.

5. Conclusions

In this study, we used our fieldwork data collected on roadkilled terrestrial vertebrates from Central Italy and 40 analogous lists proceeding from published studies from all over the world. Our analysis represents the first attempt at biological invasion risk assessment implementing only roadkill data, and, as a result, the percentage of introduced roadkilled species was higher in both the Mediterranean and Temperate biomes compared to other biomes. This is only a first assessment implementing relatively simple analyses, and we acknowledge several study limitations, but, in our opinion, they do not affect the main idea of this study, which is that roadkill datasets can be used for investigating questions related to research fields other than road ecology, such as in this case biological invasions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biology12060850/s1, Supplementary Material S1: List and amount of roadkilled species in our fieldwork survey; Supplementary Material S2: List of retained studies from the literature review, and related variables; Supplementary Material S3: Figure depicting the relation between the percentage of introduced species (both on the total of roadkilled species and on the total of existing species) and the latitude of the country hosting the study areas; Supplementary Material S4: Table showing the AIC model selection focusing on the countries of Mediterranean biome.

Author Contributions

Conceptualization, M.D. and E.M.; methodology, A.V. and E.M.; formal analysis, M.D.; data curation, A.V. and E.M.; writing—original draft preparation, A.V.; writing—review and editing, A.V., M.D. and E.M.; visualization, A.V., M.D. and E.M.; funding acquisition, E.M. All authors have read and agreed to the published version of the manuscript.

Funding

A.V. and E.M. were supported by MUR-FOE-Project Capitale Naturale-Task “Biodiversità”. MD was supported by a JdC-Inc post-doctoral grant (IJC2019-039662-I) from the Spanish Ministry of Science and Innovation (MICINN). EM was also funded by the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4—Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union—NextGenerationEU; Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP B83C22002930006, Project title “National Biodiversity Future Center—NBFC”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

Acknowledgments

Isis Elisabeth Cohen Nicholson and Emily McCluskey kindly reviewed the English (first and second version, respectively). We sincerely appreciate all valuable comments and suggestions by the three Reviewers, which helped us to improve the quality of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Ibisch, P.L.; Hoffmann, M.T.; Kreft, S.; Pe’er, G.; Kati, V.; Biber-Freudenberger, L.; Dellasala, D.A.; Vale, M.M.; Hobson, P.R.; Selva, N. A global map of roadless areas and their conservation status. Science 2016, 354, 1423–1427. [Google Scholar] [CrossRef] [PubMed]
  2. Ascensão, F.; Fahrig, L.; Clevenger, A.P.; Corlett, R.T.; Jaeger, J.A.; Laurance, W.F.; Pereira, H.M. Environmental challenges for the Belt and Road Initiative. Nat. Sustain. 2018, 1, 206–209. [Google Scholar] [CrossRef]
  3. Meijer, J.R.; Huijbregts, M.A.; Schotten, K.C.; Schipper, A.M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 2018, 13, 064006. [Google Scholar] [CrossRef] [Green Version]
  4. Trombulak, S.C.; Frissell, C.A. Review of ecological effects of roads on terrestrial and aquatic communities. Conserv. Biol. 2000, 14, 18–30. [Google Scholar] [CrossRef] [Green Version]
  5. Forman, R.T.; Sperling, D.; Bissonette, J.A.; Clevenger, A.P.; Cutshall, C.D.; Dale, V.H.; Fahrig, L.; France, R.; Goldman, C.R.; Heanue, K.; et al. Road Ecology: Science and Solutions; Island Press: Washington, DC, USA, 2003; p. 481. [Google Scholar]
  6. Van der Ree, R.; Smith, D.J.; Grilo, C. Handbook of Road Ecology; John Wiley & Sons: Oxford, UK, 2015; p. 522. [Google Scholar]
  7. Gaston, K.J.; Bennie, J.; Davies, T.W.; Hopkins, J. The ecological impacts of nighttime light pollution: A mechanistic appraisal. Biol. Rev. 2013, 88, 912–927. [Google Scholar] [CrossRef]
  8. Laurance, W.F.; Clements, G.R.; Sloan, S.; O’Connell, C.S.; Mueller, N.D.; Goosem, M.; Venter, O.; Edwards, D.P.; Phalan, B.; Balmford, A.; et al. A global strategy for road building. Nature 2014, 513, 229–232. [Google Scholar] [CrossRef]
  9. Leonard, R.J.; Hochuli, D.F. Exhausting all avenues: Why impacts of air pollution should be part of road ecology. Front. Ecol. Environ. 2017, 15, 443–449. [Google Scholar] [CrossRef]
