Abstract
Context
Landscape fragmentation negatively impacts species populations by isolating them. Assessing landscape connectivity could help to improve biodiversity conservation. Among various methods available to model and analyse connectivity, graph-theoretic approaches are recognized as powerful tools, even if their ecological significance may be questionable in some cases. Indeed, there are many ways to construct a landscape graph and their impacts on the assessment of connectivity are rarely explored.
Objectives
Our aim was to compare three methods of constructing landscape graphs to identify differences and similarities in the resulting network connectivity. The methods can be distinguished according to the type of data used: expert opinions, field data or a combination of the two. The methodological framework was applied to seven pond-dwelling species (Alytes obstetricans, Bufo bufo, Epidalea calamita, Hyla arborea, Natrix natrix, Rana temporaria, Triturus cristatus) in the Ile-de-France region (France).
Methods
Three common methods were applied to construct landscape graphs: (1) using a land cover map (LM) and expert opinions to define nodes and links; (2) using a habitat suitability model (HSM) and species occurrence data to define nodes and links; and (3) using a HSM to define nodes and a land cover map to define links (HSM_LM). To carry out our study, we produced a land cover map, collected and prepared input data for HSMs, generated HSMs to map the probability of species occurrence and constructed landscape graphs from the three methods. For each of them, several connectivity metrics were calculated and compared.
Results
The results revealed large differences in the statistical distribution of connectivity values, even though the spatial location of the main areas of low and high connectivity was roughly the same. In general, the LM method provided lower values of connectivity and smaller areas of high values than the other two, regardless of species. Conversely, the HSM method had the highest connectivity values, while the combined HSM_LM method appeared to be intermediate.
Conclusions
Our study was not intended to conclude whether one method is better than another; only to point out that results vary greatly depending on the graph construction method. To evaluate the predictive performance of each model, a validation process should be conducted with another independent biological dataset, which was not available in our study. The high variability of results argues for taking care to ensure that the construction of models is carefully consistent with ecological assumptions and the objective pursued.
Similar content being viewed by others
References
Almpanidou V, Mazaris AD, Mertzanis Y, Avraam I, Antoniou I, Pantis JD, Sgardelis SP (2014) Providing insights on habitat connectivity for male Brown Bears: a combination of habitat suitability and landscape graph-based models. Ecol Modell 286:37–44
Baldwin RA (2009) Use of maximum entropy modeling in wildlife research. Entropy 11:854–866
Beier P, Majka DR, Spencer WD (2008) Forks in the road: choices in procedures for designing wildland linkages. Conserv Biol 22:836–851
Cantor SB, Sun CC, Tortolero-Luna G, Richards-Kortum R, Follen M (1999) A comparison of C/B ratios from studies using receiver operating characteristic curve analysis. J Clin Epidemiol 52(9):885–892
Clauzel C, Godet C (2020) Combining spatial modeling tools and biological data for improved multispecies assessment in restoration areas. Biol Conserv (in press)
Clauzel C, Girardet X, Foltête JC (2013) Impact assessment of a high-speed railway line on species distribution: application to the European tree frog (Hyla arborea) in Franche-Comté. J Environ Manag 127:125–134
Clauzel C, Bannwarth C, Foltête JC (2015) Integrating regional-scale connectivity in habitat restoration: an application for amphibian conservation in eastern France. J Nat Conserv 23:98–107
Correa Ayram CA, Mendoza ME, Pérez Salicrup DR, López Granados E (2014) Identifying potential conservation areas in the Cuitzeo Lake basin, Mexico by multitemporal analysis of landscape connectivity. J Nat Conserv 22(5):424–435
Correa Ayram AC, Mendoza ME, Etter A, Salicrup DRP (2015) Habitat connectivity in biodiversity conservation: a review of recent studies and applications. Prog Phys Geogr 40(1):7–37
Dale MRT, Fortin MJ (2010) From graphs to spatial graphs. Annu Rev Ecol Evol Syst 41:21–38
Decout S, Manel S, Miaud C, Luque S (2012) Integrative approach for landscape-based graph connectivity analysis: a case study with the common frog (Rana temporaria) in human-dominated landscapes. Landsc Ecol 27(2):267–279
Dondina O, Saura S, Bani L, Mateo-Sánchez MC (2018) Enhancing connectivity in agroecosystems: focus on the best existing corridors or on new pathways? Landsc Ecol 33(10):1741–1756
Duflot R, Avon C, Roche P, Bergès L (2018) Combining habitat suitability models and spatial graphs for more effective landscape conservation planning: an applied methodological framework and a species case study. J Nat Conserv 46:38–47
Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697
Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans JR, Huettmann F, Leathwick R, Lehmann JA, Li J (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29(2):129–151
Elith J, Phillips SJ, Hastie T, Dudik M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17(1):43–57
Fletcher RJ, Burrell NS, Reichert BE, Vasudev D, Austin JD (2016) Divergent perspectives on landscape connectivity reveal consistent effects from genes to communities. Curr Landsc Ecol Rep 1:67–79
Foltête JC, Clauzel C, Vuidel G (2012) A software tool dedicated to the modelling of landscape networks. Environ Model Softw 38:316–327
Foltête JC, Girardet X, Clauzel C (2014) A methodological framework for the use of landscape graphs in land-use planning. Landsc Urban Plan 124:140–150
Foltête JC, Savary P, Clauzel C, Bourgeois M, Girardet X, Saharoui Y, Vuidel G, Garnier S (2020) Coupling landscape graph modeling and biological data: a review. Landsc Ecol. https://doi.org/10.1007/s10980-020-00998-7
Gaber C (2019) La connectivité fonctionnelle, un enjeu à l’échelle des territoires. Master’s thesis. University Paris Pantheon Sorbonne
Galpern P, Manseau M, Fall A (2011) Patch-based graphs of landscape connectivity: a guide to construction, analysis and application for conservation. Biol Conserv 144:44–55
Graves TA, Farley S, Goldstein MI, Servheen C (2007) Identification of functional corridors with movement characteristics of brown bears on the Kenai Peninsula, Alaska. Landsc Ecol 22:765–772
Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8(9):993–1009
Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Modell 135(2–3):147–186
Guisan A, Zimmermann NE, Thuiller W (2017) In habitat suitability and distribution models: with applications in R ecology, biodiversity and conservation. Cambridge University Press, Cambridge
Huang JL, Andrello M, Martensen AC, Saura S, Liu DF, He JH, Fortin MJ (2020) Importance of spatio-temporal connectivity to maintain species experiencing range shifts. Ecography 43:591–603
IAU (2019) L’inventaire numérique de l’occupation du sol de l’Île-de-France. https://www.institutparisregion.fr/mode-doccupation-du-sol-mos.html
Kadoya T (2008) Assessing functional connectivity using empirical data. Popul Ecol 51:5–15
Karger DN, Conrad O, Böhner J, Kawohl T, Kreft H, Soria-Auza RW, Zimmermann NE, Linder HP, Kessler M (2017) Climatologies at high resolution for the earth’s land surface areas. Sci Data 4:170122
Keeley ATH, Beier P, Gagnon JW (2016) Estimating landscape resistance from habitat suitability: effects of data source and nonlinearities. Landsc Ecol 31(9):2151–2162
Kindlmann P, Burel F (2008) Connectivity measures: a review. Landsc Ecol 23(8):879–890
Lechner AM, Sprod D, Carter O, Lefroy EC (2016) Characterising landscape connectivity for conservation planning using a dispersal guild approach. Landsc Ecol 32:99–113
Liu C, White M, Newell G (2013) Selecting thresholds for the prediction of species occurrence with presence-only data. J Biogeogr 40(4):778–789
Martensen AG, Saura S, Fortin MJ (2017) Spatio-temporal connectivity: assessing the amount of reachable habitat in dynamic landscapes. Methods Ecol Evol 8(10):1253–1264
Martin J, Calenge C, Quenette PY, Allaine D (2008) Importance of movement constraints in habitat selection studies. Ecol Model 213:257–262
McClure ML, Hansen AJ, Inman RM (2016) Connecting models to movements: testing connectivity model predictions against empirical migration and dispersal data. Landsc Ecol 31(7):1419–1432
Merow C, Smith MJ, Silander JA (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36(10):1058–1069
Merow C, Smith MJ, Edwards TC, Guisan A, McMahon SM, Normand S, Thuiller W, Wüest RO, Zimmermann NE, Elith J (2014) What do we gain from simplicity versus complexity in species distribution models? Ecography 37(12):1267–1281
Metz M, Rocchini D, Neteler M (2014) Surface temperatures at the continental scale: tracking changes with remote sensing at unprecedented detail. Remote Sens 6(5):3822–3840
Minor ES, Urban DL (2008) A graph-theory framework for evaluating landscape connectivity and conservation planning. Conserv Biol 22(2):297–307
Murienne J, Guilbert E, Grandcolas P (2009) Species’ diversity in the New Caledonian endemic genera Cephalidiosus and Nobarnus (Insecta: Heteroptera: Tingidae), an approach using phylogeny and species’ distribution modelling. Biol J Linn Soc 97(1):177–184
O’Brien D, Manseau M, Fall A, Fortin MJ (2006) Testing the importance of spatial configuration of winter habitat for woodland caribou: an application of graph theory. Biol Conserv 130:70–83
Oertli B, Frossard PA (2013) Mares et étangs: écologie, gestion, aménagement et valorisation. PPUR, Puerto Princesa
Ortega-Huerta MA, Peterson AT (2008) Modeling ecological niches and predicting geographic distributions: a test of six presence-only methods. Rev Mex Biodivers 79(1):205–221
