On the limitations of graph‐theoretic connectivity in spatial ecology and conservation

Summary 1. Applications of graph-theoretic connectivity are increasing at an exponential rate in ecology and conservation. Here, limitations of these measures are summarized. 2. Graph-theoretic connectivity measures are fundamentally limited as they require specification of a habitat quality threshold to allow definition of habitat patches (nodes). Frequently, a second threshold (critical dispersal distance) is applied in the identification of graph edges. 3. Graph-theoretic measures are poorly applicable to large-scale, high-resolution, grid-based data that describe distributions of species in habitats of varying quality. 4. Graph-theoretic connectivity primarily concerns the emigration-immigration component of spatial population-dynamics. Therefore, it cannot alone answer questions about the regional population size, resilience or persistence of a focal species. 5.Synthesis and applications: Conservation managers in particular should appreciate these limitations before applying graph-theoretic analysis to spatial conservation planning.

[1]  Viral B. Shah,et al.  Using circuit theory to model connectivity in ecology, evolution, and conservation. , 2008, Ecology.

[2]  Santiago Saura,et al.  Ranking individual habitat patches as connectivity providers: Integrating network analysis and patch removal experiments , 2010 .

[3]  Brendan A. Wintle,et al.  The Boundary‐Quality Penalty: a Quantitative Method for Approximating Species Responses to Fragmentation in Reserve Selection , 2007, Conservation biology : the journal of the Society for Conservation Biology.

[4]  Brendan A. Wintle,et al.  Climate change, connectivity and conservation decision making: back to basics , 2009 .

[5]  J. Elith,et al.  Species Distribution Models: Ecological Explanation and Prediction Across Space and Time , 2009 .

[6]  F. Burel,et al.  Connectivity measures: a review , 2008, Landscape Ecology.

[7]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[8]  Andrew Fall,et al.  The sensitivity of least-cost habitat graphs to relative cost surface values , 2010, Landscape Ecology.

[9]  Aurélie Coulon,et al.  Introducing a ‘stochastic movement simulator’ for estimating habitat connectivity , 2011 .

[10]  J. Travis,et al.  Habitat persistence, habitat availability and the evolution of dispersal , 1999, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[11]  Michael Drielsma,et al.  Rapid evaluation of metapopulation persistence in highly variegated landscapes. , 2009 .

[12]  Dean L. Urban,et al.  MODELING ECOLOGICAL PROCESSES ACROSS SCALES , 2005 .

[13]  Dean L Urban,et al.  A Graph‐Theory Framework for Evaluating Landscape Connectivity and Conservation Planning , 2008, Conservation biology : the journal of the Society for Conservation Biology.

[14]  Robert S Schick,et al.  Graph models of habitat mosaics. , 2009, Ecology letters.

[15]  Otso Ovaskainen,et al.  The metapopulation capacity of a fragmented landscape , 2000, Nature.

[16]  I. Hanski.  A Practical Model of Metapopulation Dynamics , 1994 .

[17]  S. Sarkar,et al.  Systematic conservation planning , 2000, Nature.

[18]  Ernesto Estrada,et al.  Using network centrality measures to manage landscape connectivity. , 2008, Ecological applications : a publication of the Ecological Society of America.

[19]  K. Watts,et al.  Developing a functional connectivity indicator to detect change in fragmented landscapes , 2010 .

[20]  Atte Moilanen,et al.  SIMPLE CONNECTIVITY MEASURES IN SPATIAL ECOLOGY , 2002 .

[21]  Atte Moilanen,et al.  Reserve Selection Using Nonlinear Species Distribution Models , 2005, The American Naturalist.

[22]  Brendan A. Wintle,et al.  Habitat area, quality and connectivity: striking the balance for efficient conservation , 2011 .

[23]  Atte Moilanen,et al.  Applying spatial conservation prioritization software and high-resolution GIS data to a national-scale study in forest conservation , 2009 .

[24]  Robert M May,et al.  Network structure and the biology of populations. , 2006, Trends in ecology & evolution.

[25]  S. Saura,et al.  Comparison and development of new graph-based landscape connectivity indices: towards the priorization of habitat patches and corridors for conservation , 2006, Landscape Ecology.

[26]  Santiago Saura,et al.  A new habitat availability index to integrate connectivity in landscape conservation planning : Comparison with existing indices and application to a case study , 2007 .

[27]  Patrick C Phillips,et al.  Network thinking in ecology and evolution. , 2005, Trends in ecology & evolution.

[28]  Timothy H. Keitt,et al.  Landscape connectivity: A conservation application of graph theory , 2000 .

[29]  Justin M. Calabrese,et al.  A comparison-shopper's guide to connectivity metrics , 2004 .

[30]  Ulla Mörtberg,et al.  Making graph theory operational for landscape ecological assessments, planning, and design , 2010 .

[31]  Timothy H. Keitt,et al.  LANDSCAPE CONNECTIVITY: A GRAPH‐THEORETIC PERSPECTIVE , 2001 .

[32]  Atte Moilanen,et al.  Complementarity-based conservation prioritization using a community classification, and its application to riverine ecosystems , 2010 .

[33]  E. Zavaleta,et al.  Biodiversity management in the face of climate change: A review of 22 years of recommendations , 2009 .

[34]  Mark S. Boyce,et al.  Corridors for Conservation: Integrating Pattern and Process , 2006 .

[35]  Elja Arjas,et al.  Bayesian methods for analyzing movements in heterogeneous landscapes from mark-recapture data. , 2008, Ecology.

[36]  Atte Moilanen,et al.  Incorporating consumer–resource spatial interactions in reserve design , 2009 .