  10. Christen, D.C.; Matlack, G.R. The habitat and conduit functions of roads in the spread of three invasive plant species. Biol. Invasions 2009, 11, 453–465. [Google Scholar] [CrossRef]
  11. Benedetti, Y.; Morelli, F. Spatial mismatch analysis among hotspots of alien plant species, road and railway networks in Germany and Austria. PLoS ONE 2017, 12, e0183691. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. McDougall, K.L.; Lembrechts, J.; Rew, L.J.; Haider, S.; Cavieres, L.A.; Kueffer, C.; Milbau, A.; Naylor, B.J.; Nuñez, M.A.; Pauchard, A.; et al. Running off the road: Roadside non-native plants invading mountain vegetation. Biol. Invasions 2018, 20, 3461–3473. [Google Scholar] [CrossRef] [Green Version]
  13. Brown, G.P.; Phillips, B.L.; Webb, J.K.; Shine, R. Toad on the road: Use of roads as dispersal corridors by cane toads (Bufo marinus) at an invasion front in tropical Australia. Biol. Conserv. 2006, 133, 88–94. [Google Scholar] [CrossRef] [Green Version]
  14. D’Amico, M.; Rouco, C.; Russell, J.C.; Román, J.; Revilla, E. Invaders on the road: Synanthopic bird foraging along highways. Oecol. Aust. 2013, 17, 86–95. [Google Scholar] [CrossRef] [Green Version]
  15. Ascensão, F.; Latombe, G.; Anadón, J.D.; Abellán, P.; Cardador, L.; Carrete, M.; Tella, J.L.; Capinha, C. Drivers of compositional dissimilarity for native and alien birds: The relative roles of human activity and environmental suitability. Biol. Invasions 2020, 22, 1447–1460. [Google Scholar] [CrossRef]
  16. D’Amico, M.; Ascensão, F.; Fabrizio, M.; Barrientos, R.; Gortázar, C. Twenty years of Road Ecology: A Topical Collection looking forward for new perspectives. Eur. J. Wildl. Res. 2018, 64, 26. [Google Scholar] [CrossRef]
  17. Collinson, W.; Davies-Mostert, H.; Roxburgh, L.; van der Ree, R. Status of road ecology research in Africa: Do we understand the impacts of roads, and how to successfully mitigate them? Front. Ecol. Evol. 2019, 7, 479. [Google Scholar] [CrossRef] [Green Version]
  18. Barrientos, R.; Ascensão, F.; D’Amico, M.; Grilo, C.; Pereira, H.M. The lost road: Do transportation networks imperil wildlife population persistence? Perspect. Ecol. Conserv. 2021, 19, 411–416. [Google Scholar] [CrossRef]
  19. Grilo, C.; Borda-de-Água, L.; Beja, P.; Goolsby, E.; Soanes, K.; le Roux, A.; Koroleva, E.; Ferreira, F.Z.; Gagné, S.A.; Wang, Y.; et al. Conservation threats from roadkill in the global road network. Glob. Ecol. Biogeogr. 2021, 30, 2200–2210. [Google Scholar] [CrossRef]
  20. Ascensão, F.; Desbiez, A.L. Assessing the impact of roadkill on the persistence of wildlife populations: A case study on the giant anteater. Perspect. Ecol. Conserv. 2022, 20, 272–278. [Google Scholar] [CrossRef]
  21. Ascensão, F.; Kindel, A.; Teixeira, F.Z.; Barrientos, R.; D’Amico, M.; Borda-de-Água, L.; Pereira, H.M. Beware that the lack of wildlife mortality records can mask a serious impact of linear infrastructures. Glob. Ecol. Conserv. 2019, 19, e00661. [Google Scholar] [CrossRef]
  22. Hels, T.; Buchwald, E. The effect of road kills on amphibian populations. Biol. Conserv. 2001, 99, 331–340. [Google Scholar] [CrossRef] [Green Version]
  23. Spencer, R.J.; Van Dyke, J.U.; Thompson, M.B. Critically evaluating best management practices for preventing freshwater turtle extinctions. Conserv. Biol. 2017, 31, 1340–1349. [Google Scholar] [CrossRef] [PubMed]
  24. Liu, J. Effects of global household proliferation on ecosystem services. In Landscape Ecology for Sustainable Environment and Culture; Fu, B., Jones, K., Eds.; Springer Science+Business Media: Dordrecht, The Netherlands, 2013; p. 364. [Google Scholar]
  25. Gilbert, S.L.; Sivy, K.J.; Pozzanghera, C.B.; DuBour, A.; Overduijn, K.; Smith, M.M.; Zhou, J.; Little, J.M.; Prugh, L.R. Socioeconomic benefits of large carnivore recolonization through reduced wildlife-vehicle collisions. Conserv. Lett. 2017, 10, 431–439. [Google Scholar] [CrossRef]
  26. Martin, A.E.; Graham, S.L.; Henry, M.; Pervin, E.; Fahrig, L. Flying insect abundance declines with increasing road traffic. Insect Conserv. Divers. 2018, 11, 608–613. [Google Scholar] [CrossRef]
  27. Conover, M.R.; Pitt, W.C.; Kessler, K.K.; DuBow, T.J.; Sanborn, W.A. Review of human injuries, illnesses, and economic losses caused by wildlife in the United States. Wildl. Soc. Bull. 1995, 23, 407–414. [Google Scholar]
  28. Ascensão, F.; Barrientos, R.; D’Amico, M. Wildlife collisions put a dent in road safety. Science 2021, 374, 1208. [Google Scholar] [CrossRef] [PubMed]
