Landscape connectivity and spatial prioritization in an urbanising world: A network analysis approach for a threatened amphibian

Abstract

Habitat fragmentation affects amphibian populations worldwide. Urban expansion and associated infrastructure (e.g., roads) are the main cause of degradation and loss of landscapescale habitat connectivity. Mitigation measures such as barriers and underpasses the construction of corridors are frequently implemented to reduce the impacts of development on protected species. However, despite the obvious potential for consequences for outcomes at multiple scales, such efforts generally focus on local outcomes rather than envisioning how the mitigation may contribute to habitat connectivity and populations persistence at a landscape scale. We used a graph-theoretical approach to model structural and functional connectivity ©2019, Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ (corridors) for a widespread but declining endangered pond-breeding amphibian, the great crested newt (Triturus cristatus), by assessing movement among breeding ponds associated with at different processes scales (dispersal, migration and colonization) in. The newts occupied a landscape with different levels of urban and rural development and linear barriers (e.g., roads of different sizes and railwaywith different permeabilities to amphibian movements). This analysis provides critical information to understand the potential capacity for larger scale impacts of mitigation measures (e.g. corridors). We used recent regional pond survey data from great crested newts combined with published data on movement and habitat use to to develop a framework to explore calculate connectivity at the landscape scale-level using estimated annual home-range patches around breeding ponds as terrestrial and wetland habitat units. We identified calculated potential areas of area for terrestrial movement corridors and assessed how landscape connectivity was investigated linkages between patches (potential movement corridors), and then analysed how these linkages were affected by landscape characteristics, such as habitat quality and quantity, and scale of movement and varied between two scenarios representing different degrees of road permeability. The different permeability of linear features (e.g., roads) that cross dispersal corridors influence the effectiveness of the corridor by affecting newt movement. Our results indicate that assessing linear barriers to movement and accounting for differences in their permeabilities is critical to understanding their impact on both shorter term migratory and longer-term dispersal aspect of planning corridors for successful movements in great crested newts. Animal movement is important at various scales, to the individual, the population, and the persistence of species across a landscape. The application of corridors as a mitigation for roads is a workable conservation action but is markedly improved by i Incorporating landscape-scale connectivity modelling which includes to identify the impact of barriers such as roads would substantially...

Habitat patches for newts in the face of climate change: local scale assessment combining niche modelling and graph theory

“…In parallel, relevant actors such as NGOs or other associations in the field of biodiversity protection could systematically bring up the results when consulted on land planning and urban projects, and aware of any risk of habitat destruction or connectivity disruption, particularly in areas identified as sources for these species. Indeed, Matos et al 79 have found impermeable roads to act as barriers to movements between ponds during both migration and dispersal for T. cristatus, whereas smaller and permeable roads increased the size of core areas. Our results can argue for the proposal of measures such as small wildlife crossings to preserve the functionality of the pond network.…”

The Development Simulation of Urban Green Space System Layout Based on the Land Use Scenario: A Case Study of Xuchang City, China

“…In addition to spatial location influencing factors, driving factors, such as economy and population, could be added as well as time variation and spatial differentiation of planning policies and constraint indicators [77]. While considering the protection of ecological space and the accessibility of green spaces, the connectivity of green space also could be taken as an important variable to optimize the layout from the perspective of biodiversity [78][79][80]. In addition, this study also tested the parameter combination of self-adaptive inertia and the competition mechanism of the CA module to obtain an optimal combination.…”

Modeling habitat connectivity in support of multiobjective species movement: An application to amphibian habitat systems

“…If it isn't present in the set of identified supported efficient paths, it is added to that set (step 19) and its Pareto frontier is recorded (step 20). Next, the Pareto frontier of the new solution is then iteratively swapped into the plane of solutions used to derive the weights used in the model to construct three new planes to add to list U � (steps [23][24]. Each of those planes in turn are used to derive three new weighting schemes (steps [26][27] which are added to the list of objective weights to consider (step 28).…”

“…The cost of traversing an analysis area is usually derived based on the assumed contribution of different landscape characteristics present within the area. For example, combinations of landscape characteristics such as forest canopy, land use and land cover, habitat quality, elevation, road density and proximity to water are frequently used in deriving landscape traversal cost [12,[21][22][23]. After each analysis area has been assigned a cost, a network can be constructed in which the nodes represent the analysis areas to be traversed with the arcs representing the spatial connection between neighboring areas.…”

“…Finally, the incumbent node i is removed from Q (step 16) and the process continues until all nodes are visited and labeled. In Stage C, the supported and unsupported non-dominated paths are retrieved by tracking labels, from each destination node back to the origin node using the reference index to the predecessor node embedded in each label (steps [18][19][20][21][22][23][24][25][26][27] and placed into the list AEP.…”