Does regional landscape connectivity influence the location of roe deer roadkill hotspots?

Girardet, Xavier; Conruyt-Rogeon, Géraldine; Foltête, Jean-Christophe

doi:10.1007/s10344-015-0950-4

Cited by 45 publications

(30 citation statements)

References 57 publications

(82 reference statements)

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“…We considered the outline of the parks as nodes, or the population sources, and the remaining raster pixels as the matrix. We assigned resistance values to each land cover class in the matrix according to its resistance to deer movement ( 26 – 28 ) and gene flow ( 29 ). Although focusing on deer, the resistance values broadly represented connectivity for other known host species of I. scapularis ticks and B. burgdorferi (Appendix Table 2).…”

Section: Methodsmentioning

confidence: 99%

Enhancement of Risk for Lyme Disease by Landscape Connectivity, New York, New York, USA

VanAcker¹,

Little²,

Molaei³

et al. 2019

Emerg. Infect. Dis.

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Most tickborne disease studies in the United States are conducted in low-intensity residential development and forested areas, leaving much unknown about urban infection risks. To understand Lyme disease risk in New York, New York, USA, we conducted tick surveys in 24 parks throughout all 5 boroughs and assessed how park connectivity and landscape composition contribute to Ixodes scapularis tick nymphal densities and Borrelia burgdorferi infection. We used circuit theory models to determine how parks differentially maintain landscape connectivity for white-tailed deer, the reproductive host for I. scapularis ticks. We found forested parks with vegetated buffers and increased connectivity had higher nymph densities, and the degree of park connectivity strongly determined B. burgdorferi nymphal infection prevalence. Our study challenges the perspective that tickborne disease risk is restricted to suburban and natural settings and emphasizes the need to understand how green space design affects vector and host communities in areas of emerging urban tickborne disease.

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Section: Methodsmentioning

confidence: 99%

Enhancement of Risk for Lyme Disease by Landscape Connectivity, New York, New York, USA

VanAcker¹,

Little²,

Molaei³

et al. 2019

Emerg. Infect. Dis.

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“…Other software applications exist, e.g. Graphab (Foltête et al, 2012), that translate the landscape as a graph and can be applied in studies related with the impact of linear infrastructures (Girardet et al, 2015). However, this approach is also based upon roadkill data, with all the related issues discussed above.…”

Section: Introductionmentioning

confidence: 99%

gDefrag: A graph-based tool to help defragmenting landscapes divided by linear infrastructures

Mestre

Ascensão

Barbosa

2019

Ecological Modelling

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Habitat fragmentation is a major biodiversity threat. Linear infrastructures (e.g. roads) hamper the movement of individuals and cause non-natural mortality. Roadkill hotspots have been used to define priority areas for road effect mitigation, but data availability and reliability is an issue, particularly on wide spatial scales. Additionally, mitigating the whole infrastructure network is unfeasible. Expedite methods are required to address such challenges. We present the gDefrag package, a graph-based approach that builds on habitat value and accessibility after simplifying the landscape as a graph. Its advantages include not requiring roadkill or movement data, and providing effective methods to deliver reliable information, allowing landscape managers to address landscape fragmentation overall. gDefrag prioritizes roads which should be targeted first to defragment the landscape. The software includes a user-friendly manual and currently implements four prioritization criteria: habitat quality, maximum number of inter-habitat paths, overall landscape connectivity, and simultaneously larger and higher-quality habitats.

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“…For instance, edge weights can quantify interaction frequency (e.g., visitation networks, Martín González et al, 2015), interaction strength (e.g., per-capita effect of one species on the growth rate of another, Brose et al, 2005), carbon-flow between trophic levels (Jordán, Liu, & Davis, 2006), genetic similarity (Rozenfeld et al, 2008), niche overlap (e.g., number of shared resources between two species, Pires, Marquitti, & Guimarães, 2017), affinity (e.g. Luthe & Wyss, 2016), dispersal probabilities (e.g., the rate at which individuals of a population move between patches, Zamborain-Mason, Russ, Abesamis, Bucol, & Connolly, 2017), cost of dispersal between patches (e.g., resistance, Girardet, Conruyt-Rogeon, & Foltête, 2015), etc.…”