  29. Llagostera, P.; Comas, C.; López, N. Modeling road traffic safety based on point patterns of wildlife-vehicle collisions. Sci. Total Environ. 2022, 846, 157237. [Google Scholar] [CrossRef] [PubMed]
  30. Calenge, C.; Chadoeuf, J.; Giraud, C.; Huet, S.; Julliard, R.; Monestiez, P.; Piffady, J.; Pinaud, D.; Ruette, S. The spatial distribution of Mustelidae in France. PLoS ONE 2015, 10, e0121689. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Dyczkowski, J. Golden nightjar in Western Sahara, Morocco, in May 2015. Dutch Bird. 2016, 38, 80–86. [Google Scholar]
  32. Lapini, L.; Dreon, A.L.; Caldana, M.; Luca, M.; Villa, M. Distribuzione, espansione e problemi di conservazione di Canis aureus in Italia (Carnivora: Canidae). Quad. Mus. Civ. Stor. Nat. Ferrara 2018, 6, 89–96. [Google Scholar]
  33. Russo, L.F.; Barrientos, R.; Fabrizio, M.; Di Febbraro, M.; Loy, A. Prioritizing road-kill mitigation areas: A spatially explicit national-scale model for an elusive carnivore. Divers. Distrib. 2020, 26, 1093–1103. [Google Scholar] [CrossRef]
  34. Schwartz, A.L.; Shilling, F.M.; Perkins, S.E. The value of monitoring wildlife roadkill. Eur. J. Wildl. Res. 2020, 66, 18. [Google Scholar] [CrossRef] [Green Version]
  35. Vercayie, D.; Herremans, M. Citizen science and smartphones take roadkill monitoring to the next level. Nat. Conserv. 2015, 11, 29–40. [Google Scholar] [CrossRef] [Green Version]
  36. Waetjen, D.P.; Shilling, F.M. Large extent volunteer roadkill and wildlife observation systems as sources of reliable data. Front. Ecol. Evol. 2017, 5, 89. [Google Scholar] [CrossRef] [Green Version]
  37. Englefield, B.; Starling, M.; Wilson, B.; Roder, C.; McGreevy, P. The Australian roadkill reporting project—Applying integrated professional research and citizen science to monitor and mitigate roadkill in Australia. Animals 2020, 10, 1112. [Google Scholar] [CrossRef]
  38. Bik, H.M.; Goldstein, M.C. An introduction to social media for scientists. PLoS Biol. 2013, 11, e1001535. [Google Scholar] [CrossRef] [Green Version]
  39. Van Strien, A.J.; Van Swaay, C.A.; Termaat, T. Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models. J. Appl. Ecol. 2013, 50, 1450–1458. [Google Scholar] [CrossRef]
  40. Mori, E.; Di Bari, P.; Coraglia, M. Interference between roe deer and Northern chamois in the Italian Alps: Are Facebook groups effective data sources? Ethol. Ecol. Evol. 2018, 30, 277–284. [Google Scholar] [CrossRef]
  41. Chyn, K.; Lin, T.E.; Chen, Y.K.; Chen, C.Y.; Fitzgerald, L.A. The magnitude of roadkill in Taiwan: Patterns and consequences revealed by citizen science. Biol. Conserv. 2019, 237, 317–326. [Google Scholar] [CrossRef]
  42. Heigl, F.; Teufelbauer, N.; Resch, S.; Schweiger, S.; Stückler, S.; Dörler, D. A dataset of road-killed vertebrates collected via citizen science from 2014–2020. Sci. Data 2022, 9, 504. [Google Scholar] [CrossRef] [PubMed]
  43. Panzeri, M.; Mori, E.; Mazza, G.; Menchetti, M. Records of introduced stripe-necked terrapins (Mauremys species) in Italy. Acta Herpetol. 2014, 9, 97–100. [Google Scholar]
  44. Caley, P.; Ramsey, D.S.; Barry, S.C. Inferring the distribution and demography of an invasive species from sighting data: The red fox incursion into Tasmania. PLoS ONE 2015, 10, e0116631. [Google Scholar] [CrossRef] [PubMed]
  45. Balčiauskas, L.; Stratford, J.; Balčiauskienė, L.; Kučas, A. Roadkills as a method to monitor raccoon dog populations. Animals 2021, 11, 3147. [Google Scholar] [CrossRef] [PubMed]
  46. Mori, E.; Amerini, R.; Mazza, G.; Bertolino, S.; Battiston, R.; Sforzi, A.; Menchetti, M. Alien shades of grey: New occurrences and relevant spread of Sciurus carolinensis in Italy. Eur. J. Ecol. 2016, 2, 13–20. [Google Scholar] [CrossRef] [Green Version]
  47. Kwak, M.L. The first record of the introduced flea Spilopsyllus cuniculi (Dale, 1878) (Siphonaptera: Pulicidae) from the invasive red fox in Australia, with a review of the fleas associated with the red fox in Australia. Aust. Entomol. 2017, 44, 289–292. [Google Scholar]
  48. Ham, C.H.; Park, S.M.; Lee, J.E.; Park, J.; Lee, D.H.; Sung, H.C. First report of the Black-headed python (Aspidites melanocephalus Krefft, 1864) found in the wild in the Republic of Korea. BioInvasions Rec. 2022, 11, 571–577. [Google Scholar] [CrossRef]
  49. Boscherini, A.; Mazza, G.; Menchetti, M.; Laurenzi, A.; Mori, E. Time is running out! Rapid range expansion of the invasive northern raccoon in central Italy. Mammalia 2020, 84, 98–101. [Google Scholar] [CrossRef] [Green Version]