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. Landsc Ecol 21(7):959–967
Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34(1):102–117
Peterman WE, Rittenhouse TAG, Earl JE, Semlitsch RD (2013) Demographic network and multi-season occupancy modeling of Rana sylvatica reveal spatial and temporal patterns of population connectivity and persistence. Landsc Ecol 28:1601–1613
Peterman WE, Winiarski KJ, Moore CE, da Silva Carvalho C, Gilbert AL, Spear SF (2019) A comparison of popular approaches to optimize landscape resistance surfaces. Landsc Ecol 34(9):2197–2208
Phillips SJ, Dudik M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31(2):161–175
Phillips SJ, Dudík M, Schapire RE (2017) Maxent software for modeling species niches and distributions (Version 3.4. 1). Biodiversity Informatics
Pittman SE, Osbourn MS, Semlitsch RD (2014) Movement ecology of amphibians: a missing component for understanding population declines. Biol Conserv 169:44–53
Poor EE, Loucks C, Jakes A, Urban DL (2012) Comparing habitat suitability and connectivity modeling methods for conserving pronghorn migrations. PLoS ONE 7(11):e49390
Préau C, Grandjean F, Sellier Y, Gailledrat M, Bertrand R, Isselin-Nondedeu F (2020) Habitat patches for newts in the face of climate change: local scale assessment combining niche modelling and graph theory. Sci Rep 10(1):1–13
Rayfield B, Fortin MJ, Fall A (2010) The sensitivity of least-cost habitat graphs to relative cost surface values. Landsc Ecol 25(4):519–532
Rayfield B, Fortin M-J, Fall A (2011) Connectivity for conservation: a framework to classify network measures. Ecology 92:847–858
Rubio L, Saura S (2012) Assessing the importance of individual habitat patches as irreplaceable connecting elements: an analysis of simulated and real landscape data. Ecol Complex 11:28–37
Sahraoui Y, Foltête J-C, Clauzel C (2017) A multi-species approach for assessing the impact of land-cover changes on landscape connectivity. Landsc Ecol 32(9):1819–1835
Saura S, Pascual-Hortal L (2007) A new habitat availability index to integrate connectivity in landscape conservation planning: comparison with existing indices and application to a case study. Landsc Urban Plan 83(2–3):91–103
Saura S, Estreguil C, Mouton C, Rodríguez-Freire M (2011) Network analysis to assess landscape connectivity trends: application to European forests (1990–2000). Ecol Indic 11(2):407–416
Stevenson-Holt CD, Watts K, Bellamy CC, Nevin OT, Ramsey AD (2014) Defining landscape resistance values in least-cost connectivity models for the invasive grey squirrel: a comparison of approaches using expert-opinion and habitat suitability modelling. PLoS ONE 9(11):e112119
Taiyun W, Viliam S (2018) R package “corrplot”: visualization of a Correlation Matrix (Version 0.85). https://github.com/taiyun/corrplot
Urban D, Keitt T (2001) Landscape connectivity: a graph-theoretic perspective. Ecology 82(5):1205–1218
Urban DL, Minor ES, Treml EA, Schick RS (2009) Graph models of habitat mosaics. Ecol Lett 12(3):260–273
Wang YH, Yang KC, Bridgman C, Lin LK (2008) Habitat suitability modelling to correlate gene flow with landscape connectivity. Landsc Ecol 23(8):989–1000
Wilson JW, Sexton JO, Jobe RT, Haddad NM (2013) The relative contribution of terrain, land cover, and vegetation structure indices to species distribution models. Biol Conserv 164:170–176
Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landsc Ecol 27(6):777–797
Zeller KA, Jennings MK, Vickers TW, Ernest HB, Cushman SA, Boyce WM (2018) Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data. Divers Distrib 24(7):868–879
Zetterberg A, Mörtberg UM, Balfors B (2010) Making graph theory operational for landscape ecological assessments, planning, and design. Landsc Urban Plan 95:191
Zucca M, Loïs G, Muratet A, Ricci O (2019) Panorama de la biodiversité francilienne. ARB ÎdF, Paris
Acknowledgements
The authors thank Carole Gaber (funded by ARP-Astrance in 2018–2019) for her preliminary work on pond-dwelling species and Christophe Eggert from the National Society for the Protection of Nature (SNPN) for the provision of the naturalist inventory of ponds database and his advice. Computations were performed on the TGIR Huma-num server (https://www.huma-num.fr).
Funding
This study was funded by the Ile-de-France Region (TRAMARE Project 2019–2020).
Author information
Authors and Affiliations
Contributions
C. Clauzel contributed to the study conception and design. Material preparation, data collection and analysis were performed by C. Godet. The first draft of the manuscript was written by C. Godet and C. Clauzel reviewed and corrected previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Godet, C., Clauzel, C. Comparison of landscape graph modelling methods for analysing pond network connectivity. Landscape Ecol 36, 735–748 (2021). https://doi.org/10.1007/s10980-020-01164-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10980-020-01164-9