Section: Introductionmentioning

confidence: 99%

“…The choice of a centrality measure thus depends on the research question at hand, and on the characteristics of the data being analyzed. Different centrality measures have been used to identify keystone species in networks of biotic interactions (Jordán, 2009;Martín González, Dalsgaard, & Olesen, 2010), to explore the robustness of metapopulations (Thompson, Rayfield, & Gonzalez, 2015), to describe connectivity patterns across fragmented habitats (Carroll, McRae, & Brookes, 2012), to explore social behavior and pathogen spread within populations (Aplin et al, 2013), and to provide a theoretical background to support decision-making in conservation planning and urban management (Girardet, Conruyt-Rogeon, & Foltête, 2015;Poodat, Arrowsmith, Fraser, & Gordon, 2015; see Supporting Information for a complete list). Given the wide array of available techniques and the span of ecological applications, confusion may arise when performing and reporting centrality analysis.…”

Section: Introductionmentioning

confidence: 99%

Ecological networks: Pursuing the shortest path, however narrow and crooked

Costa

González

Doglioli

et al. 2018

Preprint

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21Representing data as networks cuts across all sub-disciplines in ecology and evolutionary biology. 22Besides providing a compact representation of the interconnections between agents, network analysis 23 allows the identification of especially important nodes, according to various metrics that often rely on 24 the calculation of the shortest paths connecting any two nodes. While the interpretation of a shortest 25 paths is straightforward in binary, unweighted networks, whenever weights are reported, the 26 calculation could yield unexpected results. We analyzed 129 studies of ecological networks published 27 in the last decade and making use of shortest paths, and discovered a methodological inaccuracy 28 related to the edge weights used to calculate shortest paths (and related centrality measures), 29 particularly in interaction networks. Specifically, 49% of the studies do not report sufficient information 30 on the calculation to allow their replication, and 61% of the studies on weighted networks may contain 31 errors in how shortest paths are calculated. Using toy models and empirical ecological data, we show 32 how to transform the data prior to calculation and illustrate the pitfalls that need to be avoided. We 33 conclude by proposing a five-point check-list to foster best-practices in the calculation and reporting of 34 centrality measures in ecology and evolution studies. 42Data might either simply report the presence/absence of an edge (binary, unweighted networks), or 43 provide a strength for each edge (weighted networks). In turn, these weights can represent a variety of 44 ecologically-relevant quantities, depending on the system being described. For instance, edge weights 45 can quantify interaction frequency (e.g., visitation networks 5 ), interaction strength (e.g., per-capita 46 effect of one species on the growth rate of another 3 ), carbon-flow between trophic levels 6 , genetic 47 similarity 7 , niche overlap (e.g., number of shared resources between two species 8 ), affinity 9 , dispersal 48 probabilities (e.g., the rate at which individuals of a population move between patches 10 ), cost of 49 dispersal between patches (e.g., resistance 11 ), etc. 51Despite such large variety of ecological network representations, a common task is the identification of 52 nodes of high importance, such as keystone species in a food web, patches acting as stepping stones 53 in a dispersal network, or genes with pleiotropic effects. The identification of important nodes is 54 typically accomplished through centrality measures 5,12 . A large number of centrality measures has 55 been proposed, each probing complementary aspects of node-to-node relationships 13 . For instance, 56Closeness centrality 14,15 highlights nodes that are "near" to all other nodes in the network in terms of 57 average distance (calculated as number of edges) from all other nodes. Whenever the effects of a 58 node on another weaken along the path 16 , then central nodes are those having the largest capacity to 59 influence the others...

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Does regional landscape connectivity influence the location of roe deer roadkill hotspots?

Abstract: International audienc

Cited by 45 publications

References 57 publications

Enhancement of Risk for Lyme Disease by Landscape Connectivity, New York, New York, USA

Enhancement of Risk for Lyme Disease by Landscape Connectivity, New York, New York, USA

gDefrag: A graph-based tool to help defragmenting landscapes divided by linear infrastructures

Ecological networks: Pursuing the shortest path, however narrow and crooked

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