  50. Santos, S.M.; Lourenco, R.; Mira, A.; Beja, P. Relative effects of road risk, habitat suitability, and connectivity on wildlife roadkills: The case of tawny owls (Strix aluco). PLoS ONE 2013, 8, e79967. [Google Scholar] [CrossRef] [Green Version]
  51. Haigh, A.; O’Riordan, R.M.; Butler, F. Hedgehog Erinaceus europaeus mortality on Irish roads. Wildl. Biol. 2014, 20, 155–160. [Google Scholar] [CrossRef] [Green Version]
  52. Meyrom, K.; Yosef, R.; Charter, M. Are roadkills density-dependent? Case study of the barn owl (Tyto alba). Diversity 2023, 15, 412. [Google Scholar] [CrossRef]
  53. Lapini, L. Il cane viverrino Nyctereutes procyonoides ussuriensis Matschie, 1908 in Italia: Segnalazioni 1980–2005 (Mammalia: Carnivora: Canidae). Boll. Mus. Civ. St. Nat. Venezia 2006, 57, 235–239. [Google Scholar]
  54. Barrientos, R. Adult sex-ratio distortion in the native European polecat is related to the expansion of the invasive American mink. Biol. Conserv. 2015, 186, 28–34. [Google Scholar] [CrossRef]
  55. Chen, T.Y.; Richard, R.; Lin, T.E.; Huang, S.P. Landscape forest impacts the potential activity time of an invasive lizard and its possibilities for range expansion in Taiwan under climate warming. J. Therm. Biol. 2021, 98, 102948. [Google Scholar] [CrossRef]
  56. Kowalczyk, R.; Zalewski, A.; Jędrzejewska, B.; Ansorge, H.; Bunevich, A.N. Reproduction and mortality of invasive raccoon dogs (Nyctereutes procyonoides) in the Białowieża Primeval Forest (eastern Poland). Ann. Zool. Fenn. 2009, 46, 291–301. [Google Scholar] [CrossRef]
  57. Boncompagni, L.; Molfini, M.; Ciampelli, P.; Fazzi, P.; Lucchesi, M.; Mori, E.; Petralia, L.; Mazza, G. No country for native crayfish: Importance of crustaceans in the diet of native and alien Northern raccoon. Ethol. Ecol. Evol. 2021, 33, 576–590. [Google Scholar] [CrossRef]
  58. Cuttelod, A.; García, N.; Malak, D.A.; Temple, H.J.; Katariya, V. The Mediterranean: A biodiversity hotspot under threat. In Wildlife in a Changing World—An Analysis of the 2008 IUCN Red List of Threatened Species; Viè, J.C., Hilton-Taylor, C., Stuart, S.N., Eds.; IUCN Editions: Gland, Switzerland, 2009; pp. 89–102. [Google Scholar]
  59. Clavero, M.; Hermoso, V.; Levin, N.; Kark, S. Biodiversity research: Geographical linkages between threats and imperilment in freshwater fish in the Mediterranean Basin. Divers. Distrib. 2010, 16, 744–754. [Google Scholar] [CrossRef]
  60. Cao Pinna, L.; Axmanová, I.; Chytrý, M.; Malavasi, M.; Acosta, A.T.; Giulio, S.; Attorre, F.; Bergmeier, E.; Biurrun, I.; Campos, J.A.; et al. The biogeography of alien plant invasions in the Mediterranean Basin. J. Veg. Sci. 2021, 32, e12980. [Google Scholar] [CrossRef]
  61. Laubier, L. Mediterranean Sea and humans: Improving a conflictual partnership. In The Mediterranean Sea; Saliot, A., Ed.; Springer: Berlin, Germany, 2005; pp. 3–27. [Google Scholar]
  62. De Jong, Y.; Verbeek, M.; Michelsen, V.; de Place Bjørn, P.; Los, W.; Steeman, F.; Hagedorn, G.; Wetzel, F.T.; Glöcker, F.; Kroupa, A.; et al. Fauna Europaea—All European animal species on the web. Biodivers. Data J. 2014, 2, e4034. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Martín-Forés, I. Exotic plant species in the Mediterranean biome: A reflection of cultural and historical relationships. In Mediterranean Identities—Environment, Society, Culture; Fuerst-Bjelis, B., Ed.; IntechOpen Limited: London, UK, 2017; pp. 179–201. [Google Scholar]
  64. Maiorano, L.; Amori, G.; Capula, M.; Falcucci, A.; Masi, M.; Montemaggiori, A.; Pottier, J.; Psomas, A.; Rondinini, C.; Russo, D.; et al. Threats from climate change to terrestrial vertebrate hotspots in Europe. PLoS ONE 2013, 8, e74989. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Gippoliti, S.; Groves, C.P. Overlooked mammal diversity and conservation priorities in Italy: Impacts of taxonomic neglect on a Biodiversity Hotspot in Europe. Zootaxa 2018, 4434, 511–528. [Google Scholar] [CrossRef]
  66. Loy, A.; Aloise, G.; Ancillotto, L.; Angelici, F.M.; Bertolino, S.; Capizzi, D.; Castiglia, R.; Colangelo, P.; Contoli, L.; Cozzi, B.; et al. Mammals of Italy: An annotated checklist. Hystrix 2019, 30, 87–106. [Google Scholar]
  67. Lorenzoni, M.; Borghesan, F.; Carosi, A.; Ciuffardi, L.; De Curtis, O.; Delmastro, G.B.; Di Tizio, L.; Franzoi, P.; Maio, G.; Mojetta, A.; et al. Check-list dell’ittiofauna delle acque dolci italiane. Ital. J. Freshw. Ichthyol. 2019, 1, 239–254. [Google Scholar]
  68. Saliot, A. The Mediterranean Sea; Springer: Berlin, Germany, 2005; p. 408. [Google Scholar]
  69. Blondel, J. The ‘design’ of Mediterranean landscapes: A millennial story of humans and ecological systems during the historic period. Hum. Ecol. 2006, 34, 713–729. [Google Scholar] [CrossRef]
