Coupling landscape graph modeling and biological data: a review

Abstract

ContextLandscape graphs are widely used to model networks of habitat patches. As they require little input data, they are particularly suitable for supporting conservation decisions (and decisions about other issues as e.g. disease spread) taken by land planners. However, it may be problematic to use these methods in operational contexts without validating them with empirical data on species or communities. ObjectivesSince little is known about methodological alternatives for coupling landscape graphs with biological data, we have made an exhaustive review of these methods to analyze links between the main purposes of the studies, the way landscape graphs are constructed and used, the type of field data, and the way these data are integrated into the analysis. MethodsWe systematically describe a corpus of 71 scientific papers dealing with terrestrial species, with particular emphasis on methodological choices and contexts of the studies. ResultsDespite a great variability of types of biological data and coupling strategies, our analyses reveal a dichotomy according to the objective of the studies, between (i) approaches aimed at improving ecological knowledge, mainly based on land-cover maps and using biological data to test the influence of landscape connectivity on biological responses, and (ii) approaches with an operational aim, in which biological data are directly integrated into the graph construction and assuming a positive effect of connectivity. ConclusionsBeyond these main contrasts, the review shows that landscape graphs can benefit from field data of different types at varying scales. The great variability of approaches adopted reveals the flexible nature of these tools.

How to assess the temporal dynamics of landscape connectivity in ever-changing landscapes: a literature review

“…Whatever the type of landscape graph used, the impact of the temporal dynamics of connectivity on biodiversity can be either assumed or tested. This dichotomy is found in all studies of connectivity using landscape graphs (Foltête et al 2020). The first set of studies investigates the temporal dynamics of connectivity without testing their effects on biodiversity.…”

Pooling biodiversity offsets to improve habitat connectivity and species conservation

“…Finally, models based on graph theory provide interesting leads to implement biodiversity offsets by contributing to preserving biodiversity and the functionality of natural environments (Foltête, 2019). But we can note two major limitations: they are only based on the availability of spatially explicit data on species' habitat and their uncertainty is rarely assessed (Gippoliti and Battisti, 2017; but see Foltête et al, 2020). Therefore, even if our understanding of connectivity is improved by modelling approaches, we will still need to collect field data, on species behavior, habitat quality and demography (Kool et al, 2013).…”

A Validation Procedure for Landscape Connectivity Approaches: Evaluation of The Accuracy of Ecological Corridor Locations.

“…2018) used validation methods based on observed dispersal paths de ned from GPS locations to compare the output of connectivity approaches based on resistance surfaces estimated from presence-only data, telemetry and genetic data. However, in a review of the use of biological data in combination with landscape graphs, Foltête et al (2020) found that telemetry and molecular data represent only 8.4% of the studies and are much less used than presence data, that can be easily extracted from existing databases, especially in studies with operational objectives. Therefore, further validation procedures are required.…”

“…Therefore, further validation procedures are required. Foltête et al (2020) found few papers that used biological data (regardless of data type) both a priori and a posteriori while the classical statistical procedure of validation consists in calibrating a model with a subsample of data and validating it with another subsample (Fielding and Bell, 1997). The a priori use of biological data informs landscape connectivity approaches of species requirements (e.g.…”