  70. Oro, D.; Pueyo, Y.; Bauzà, J.; Errea, M.P.; Arroyo, A.I. Long transient response of vegetation dynamics after four millennia of anthropogenic impacts in an island ecosystem. Glob. Chang. Biol. 2022, 28, 6318–6332. [Google Scholar] [CrossRef] [PubMed]
  71. Dijkstra, J.A.; Westerman, E.L.; Harris, L.G. Elevated seasonal temperatures eliminate thermal barriers of reproduction of a dominant invasive species: A community state change for northern communities? Divers. Distrib. 2017, 23, 1182–1192. [Google Scholar] [CrossRef]
  72. Hulme, P.E. Climate change and biological invasions: Evidence, expectations, and response options. Biol. Rev. 2017, 92, 1297–1313. [Google Scholar] [CrossRef]
  73. Pyšek, P.; Hulme, P.E.; Simberloff, D.; Bacher, S.; Blackburn, T.M.; Carlton, J.T.; Dawson, W.; Essl, F.; Foxcroft, L.C.; Genovesi, P.; et al. Scientists’ warning on invasive alien species. Biol. Rev. 2020, 95, 1511–1534. [Google Scholar] [CrossRef]
  74. Westphal, M.I.; Browne, M.; MacKinnon, K.; Noble, I. The link between international trade and the global distribution of invasive alien species. Biol. Invasions 2008, 10, 391–398. [Google Scholar] [CrossRef]
  75. Hulme, P.E. Biological invasions in Europe: Drivers, pressures, states, impacts and responses. In Biodiversity under Threat; Hester, R.E., Harrison, R.M., Eds.; The Royal Society of Chemistry: Cambridge, UK, 2007; pp. 56–80. [Google Scholar]
  76. Gallardo, B. Europe’s top 10 invasive species: Relative importance of climatic, habitat and socio-economic factors. Ethol. Ecol. Evol. 2014, 26, 130–151. [Google Scholar] [CrossRef] [Green Version]
  77. Margolis, M.; Shogren, J.F.; Fischer, C. How trade politics affect invasive species control. Ecol. Econ. 2005, 52, 305–313. [Google Scholar] [CrossRef] [Green Version]
  78. Hulme, P.E. Invasion pathways at a crossroad: Policy and research challenges for managing alien species introductions. J. Appl. Ecol. 2015, 52, 1418–1424. [Google Scholar] [CrossRef]
  79. Cantini, M.; Menchetti, M.; Vannini, A.; Bruni, G.; Borri, B.; Mori, E. Checklist of Amphibians and Reptiles in a hilly area of Southern Tuscany (Central Italy): An update. Herpetol. Notes 2013, 6, 223–228. [Google Scholar]
  80. Vannini, A.; Menchetti, M.; Mori, E. L’avifauna del SIC “Poggi di Prata” (Grosseto, Italia Centrale): Analisi faunistica, quantitativa e considerazioni sulla gestione ambientale del sito. Alula 2013, 20, 101–112. [Google Scholar]
  81. Mori, E.; Menchetti, M.; Dondini, G.; Biosa, D.; Vergari, S. Theriofauna of Site of Community Importance Poggi di Prata (Grosseto, Central Italy): Terrestrial mammals and preliminary data on Chiroptera. Check List 2014, 10, 718–723. [Google Scholar] [CrossRef] [Green Version]
  82. BirdLife International. Streptopelia turtur. In The IUCN Red List of Threatened Species; BirdLife International: Cambridge, UK, 2019. [Google Scholar] [CrossRef]
  83. Gippoliti, S.; Amori, G. Ancient introductions of mammals in the Mediterranean Basin and their implications for conservation. Mammal Rev. 2006, 36, 37–48. [Google Scholar] [CrossRef]
  84. Kemp, M.E.; Mychajliw, A.M.; Wadman, J.; Goldberg, A. 7000 years of turnover: Historical contingency and human niche construction shape the Caribbean’s Anthropocene biota. Proc. R. Soc. B Biol. Sci. 2020, 287, 20200447. [Google Scholar] [CrossRef]
  85. Halevi, G.; Moed, H.; Bar-Ilan, J. Suitability of Google Scholar as a source of scientific information and as a source of data for scientific evaluation—Review of the literature. J. Informetr. 2017, 11, 823–834. [Google Scholar] [CrossRef]
  86. Olson, D.M.; Dinerstein, E. The Global 200: A representation approach to conserving the Earth’s most biologically valuable ecoregions. Conserv. Biol. 1998, 12, 502–515. [Google Scholar] [CrossRef] [Green Version]
  87. Olson, D.M.; Dinerstein, E.; Wikramanayake, E.D.; Burgess, N.D.; Powell, G.V.N.; Underwood, E.C.; D’Amico, J.A.; Itoua, I.; Strand, H.E.; Morrison, J.C.; et al. Terrestrial ecoregions of the world: A new map of life on Earth. Bioscience 2001, 51, 933–938. [Google Scholar] [CrossRef]
  88. Burnham, K.P.; Anderson, D.R. Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach; Springer: New York, NY, USA, 2002; p. 488. [Google Scholar]
  89. Burnham, K.P.; Anderson, D.R. Multimodel inference: Understanding AIC and BIC in model selection. Sociol. Methods Res. 2004, 33, 261–304. [Google Scholar] [CrossRef]
  90. Burnham, K.P.; Anderson, D.; Huyvaert, K. AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behav. Ecol. Sociobiol. 2011, 65, 23–35. [Google Scholar] [CrossRef]
  91. D’Amico, M.; Román, J.; de los Reyes, L.; Revilla, E. Vertebrate road-kill patterns in Mediterranean habitats: Who, when and where. Biol. Conserv. 2015, 191, 234–242. [Google Scholar] [CrossRef]
  92. Grilo, C.; Koroleva, E.; Andrášik, R.; Bíl, M.; González-Suárez, M. Roadkill risk and population vulnerability in European birds and mammals. Front. Ecol. Environ. 2020, 18, 323–328. [Google Scholar] [CrossRef]
  93. Medrano-Vizcaíno, P.; Grilo, C.; Silva Pinto, F.A.; Carvalho, W.D.; Melinski, R.D.; Schultz, E.D.; González-Suárez, M. Roadkill patterns in Latin American birds and mammals. Glob. Ecol. Biogeogr. 2022, 31, 1756–1783. [Google Scholar] [CrossRef]
  94. Eberhardt, E.; Mitchell, S.; Fahrig, L. Road kill hotspots do not effectively indicate mitigation locations when past road kill has depressed populations. J. Wildl. Manag. 2013, 77, 1353–1359. [Google Scholar] [CrossRef]
  95. Quiles, P.; Ascensão, F.; D’Amico, M.; Revilla, E.; Barrientos, R. Are road-kills representative of wildlife community obtained from atlas data? Hystrix 2021, 32, 89–94. [Google Scholar]
  96. Delgado, J.D.; Arévalo, J.R.; Fernández-Palacios, J.M. Road and topography effects on invasion: Edge effects in rat foraging patterns in two oceanic island forests (Tenerife, Canary Islands). Ecography 2001, 24, 539–546. [Google Scholar] [CrossRef]
  97. Hollings, T.; Jones, M.; Mooney, N.; McCallum, H. Disease-induced decline of an apex predator drives invasive dominated states and threatens biodiversity. Ecology 2016, 97, 394–405. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  98. Schwartz, A.L.; Williams, H.F.; Chadwick, E.; Thomas, R.J.; Perkins, S.E. Roadkill scavenging behaviour in an urban environment. J. Urban Ecol. 2018, 4, juy006. [Google Scholar] [CrossRef]
  99. Dhiab, O.; D’Amico, M.; Selmi, S. Experimental evidence of increased carcass removal along roads by facultative scavengers. Environ. Monit. Assess. 2023, 195, 216. [Google Scholar] [CrossRef] [PubMed]
  100. Ascensão, F.; D’Amico, M.; Martins, R.C.; Rebelo, R.; Barbosa, A.M.; Bencatel, J.; Barrientos, R.; Abellán, P.; Tella, J.L.; Cardador, L.; et al. Distribution of alien tetrapods in the Iberian Peninsula. NeoBiota 2021, 64, 1–21. [Google Scholar] [CrossRef]
  101. Underwood, E.C.; Viers, J.H.; Klausmeyer, K.R.; Cox, R.L.; Shaw, M.R. Threats and biodiversity in the Mediterranean biome. Divers. Distrib. 2009, 15, 188–197. [Google Scholar] [CrossRef]
  102. Pyšek, P.; Richardson, D.M. Invasive species, environmental change and management, and health. Annu. Rev. Environ. Resour. 2010, 35, 25–55. [Google Scholar] [CrossRef] [Green Version]
  103. Pyšek, P.; Jarošík, V.; Hulme, P.E.; Pergl, J.; Hejda, M.; Schaffner, U.; Vilà, M. A global assessment of invasive plant impacts on resident species, communities and ecosystems: The interaction of impact measures, invading species’ traits and environment. Glob. Change Biol. 2012, 18, 1725–1737. [Google Scholar] [CrossRef]
  104. Shi, J.; Luo, Y.Q.; Zhou, F.; He, P. The relationship between invasive alien species and main climatic zones. Biodivers. Conserv. 2010, 19, 2485–2500. [Google Scholar] [CrossRef]
  105. Weidema, I.R. Introduced Species in the Nordic Countries; Nord 2000 Editions: Aarhus, Denmark, 2020; p. 242. [Google Scholar]
  106. Zhou, Y.; Smith, S.J.; Zhao, K.; Imhoff, M.; Thomson, A.; Bond-Lamberty, B.; Asrar, G.R.; Zhang, X.; He, C.; Elvidge, C.D. A global map of urban extent from nightlights. Environ. Res. Lett. 2015, 10, 054011. [Google Scholar] [CrossRef]
  107. Kuemmerle, T.; Levers, C.; Erb, K.; Estel, S.; Jepsen, M.R.; Müller, D.; Plutzar, C.; Stürck, J.; Verkerk, P.J.; Reenberg, A. Hotspots of land use change in Europe. Environ. Res. Lett. 2016, 11, 064020. [Google Scholar] [CrossRef] [Green Version]
  108. Richardson, H.W.; Bae, C.H.C. Urban Sprawl in Western Europe and the United States; Routledge: London, UK, 2017; p. 344. [Google Scholar]
  109. Masseti, M.; Albarella, U.; De Grossi Mazzorin, J. The crested porcupine, Hystrix cristata L., 1758, in Italy. Anthropozoologia 2010, 45, 27–42. [Google Scholar] [CrossRef]
  110. Esattore, B.; Saggiomo, L.; Sensi, M.; Francia, V.; Cherin, M. Tell me what you eat and I’ll tell you…where you live: An updated review of the worldwide distribution and foraging ecology of the fallow deer (Dama dama). Mamm. Biol. 2022, 102, 321–338. [Google Scholar] [CrossRef]
  111. Moreira, F.; Allsopp, N.; Esler, K.J.; Wardell-Johnson, G.; Ancillotto, L.; Arianoutsou, M.; Clary, J.; Brotons, L.; Clavero, M.; Dimitrakopoulos, P.G.; et al. Priority questions for biodiversity conservation in the Mediterranean biome: Heterogeneous perspectives across continents and stakeholders. Conserv. Sci. Pract. 2019, 1, e118. [Google Scholar] [CrossRef] [Green Version]
  112. Newbold, T.; Oppenheimer, P.; Etard, A.; Williams, J.J. Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change. Nat. Ecol. Evol. 2020, 4, 1630–1638. [Google Scholar] [CrossRef]
  113. Garriga, N.; Santos, X.; Montori, A.; Richter-Boix, A.; Franch, M.; Llorente, G.A. Are protected areas truly protected? The impact of road traffic on vertebrate fauna. Biodivers. Conserv. 2012, 21, 2761–2774. [Google Scholar] [CrossRef]
  114. Kang, W.; Minor, E.S.; Woo, D.; Lee, D.; Park, C.R. Forest mammal roadkills as related to habitat connectivity in protected areas. Biodivers. Conserv. 2016, 25, 2673–2686. [Google Scholar] [CrossRef]
  115. Collinson, W.J.; Marneweck, C.; Davies-Mostert, H.T. Protecting the protected: Reducing wildlife roadkill in protected areas. Anim. Conserv. 2019, 22, 396–403. [Google Scholar] [CrossRef]
  116. Mori, E.; Bini, A.; Viviano, A.; Bartolommei, P.; Mazza, G. Distribution of introduced American mink in the Northern Apennine area (Central Italy). Mammalia 2022, 86, 266–270. [Google Scholar] [CrossRef]
  117. Périquet, S.; Roxburgh, L.; Le Roux, A.; Collinson, W.J. Testing the value of citizen science for roadkill studies: A case study from South Africa. Front. Ecol. Evol. 2018, 6, 15. [Google Scholar] [CrossRef] [Green Version]
  118. Valerio, F.; Basile, M.; Balestrieri, R. The identification of wildlife-vehicle collision hotspots: Citizen science reveals spatial and temporal patterns. Ecol. Process. 2021, 10, 6. [Google Scholar] [CrossRef]
  119. Aristeidou, M.; Herodotou, C.; Ballard, H.L.; Young, A.N.; Miller, A.E.; Higgins, L.; Johnson, R.F. Exploring the participation of young citizen scientists in scientific research: The case of iNaturalist. PLoS ONE 2021, 16, e0245682. [Google Scholar] [CrossRef]
  120. Mesaglio, T.; Callaghan, C.T. An overview of the history, current contributions and future outlook of iNaturalist in Australia. Wildl. Res. 2021, 48, 289–303. [Google Scholar] [CrossRef]
  121. Wolf, S.; Mahecha, M.D.; Sabatini, F.M.; Wirth, C.; Bruelheide, H.; Kattge, J.; Martínez, A.M.; Mora, K.; Kattenborn, T. Citizen science plant observations encode global trait patterns. Nat. Ecol. Evol. 2022, 6, 1850–1859. [Google Scholar] [CrossRef]
  122. Otranto, D.; Dantas-Torres, F.; Mihalca, A.D.; Traub, R.J.; Lappin, M.; Baneth, G. Zoonotic parasites of sheltered and stray dogs in the era of the global economic and political crisis. Trends Parasitol. 2017, 33, 813–825. [Google Scholar] [CrossRef]
  123. Dong, S. Overview: Pastoralism in the World. In Building Resilience of Human-Natural Systems of Pastoralism in the Developing World; Dong, S., Kassam, K.A., Tourrand, J., Boone, R., Eds.; Springer International Publishing: Cham, Switzerland, 2016; p. 298. [Google Scholar]
  124. Giuntini, S.; Ancillotto, L.; Mori, E.; Viviano, A. The Alexandrine parakeet Psittacula eupatria as a naturalized breeding species: A proposal of integration to the national official checklist. Avocetta 2021, 45, 177–179. [Google Scholar]
  125. Di Nicola, M.; Cavigioli, L.; Luiselli, L.; Andreone, F. Anfibi & Rettili d’Italia; Belvedere, Ed.; Historia Naturae: Latina, Italy, 2021; p. 576. [Google Scholar]
  126. McKinney, M.L. Influence of settlement time, human population, park shape and age, visitation and roads on the number of alien plant species in protected areas in the USA. Divers. Distrib. 2002, 8, 311–318. [Google Scholar] [CrossRef]
  127. Cardador, L.; Carrete, M.; Gallardo, B.; Tella, J.L. Combining trade data and niche modelling improves predictions of the origin and distribution of non-native European populations of a globally invasive species. J. Biogeogr. 2016, 43, 967–978. [Google Scholar] [CrossRef] [Green Version]
  128. Mazzamuto, M.V.; Wauters, L.A.; Koprowski, J.L. Exotic pet trade as a cause of biological invasions: The case of tree squirrels of the genus Callosciurus. Biology 2021, 10, 1046. [Google Scholar] [CrossRef] [PubMed]
Biology 12 00850 g001 550
Figure 1. Study area and roadkilled species during the fieldwork survey. On the left is the location of the study area (Central Italy, Tuscany). Roadkill surveys started in the village of Prata (Province of Grosseto) in the direction of Grosseto and then Siena (101 km). On the right is the number of individuals of each roadkilled species.
Figure 1. Study area and roadkilled species during the fieldwork survey. On the left is the location of the study area (Central Italy, Tuscany). Roadkill surveys started in the village of Prata (Province of Grosseto) in the direction of Grosseto and then Siena (101 km). On the right is the number of individuals of each roadkilled species.
Biology 12 00850 g001
Biology 12 00850 g002 550
Figure 2. Global literature review of roadkill studies. Country location of retained studies (N = 41). We retained articles focusing on the whole community of terrestrial vertebrates and including an explicit list of roadkilled species. For each considered country, we provided the percentage of introduced species on the total of roadkilled species.
Figure 2. Global literature review of roadkill studies. Country location of retained studies (N = 41). We retained articles focusing on the whole community of terrestrial vertebrates and including an explicit list of roadkilled species. For each considered country, we provided the percentage of introduced species on the total of roadkilled species.
Biology 12 00850 g002
Biology 12 00850 g003 550
Figure 3. Percentage of introduced species in different biomes, including both the percentage of introduced species on the total of roadkilled species (circles) and on the total of existing species (triangles).
Figure 3. Percentage of introduced species in different biomes, including both the percentage of introduced species on the total of roadkilled species (circles) and on the total of existing species (triangles).
Biology 12 00850 g003
Biology 12 00850 g004 550
Figure 4. Percentage of introduced species on the total of roadkilled species in different countries, from the model considering all retained studies focusing on Mediterranean biomes.
Figure 4. Percentage of introduced species on the total of roadkilled species in different countries, from the model considering all retained studies focusing on Mediterranean biomes.
Biology 12 00850 g004
Table
Table 1. Factors affecting the percentage of introduced species on the total of roadkilled species (upper table) and the percentage of introduced species on the total of existing species (lower table), model ranks by AICs. The upper table shows the model selection for the roadkill dataset (i.e., literature review on roadkilled terrestrial vertebrates plus our own field data), whereas the lower table shows the model selection for the all-species dataset (i.e., literature review on introduced terrestrial vertebrates in different countries). K is the number of model parameters. ΔAICc is the relative difference between a given AIC value compared to the smallest AICc value. AICc weights (wAICc) indicate the relative support for every model (the weights of all the models in the candidate set have the sum of 1). Evidence ratio (ER) is the ratio of wAIC, comparing the best-supported model with every competing one. Model rank is provided only for the best-supported models. R2 is a measure of the global fit of each model. Into the model column, Nc are neighbouring countries.
Table 1. Factors affecting the percentage of introduced species on the total of roadkilled species (upper table) and the percentage of introduced species on the total of existing species (lower table), model ranks by AICs. The upper table shows the model selection for the roadkill dataset (i.e., literature review on roadkilled terrestrial vertebrates plus our own field data), whereas the lower table shows the model selection for the all-species dataset (i.e., literature review on introduced terrestrial vertebrates in different countries). K is the number of model parameters. ΔAICc is the relative difference between a given AIC value compared to the smallest AICc value. AICc weights (wAICc) indicate the relative support for every model (the weights of all the models in the candidate set have the sum of 1). Evidence ratio (ER) is the ratio of wAIC, comparing the best-supported model with every competing one. Model rank is provided only for the best-supported models. R2 is a measure of the global fit of each model. Into the model column, Nc are neighbouring countries.
Roadkill Model Selection
ModelKAICcΔAICcwAICcERRankR2
Latitude3244.60.00.781.010.15
Nc4247.93.30.155.020.21
Biome5250.55.90.0419.030.16
Null2251.77.10.0234.7-0.00
GDP4256.211.60.01324.5-0.01
Full10258.313.60.00918.0-0.32
All-Species Model Selection
Model AICcΔAICcwAICcERRankR2
Biome5187.90.00.991.010.19
Latitude3197.39.30.01107.0-0.54
Full10199.211.20.00276.7-0.57
Nc4205.517.60.006555.3-0.23
Null2210.222.30.0070,829.4-0.00
GDP4214.126.10.00475,931.8-0.02
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Viviano, A.; D’Amico, M.; Mori, E. Aliens on the Road: Surveying Wildlife Roadkill to Assess the Risk of Biological Invasion. Biology 2023, 12, 850. https://doi.org/10.3390/biology12060850

AMA Style

Viviano A, D’Amico M, Mori E. Aliens on the Road: Surveying Wildlife Roadkill to Assess the Risk of Biological Invasion. Biology. 2023; 12(6):850. https://doi.org/10.3390/biology12060850

Chicago/Turabian Style

Viviano, Andrea, Marcello D’Amico, and Emiliano Mori. 2023. "Aliens on the Road: Surveying Wildlife Roadkill to Assess the Risk of Biological Invasion" Biology 12, no. 6: 850. https://doi.org/10.3390/biology12060850

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop