Identifying potential conservation areas in the Cuitzeo Lake basin, Mexico by multitemporal analysis of landscape connectivity

Accepted Manuscript Title: IDENTIFYING POTENTIAL CONSERVATION AREAS IN THE CUITZEO LAKE BASIN, MEXICO BY MULTITEMPORAL ANALYSIS OF LANDSCAPE CONNECTIVITY Author: Camilo A. Correa Ayram Manuel E. Mendoza Diego R. P´erez Salicrup Erna L´opez Granados PII: DOI: Reference:

S1617-1381(14)00049-1 http://dx.doi.org/doi:10.1016/j.jnc.2014.03.010 JNC 25352

To appear in: Received date: Revised date: Accepted date:

13-9-2013 31-3-2014 31-3-2014

Please cite this article as: Ayram, C. A. C., Mendoza, M. E., Salicrup, D. R. P., & Granados, E. L.,IDENTIFYING POTENTIAL CONSERVATION AREAS IN THE CUITZEO LAKE BASIN, MEXICO BY MULTITEMPORAL ANALYSIS OF LANDSCAPE CONNECTIVITY, Journal for Nature Conservation (2014), http://dx.doi.org/10.1016/j.jnc.2014.03.010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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IDENTIFYING POTENTIAL CONSERVATION AREAS IN THE CUITZEO LAKE

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BASIN, MEXICO BY MULTITEMPORAL ANALYSIS OF LANDSCAPE

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CONNECTIVITY Camilo A. Correa Ayram1, Manuel E. Mendoza1*, Diego R. Pérez Salicrup2, Erna López

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Granados3

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de México, Antigua Carretera a Pátzcuaro No. 8701, Col. Ex-Hacienda de San José

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de la Huerta CP 58190, Morelia, Michoacán, México. 52 (443) 322 38 39.

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*[email protected], [email protected]

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Centro Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma

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México, Antigua Carretera a Pátzcuaro No. 8701, Col. ExHacienda de San José de la

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Huerta CP 58190, Morelia, Michoacán México. 52 (443) 332 27 08.

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[email protected]

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Departamento de Geología y Mineralogía, Instituto de Investigaciones

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Centro de Investigaciones en Ecosistemas, Universidad Nacional Autónoma de

Metalúrgicas, Universidad Michoacana de San Nicolás de Hidalgo, Ciudad Universitaria Edificio: U, Morelia, Michoacán, México. Tel. 443 3 22 38 39; [email protected]

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Abstract

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covers that are key elements for the long-term support of biodiversity. We studied landscape

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connectivity changes for the years 1975, 1996, 2000, 2003 and 2008 to identify potential

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conservation areas in the basin. We modeled potential distributions of the Mexican bobcat (Lynx

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rufus escuinapae) and the ringtail (Bassariscus astutus) –two terrestrial mammal focal species

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with contrasting dispersal capacities– and we determined their habitat availability and suitability

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in the basin. We then identified their optimal habitat patches and produced landscape cumulative

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resistance maps, estimated least-cost paths (graph theory approach), and elaborated current flow

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maps (circuit theory approach). For evaluation of landscape connectivity, we applied an integral

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index of connectivity (IIC) to each study period, and determined individual habitat patch

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contribution to the overall landscape connectivity. The IIC index had very low values associated

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with reduced availability of focal species habitat. However, our study showed the conservation

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importance of the surface of optimal habitat patch areas. The combined application of a graph-

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based approach and current flow mapping were useful, and complementary both in terms of

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estimating potential dispersal corridors and identifying high probability dispersal areas. This

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indicated that landscape connectivity analysis is a useful tool for identification of potential

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conservation areas and for local landscape planning.

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Keywords: Landscape connectivity, habitat suitability, landscape resistance, potential corridors,

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graph theory, circuit theory.

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Lake Cuitzeo basin is an important ecological area subjected to strong human pressure on forest

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Introduction

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Landscape connectivity changes have important consequences for conservation of ecosystem

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services and landscape planning. Connectivity can help control the rate of biotic and abiotic

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flows and other important processes to service provision such as population sizes (Mitchell et al.,

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2013). Connectivity increases whenever a change in the structure of the landscape enhances the

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capacity of movement (flow) of organisms across the landscape (Taylor et al., 2006). In

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consequence, the probability of exchanging individuals between isolated populations increases,

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and the extinction rates decrease (Beier, 1995; Bennet, 1999). Maintenance of connectivity

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between habitat patches might improve the capacity of biodiversity to respond to disturbances

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caused by climate change, both because it allows dispersal of organisms and maintains their

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potential migration into areas better suited for their establishment (Heller and Zavaleta, 2009;

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Brost and Beier, 2012; Schloss et al., 2011). Connectivity depends on the changing trend of the

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structural units of the landscape and on the intrinsic needs of the species that inhabit them

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(Hopkins et al., 2007).

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The concept of functional connectivity relates to the characteristics of the landscape that

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facilitate or hamper the mobility of species between habitat patches (Taylor, 1993). The study of

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connectivity integrates the structural characteristics of landscapes with the species dispersal

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capacity within these landscapes (Adriaensen et al., 2003).

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Studies of functional connectivity based on graph theory allow for the identification of the least-

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cost paths between patches for given organisms (Urban and Keith, 2001; Adriaensen et al., 2003;

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Rayfield et al., 2010). Graph theory studies the complexity of structural and functional

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interactions between habitat areas, and the dispersal occurring in them (Urban et al., 2009;

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Zetterberg et al., 2010). In addition, graph theory allows interpretation of the role of the

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landscape matrix (the dominant land cover type in terms of area, degree of connectivity, and

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control that is exerted over dynamics of the landscape; Forman, 1995) in the context of landscape

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connectivity (Bunn et al., 2000; Urban and Keitt, 2001). More recent studies incorporate circuit

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theory for modeling the distribution of the dispersal probabilities of focal species (McRae and

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Beier, 2007; McRae et al., 2008; Epps et al., 2011; Walpole et al., 2012) by taking into account

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all possible routes between habitat patches through a basic connectivity parameter called

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resistance distance (McRae et al., 2008). In contrast, graph theory only takes into account the

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effective distance between a pair of specified patches (McRae et al., 2008). While both

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approaches differ, few efforts have been made to evaluate their complementarity. In the present

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study we integrate both approaches.

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In the Cuitzeo basin, the accelerated increase of urban population during the last 30 years, has

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exerted a strong pressure on natural resources. The processes of anthropic transformation have

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transformed the landscape into one dominated by human-modified land covers and land uses,

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such as grasslands and croplands replacing natural arboreal and shrubby land cover types (López

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et al., 2006; Mendoza et al., 2011). These land cover changes generate environmental problems

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such as habitat fragmentation and reduction of ecological connectivity, which affects the stability

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of animal populations; particularly of mammals (Núñez, 2010).

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The environmental issues in the basin appear to be related to habitat fragmentation (Correa et al.,

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submitted), which is considered one of the main drivers of recent species extinctions (Fahrig,

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2003). Because the variability of populations in fragmented landscapes depends largely on the

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functional connectivity of the landscape, and because landscape connectivity itself depends on

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the habitat preferences of particular species, in this study we analyzed the potential scenarios for

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two focal species of terrestrial mammals with contrasting ecological characteristics but common

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conservation priorities: The Mexican bobcat (Lynx rufus escuinapae) and the ringtail

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(Bassariscus astutus). The Mexican bobcat is classified by the U.S. Fish and Wildlife Service as

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threatened with extinction, and is included in Appendix II of the Convention on International

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Trade in Endangered Species of Wild Flora and Fauna (CITES). The ringtail is also included in

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Appendix II of CITES, and is also contained in the Official Mexican Norm (NOM-059 ECOL-

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2001) under the status of threatened with extinction.

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The gathered information and methodology followed by our study can be of value for those

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involved in decision-making regarding conservation of biodiversity and landscape planning. Our

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general objective was to analyze the changes of landscape functional connectivity for the years

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1975, 1996, 2000, 2003, and 2008, to identify priority areas for conservation of the ecological

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integrity of the Cuitzeo basin.

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Methods

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Study area

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The Lake Cuitzeo basin has an area of 4,000 km2, and is located in the Trans Mexican Volcanic

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Belt within the states of Michoacán and Guanajuato, between the coordinates 19°30´and 20°05´

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latitude north, and 100°35´and 101°30´ longitude (Figure 1; Mendoza et al., 2011). The basin has

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an altitudinal range of 1,800 to 3,420 m a.s.l., the climate is temperate with summer rains, the

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mean annual temperatures oscillate between 14° and 18° C, and the mean annual precipitation is

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about 800 mm (Carlón and Mendoza, 2007; Carlón et al., 2009). Drainage concentrates in the

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lower part of the basin, forming Lake Cuitzeo. Land cover types –such as rainfed and irrigated

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croplands, grasslands, and forest plantations– dominate 50% of the landscape, which indicates a

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high degree of human intervention; arboreal (19%) and shrubby (13%) land covers follow in

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importance (Mendoza et al., 2011), and provide most of the optimal habitat for the fauna of the

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basin. The urban area associated with Morelia (capital of the State of Michoacan) has

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experienced accelerated growth for the past 35 years (López et al., 2001; Mendoza et al., 2011).

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Total human population of the basin increased from 380,782 inhabitants in 1970 to 944,606

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inhabitants in 2010 (INEGI, 1970, 2010).

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Figure 1

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Processing of land cover and land use data

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Land cover and land use data were compiled from previous studies for the analyzed years (1975,

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1996, 2000, 2003 and 2008; López et al., 2006; Mendoza et al., 2011). The spatial databases

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were generalized from 19 to 9 land cover types relevant to the study and standardized to 100m

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spatial resolution. Land cover types used were: 1) closed forests, 2) semi open and open forests,

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3) scrubland, 4) halophilous grasslands, 5) aquatic vegetation, 6) forest plantations, 7) induced

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grasslands and croplands, 8) water bodies, and 9) human settlements. The minimum mappable

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area was 3 ha. All the spatial databases used are in Universal Transversal Mercator (UTM)

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projection, zone 14 N, and use the North American datum 1927 (NAD 27).

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Selection of focal species and modeling of the suitability of habitat

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The analysis of connectivity was based on the concept of focal species (Watts et al., 2007),

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introduced into landscape ecology to refer to the species used for modeling structural and

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functional connectivity of the landscape. Focal species were chosen according to criteria such as

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interest for conservation, potential contrast or complementarity with other species in terms of

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their ecological requirements, availability of valid data, and because they have a restricted

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geographic range despite habitat availability (Watts et al., 2007).

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In the present case study, two terrestrial mammals were chosen that are potentially present in the

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basin. The Mexican bobcat was selected due to its capacity for wide dispersal and large home

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ranges, and because of its specialized diet (Lariviere & Walton 1997; Aranda et al., 2002; Burton

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et al., 2003; Hansen, 2007). In contrast, the ringtail is a generalist capable of living in sites

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disturbed by human activities (Castellanos, 2006), and has much smaller home ranges than those

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of the Mexican bobcat (Castellanos, 2006; Timm et al., 2008).

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The models of potential distribution of the focal species were used to measure habitat suitability

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because they indicate areas having a high probability of occurrence, and are better suited for

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these species –these areas can be considered habitat patches (Pascual Hortal and Saura, 2008;

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Decout et al., 2012). Spatial models were based on 120 points of occurrence of the Mexican

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bobcat and 187 for the ringtail throughout Mexico. The points were obtained both from the

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National Information System About Biodiversity of CONABIO and the literature (Rodríguez-

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Martínez et al., 2007; Bárcenas & Medellín 2007; Orduña, 2008; Burton et al., 2003; Fernández

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et al., 2007). The final distribution maps of the focal species were generated using the software

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MaxEnt (Philips & Dudik, 2008).

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The 19 bio-climatic variables distributed by WorldClim (Hijtman et al., 2005) were included in

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the model, as well as elevation data from the 90 m resolution digital elevation model (DEM)

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distributed by CONABIO, potential vegetation (Rzedowski, 1990), and terrestrial ecoregions

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map for Mexico (INEGI & CONABIO, 2008; 1:1 000,000 scale). For each species, the

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distribution models were elaborated in MaxEnt, and the receiver operating characteristic (ROC)

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curves were calculated both for the training (75%) and the test (25%) data (Philips et al, 2006).

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The output maps were normalized (0 to 1 value), the highest values indicating a higher

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probability of occurrence of the focal species.

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Valuing landscape resistance by expert opinion

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Beier et al. (2009) highlight that expert opinion was used in 15 of 26 (65%) functional

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connectivity studies valuing the resistance of the landscape matrix. With that purpose, a

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questionnaire was generated with eight characteristics of the landscape affecting resistance to

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dispersal, which were ranked from 0 to 100 by eight experts in the ecology of the focal species.

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A high resistance value means a higher difficulty for the focal species moving through the

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geographic space. The variables used were land cover, road type, road density, elevation, slope

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(degrees), human population density, and distance to human settlements. Likewise, two meetings

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were held with an expert in each one of the focal species. All the obtained information was used

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for complementing the selection of patches to be connected.

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The methodology of Compton et al. (2007) was applied to estimate final values of landscape

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resistance by assigning a truncated mean–i.e., the arithmetic mean was calculated after excluding

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the highest and lowest values given by the eight experts for each variable and focal species. The

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use of the truncated mean is advantageous, and it is recognized as a robust estimator, given that it

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is less sensitive than the arithmetic mean to outliers (Kim, 1992).

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Selection of habitat patches

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Modeling of the distribution of habitat patches for each time period required the following

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cartographic information: a) potential habitat of focal species (from MaxEnt) reclassified from 0

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to 100; b) the land cover for each time period, reclassified into three categories according to the

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landscape quality: habitat (land cover types with higher habitat quality and probability of

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maintaining a viable population), hospitable matrix (land cover types in which species may occur

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or transit through but may not establish a stable population), and inhospitable matrix (land cover

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types that do not qualify as habitat nor provide any habitat quality features for the species;

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Tischendorf , 2003; Rayfield et al., 2010; López, 2010); and c) models of suitability by elevation

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and slope respectively; and d) a map of perturbations (distance to roads, distance to human

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settlements, road density).

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In addition, a value from 0 to 100 was assigned to each variable based both on the available

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information for each focal species, and on the information from the applied questionnaires and

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the meetings with experts. High values (ca. 100) correspond to the more suitable areas in terms

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of habitat quality for the focal species. In the case of land cover, high values mean a higher

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probability of survival, and in the case of potential habitat, a larger probability of occurrence of

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the species, and therefore a higher quality habitat (Pascual Hortal and Saura, 2008). The

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elevation corresponds to the altitudinal range in which the species can occur, and the slope

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relates to the difficulty of movement due to the degree of inclination. Higher values for the

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variables labeled as disturbances correspond to higher capacities of the focal species to adapt to

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and to tolerate characteristics.

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Subsequently, the layers of potential habitat (MaxEnt model) and actual habitat (reclassified land

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cover) were added using map algebra and weighted by multiplying them by two because they are

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determinant variables of the quality of the habitat for the focal species (López, 2010). The

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resulting product was added to the values of the remaining variables:

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2 ! HabP ! HabA ! ! ! DR ! RDens ! DHS ! Elev ! Slope!

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where:

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! ! ! ! ! ! !

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Eq. 1,

PHab = Potential habitat from the MaxEnt model AHab = Actual habitat for each time period DR = Distance to roads RDens = Road density DHS = Distance to human settlements Elev = Elevation suitability Slope = Slope suitability

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The resulting map values were divided in quartiles, and the two lower intervals were classified as

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deficient habitat, the next higher interval as suboptimal habitat, and the upper interval as optimal

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habitat (López, 2010). Only those patches having optimal habitat value were taken into account

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in defining the final habitat patches. A new binary model was generated (optimal/non-optimal),

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and the minimum areas of habitat patch were selected. In the case of the Mexican bobcat, these

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patches had a minimum area of 2,000 ha, which approximately corresponds to the minimum area

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in which they can maintain a viable population (Boyle and Fendley, 1987). Information about the

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minimum area of habitat patches for the ringtail was unavailable, and the size of the home range

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was used as a proxy, following the suggestions of Theobald (2006), and of Beier et al. (2006).

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The previously selected habitat patches were visited to validate that the assigned land cover type

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corresponded with that in the most recent land cover map (year 2008). In total, five patches were

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visited, representing 80% of the remnant habitat for the focal species.

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Modeling of the functional connectivity

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Cumulative resistance models based on the truncated means and expert knowledge were

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developed for each studied year and focal species. The non-categorical variables were previously

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reclassified based on the expert’s rankings assigned to them in the questionnaire. Afterwards, the

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cumulative resistance models for each year were obtained by the arithmetical addition of

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individual models.

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The determination of the corridors communicating the habitat patches of the focal species

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required the use of two complementary approaches: one based on least-cost path (LCP)

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(Adriaensen et al., 2003; Theobald, 2006), and one based on current flows (McRae et al., 2008).

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The LCP method is used for estimating effective distances between habitat patches taking into

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account the capacity of dispersal between them. These models mainly require two types of data:

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1) the landscape resistance, indicating the ease or difficulty with which an organism can move

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through a given area; and 2) the distribution of habitat patches between which connectivity is

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measured (Theobald, 2005). The software tool Linkage Mapper (McRae and Kavanagh, 2011)

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was used to calculate potential corridors. The advantage of this tool is that it calculates the LCP

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and models least-cost corridors (LCC) between the minimum resistance values, which are

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expressed in a gradient of cumulative least-cost paths. The resistance values of the corridors are

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normalized by subtraction of the distance of the LCP, representing the minimum value for all

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normalized individual corridors (McRae and Kavanagh, 2011).

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The cost weighted distance ratio was calculated along each modeled potential corridor

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(cwdToPathratio) (WHCWG, 2010) to assess their potential mobility. The index was calculated

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with Linkage Mapper (McRae and Kavanagh, 2011), using the ratio between the cost distance

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and the length of the LCP in each generated corridor.

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The connectivity models based on LCP were supplemented by current flow models derived from

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circuit theory (McRae et al., 2008), to model the probability of connectivity distribution. Those

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models were calculated using the software Circuitscape (Sha and McRae, 2008; McRae and

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Shah, 2011). Circuit analysis can identify pixels with high probability of movement between

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habitat patches based on a high current flow. One advantage of this approach is that it considers

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all possible connections between all habitat patches, while the LCP are calculated only through

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one optimal path. The densities of current flows can be used to identify potential corridors, i.e.

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optimal routes where focal species have a high probability of moving between habitat patches

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(McRae et al, 2008.).

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Evaluation of landscape connectivity

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The evaluation of change in landscape connectivity for each year required the application of the

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integral index of connectivity (IIC) (Saura and Torné, 2009). The IIC integrates in a single value

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of connectivity the attributes of the habitat, and the connectivity of the landscape (Decout et al.,

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2012). Thus, the attributes of the habitat patches of the focal species are represented as nodes,

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and the topological relations or connections between them are represented as links based on

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graph theory. The values of IIC are from 0 to 1, increasing as the connectivity improves. A value

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of 1 is reached in the hypothetical case of the landscape being totally occupied by the habitat.

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The IIC is calculated by the algorithm of Pascual-Hortal and Saura (2006).

IIC !

i! 1 j! 1

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ai ! a j

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were ai is the area of the ith patch, aj is the area of the jth patch, nlij is the number of least-cost

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paths (topological distance) between the ith and jth patches, and AL is the total landscape area.

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The node importance index (dIIC) was calculated to determine the individual contribution of

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each habitat patch to the general connectivity of the landscape. In this case, each habitat patch is

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iteratively removed and the IIC is recalculated. The percentage of connectivity loss measures the

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individual contribution of each habitat patch to the maintenance of connectivity (Pascual Hortal

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and Saura, 2008). The dIIC is calculated as follows.

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dIIC = 100

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where IIC and IIC´ correspond to the values of IIC before and after, respectively, a node is

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removed. The indexes of habitat availability were calculated using the software Conefore

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Sensinode 2.2 (Saura and Torné, 2009). Finally, the obtained values were integrated to the

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habitat patch layer to map the distribution of dIIC.

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IIC ! IIC´ IIC

Eq. 3,

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Results

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Modeling functional connectivity

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the cost and Euclidean distances between habitat patches and the results from the comparison of

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the mean length of the LCP, as well as the relation between the cost distance of the LCP as an

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indicator of the quality of the potential corridors for each studied year.

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Cost distance and least-cost paths

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optimal habitat patches for each year, the ranks of Euclidean distances being relatively low. The

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year 1975 displayed both the lower and the higher mean Euclidean and cost distances (minimum

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mean distance = 0.2 km, minimum mean cost distance = 23.4 km, maximum mean distance =

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53.4, maximum mean cost distance = 4891 km). The range of lengths of the LCP was 25.5 km

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for the year 2000 and 30.9 km for 2003 (Table 1).

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The average of the Euclidean distances peaked in 1996 at 26.1 km, and was lowest in 2000 (21.3

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km). Conversely, the average values of the mean cost distances were higher in 1975 and 1996

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(2308 and 2561, respectively), decreased in 2000 (2091), and gradually increased until 2008.

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Table 1.

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For the ringtail, 156 and 170 links were present each year between 65 habitat patches. The ranges

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of Euclidean distance and of cost distance were relatively low.

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The mean Euclidean and cost distances behaved similarly along the studied years, remaining

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relatively stable from 1975 (5.2 and 665, respectively) to 2003 (4.8 and 626); however, these

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values increased considerably for 2008 (6.0 and 830, respectively) (Table 1).

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Regarding the Mexican bobcat, between 7 and 11 links resulted from the models between 6

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The mean length of the LCP showed the same behavior as the Euclidean and cost distances, in

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1975 it was 6.7 km, it decreased to 6.0 km in 1996, stabilized in 2003, and increased to 8.0 km in

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2008.

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The high values of the cwdToPathratio index (Table 1) for the Mexican bobcat suggest poor

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conditions for mobility between patches, and that in general, the LCP transited through areas

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with high mean resistance to mobility. Also, the link between habitat patches 1 and 3 for the year

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2008 reached the highest cwdToPathratio index value; which corresponds to the year and link

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having the lowest quality for mobility of the Mexican bobcat.

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In contrast, the link between patches 2 and 4 in the year 2003 displayed the highest quality for

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mobility of the feline, offering the least mean resistance along the LCP.

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The general values of the cwdToPathRatio index for the ringtail (Table 1) were higher than those

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calculated for the Mexican bobcat, which would mean that the conditions for mobility along

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links would be worse for the ringtail than for the Mexican bobcat. Nevertheless, the lower

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cwdToPathRatio index indicates the opposite, given that they were lower for the ringtail.

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Changes in the probability of dispersal

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Mexican bobcat. – The areas having a very high probability of connectivity -based on circuit

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theory- covered less than 15% of the study area. The highest probability of dispersal occurred in

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1975, when about 12% of the basin favored the Mexican bobcat. However, between 1975 and

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2000 a considerable decrease of the probability of connectivity was detected, with only 6% of the

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area of the basin facilitating connectivity. In the years 2003 and 2008, a slight increase in

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favorable area for dispersal was modeled, reaching only 8% of the study area.

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The maps (Figure 2) show that the areas of low and medium probability of connectivity for the

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Mexican bobcat are mainly distributed in the central portion of the basin, where croplands are

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concentrated. The higher probability of connectivity occurs along the extremes of the basin,

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mostly coincident with the distribution of the LCP, which indicates that a high probability of

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dispersal is associated with arboreal land cover types that contain optimal habitat patches.

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Figure 2.

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Ringtail. – A comparison of the models of current flow for the Mexican bobcat (Figure 2) and

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the ringtail (Figure 3) reveal a higher probability of connectivity between optimal habitat patches

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for the ringtail. The areas of higher probability of dispersal for the ringtail occupy between 27%

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and 35% of the basin, representing nearly twice the area calculated for the Mexican bobcat.

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In the year 1975, 27% of the study area displayed a high probability of connectivity for the

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ringtail (Figure 3), a percentage that remained relatively stable in 2003, when it increased to

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30%. Finally, in 2008 the area of high probability of connectivity covered about 35% of the

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basin. In contrast to the probability of dispersal of the Mexican bobcat, that of the ringtail was

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only partially affected by the proximity to the urban area of Morelia. Cropland areas were

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associated with low and medium probability of dispersal –as in the case of the Mexican bobcat,

333

but to a lesser degree.

334

Figure 3.

335

Importance of habitat patches for landscape connectivity

336

In this section we present the results of the analysis of changes in landscape connectivity for the

337

studied years taking into account the IIC values, and the results of the categorization of the

338

importance of individual habitat patches for the landscape connectivity in 2008. We have omitted

339

the results for the other years analyzed because they had similar values with little variation in

340

space and time (unpublished data).

341

Changes in the Integral Index of Connectivity (IIC)

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Values of IIC were low for all analyzed periods in both focal species, the Mexican bobcat having

343

an average IIC value of 0.0032, and the ringtail of 0.00089 (Figure 4).

344

The trends of the IIC values for the Mexican bobcat and the ringtail were similar throughout the

345

studied time period. The lower values were registered in the earlier years, and reflect the low

346

availability of habitats between 1975 and 1996, while –due to the increase in habitat availability–

347

the highest values corresponded to the period between 1996 and 2003. For the ringtail, an

348

increase in the IIC values were calculated from 1975 (0.0005) to 1996 (0.0012). The lowest IIC

349

value for both focal species was obtained for 1975, with the index stabilizing between 1996 and

350

2003 at around 0.0012, and reaching the lower value of 0.0007 in 2008. For the Mexican bobcat,

351

the lowest IIC value was of 0.001 in 1975, which increased to 0.004 in 1996, stabilized in 2003,

352

and decreased slightly towards 2008 (0.0032; Figure 4).

353

Figure 4.

354 355 356

Importance of individual habitat patches for landscape connectivity

357

importance category (85%) with an average extent of 20,832 ha, corresponding to 35% of all

358

habitat patches for the species. The category of high importance for connectivity was represented

359

by one habitat patch of the Mexican bobcat, with an average surface of 5,708 ha, 9.6% of the

360

area for all habitat patches (Table 2). Ninety seven percent of the habitat patches for the Mexican

361

bobcat had low importance for connectivity, and relatively low mean extensions (ca. 36.6 ha) in

362

comparison to the higher categories of individual habitat patch importance for connectivity.

363

Table 2.

364

For the ringtail, only one habitat patch –with an extent of 5,729 ha corresponding to 10.3% of the

365

total habitat patch surface– was ranked in the category of very high importance for connectivity,

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A single Mexican bobcat habitat patch was modeled within the very high connectivity

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with a total value of 30%. Three patches ranked in the high importance category, with an extent

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of 18.3% of the total basin (3,394 ha). In general, the habitat patches behaved similarly to those

368

of the Mexican bobcat, the size of patches decreasing as their importance for connectivity

369

increased. A very low importance value corresponded to 96.5% of the habitat patches of the

370

ringtail, with a low average size (34.5 ha) compared to patches in the categories of higher

371

importance for connectivity. However, this very low importance category dominated over 31%

372

of the total habitat patch area (Table 2).

373

Discussion

374

Modeling of the functional connectivity

375

Changes in the cost distances and links of the landscape

376

According to our results the mean lengths of LCP were relatively suited for the mobility of the

377

Mexican bobcat, given it can move between 1.1 and 183 km (Lariviere and Walton, 1997). In

378

general, the corridors for the Mexican bobcat are distributed where suboptimal habitat patches

379

occur, these patches serving as interconnection elements between optimal habitat patches, as is

380

observed in the western and eastern extremes of the basin. The potential corridors can go through

381

the matrix dominated by induced grasslands and croplands, given the presence of suboptimal

382

habitat patches as stepping stones. The high values of cwdToPathratio index suggest poor

383

general conditions for the movement of the Mexican bobcat between habitats, mostly crossing

384

through areas of low habitat quality. In general, the corridors associated to areas of high average

385

resistance to mobility of the feline.

386

The LCP between habitat patches 1 and 3 had the highest quality during 2008, meaning this

387

route may be useful for future conservation strategies (Figure 5). The linked area has the basic

388

pattern (buffer zones, corridors and large habitat patches) needed for its consideration as a

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conservation scenario, in which an interconnected network of large habitat patches is immersed

390

(core reserves; Noss, 1992; Bennet, 1999). As a conservation strategy, it should be stipulated that

391

habitat patches be surrounded by buffer zones linked between them in order to maintain both the

392

functional connectivity of the focal species, and all other ecological processes (Noss, 1992).

393

Figure 5.

394

It became evident that the expansion pattern of the human settlement of Morelia considerably

395

affected the distribution of potential corridors. The city of Morelia is considered as a dispersal

396

barrier, due to its high degree of resistance to movement, and its low quality habitat for the

397

Mexican bobcat. Consequently, the increase in size of the urban area and the decrease in

398

frequency of suboptimal habitat patches (as calculated for 2008), increases the relation between

399

the distance cost and the length of the LCP. This in turn negatively affects the dispersal quality

400

of potential corridors (Figure 6).

401

Habitat patches occupy large extensions, most of which should be protected. These areas have a

402

higher probability of sustaining optimal habitats for a large number of species because the

403

surface of these habitat patches is a primary attribute in the maintenance of landscape

404

connectivity. Buffer zones are areas surrounding habitat patches in which conservation policies

405

can be implemented. However, contrasting with corridors that connect habitat patches with

406

buffer zones in a functional system, buffer areas offer a high resistance to mobility (Bennett,

407

1999). Small sized habitat patches interspersed along the basin are insufficient for harboring

408

viable populations, but can be taken advantage of as stepping stones for connectivity by reducing

409

the resistance to mobility (Rubio and Saura, 2012)

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The LCP for the ringtail are distributed throughout the basin (Figure 6), with links between a

411

large number of optimal habitat patches (64); a distribution caused in part by the high tolerance

412

to landscape resistance of this focal species (Castellanos, 2006).

413

Figure 6.

414

The matrix of induced grasslands and croplands does not hamper the mobility of the ringtail.

415

According to the values of the cwdToPathRatio indexes, the corridors have an average length

416

that is much shorter than that of the Mexican bobcat, and offer less average resistance to

417

mobility, thus favoring dispersal of the ringtail.

418

Information about dispersal distances for the ringtail is scarce, so it is difficult to compare

419

corridor lengths. However, the size of their home range reported for non-urban areas (21-63 ha)

420

(Timm et al., 2008) may indicate that the average corridor length may be relatively adequate for

421

ringtail dispersal.

422

Probability of connectivity

423

According to McRae et al. (2008), the probability of dispersal is influenced by the resistance

424

level of the matrix. Areas with higher current flow have priority for conservation, because they

425

are critical habitat points with high flow density expressed as a high probability for dispersal and

426

low level of resistance, and because they reflect optimal routes between two habitat patches. In

427

the study area, a high probability for dispersal exists along the higher parts of the basin, where

428

optimal habitats also occur, which reinforces the priority for conservation of these zones.

429

However, as our results indicate, such areas of high probability for dispersal of the focal species

430

have decreased and are poorly represented because they always occupied less than 12% of the

431

basin area.

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Following Walpole et al. (2012), application of methods derived from circuit theory in

433

combination with models of potential corridors can be appropriate for conservation and

434

maintenance of functional connectivity because corridors also present high probability for

435

dispersal likely to be used by species mobilizing between habitat patches.

436

Importance of habitat patches for general connectivity

437

In previous studies of functional connectivity of the gray fox (Urocyon cinereoargenteus), and of

438

the Mexican hairy dwarf porcupine (Sphiggurus mexicanus) in the mountain cloud forests of

439

Veracruz, Mexico, López (2010) suggested that the low values of IIC evince a serious threat to

440

the persistence of these species in fragmented landscapes. In our study, the IIC values were low

441

due to limited habitat availability in the basin. Thus, the increase during the first years (1975,

442

1996, 2000), indicating that connectivity improved during that time period. However, between

443

2000 and 2008 the connectivity decreased drastically. Our results indicate that if the habitat

444

patches having high importance for connectivity were to disappear, nearly 85% of the functional

445

connectivity would be lost.

446

The loss of connectivity we observed reveals a loss of habitat availability for both focal species

447

studied. In the study of functional connectivity made by Pascual Hortal and Saura (2008) of the

448

Western Capercaillie (Tetrao urogallus) in Spain, the IIC is considered as an appropriate

449

indicator of habitat availability because it involves both connectivity (in this case, as cost

450

distances) and patch area. Because the area of habitat patches was used as an input, the IIC

451

values were dependent on that attribute, and the authors suggested that higher IIC values result

452

when the landscape is mostly occupied by the habitat of focal species, with the opposite

453

occurring when habitat is scarce (Saura and Pascual Hortal, 2007).

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Regarding the importance of individual habitat patches, our results indicate that a strict relation

455

exists between the size of a patch and its importance for maintaining general connectivity of the

456

basin. The larger patches are more convenient for maintaining connectivity, assuming the size of

457

the patch offers more or less habitat suitability (Figure 7). According to Pascual Hortal and Saura

458

(2008), the percentage of loss of connectivity we calculated is showing the contribution to

459

general landscape connectivity of each habitat patch in terms of their area. Other studies such as

460

those of Ferrari et al. (2007), and of Saura and Rubio (2010), report the trend of these indexes for

461

assigning high values of connectivity to the largest habitat patches, as in this case, with different

462

dispersion distances. Nevertheless, some times the degree of connectivity is not dependent on

463

habitat patch area (García-Feced and Saura, 2011).

464

Figure 7.

465

Despite these differences, the size of habitat patches is one of the most used attributes at present

466

for prioritizing conservation areas (Pascual Hortal and Saura 2008). We also found some

467

coincidences between the distributions of the habitat patches that are important for connectivity

468

for both focal species. In the case of the Mexican bobcat, most of its habitat patches at the higher

469

parts of the basin –where the largest such patches are conserved– ranked as very high or high

470

importance for connectivity (Figure 7). If such areas were to be considered conservation

471

priorities, connectivity at the basin would be largely maintained. In this regard, we can suggest

472

that contiguous habitat patches outside the limits of the basin also be managed in conservation

473

strategies. In addition, establishment of new paths between habitat patches relevant for

474

connectivity can be more strategic than establishing connections between disturbed areas,

475

because the latter habitat patches have natural and semi natural elements that offer the species

476

better habitat quality and suitability.

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The patch dynamic through the study period presented here is mainly driven by increases in

478

emigration rates to the USA (Corona Vazquez, 1990), which was verified by Mendoza et al.

479

(2011). This change in migration pattern is related to the application of the Immigration Reform

480

and Control Act (IRCA), or Simpson-Rodino Law which, according to Cornelius (1989), seems

481

to have prompted more undocumented worker to permanently migrate to the USA by providing

482

them with the opportunity to legalize their immigration status when they were able to prove they

483

had worked for at least 90 days in farms during the twelve month period ending on the first day

484

of 1986. Another driving factor related to the LCLU patterns and processes of change is related

485

to the Mexican economic crisis of the eighties which, together with other pressures in the labor

486

market, caused a general deterioration of the standards of living for the Mexican population

487

(Mendoza et al., 2001). The noteworthy expansion of urban areas, mainly between 1986 and

488

1996 coincides with the aftermath of the earthquake of September 19th, 1985, that affected

489

Mexico City; during that time a federal policy for decentralization of several institutions was

490

promoted by the government (Mendoza et al., 2001).

491

Conclusions

492

In general, during the studied time period the connectivity of the landscape in the Lake Cuitzeo

493

basin was very low. This finding corresponds with the low availability of habitat for both studied

494

focal species, which is mostly caused by land-use change. The probability of connectivity for the

495

two focal species was also low, but the ringtail exhibited a larger area with higher dispersal

496

probability than the bobcat. This is related to the former specie' higher tolerance to human

497

presence and its greater ability to disperse through anthropogenic land-cover matrices. The

498

analysis of the relation of the cost distances with the length of potential corridors was a useful

499

indicator both for the selection of these corridors and for evaluating their efficiency. Hence, it

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represents a useful tool for proposals of biodiversity conservation efforts. Applying methods

501

derived from circuit theory in combination with potential corridor modeling –based on

502

normalized LCP over a minimum distance and cost gradient– provided a suitable option for a

503

complementary analysis of landscape connectivity. Our work provides a solid methodological

504

framework for studying landscape connectivity. The approaches of graph theory (LCP) and of

505

circuit theory (current flows) complemented by expert knowledge provide information for the

506

conservation of biodiversity.

507

The multi temporal modeling of connectivity herein presented –of which ours is a pioneer study

508

in Mexico– could be made only due to the availability of a long-term database. However, these

509

available data are mostly limited to the extension of the lake Cuitzeo basin, and the landscapes at

510

higher elevations –which in general have higher habitat suitability–are incomplete, because they

511

extend beyond the water divide of the basin.

512

Despite these limitations, our work provides not only useful information for the Ecological

513

Planning of the Lake Cuitzeo Basin (in effect since July of 2011), but also provides a replicable

514

spatial approach; its outputs can serve as key information for decision-making regarding the

515

conservation of biodiversity, especially in areas with limited ecological information that need

516

prompt actions.

517

Acknowledgements

518

The authors acknowledge DGAPA-PAPIIT for financing the project (IN1118119). The first

519

author acknowledges CONACYT for the scholarship granted to him for postgraduate studies in

520

the Master in Geography program at UNAM. Thanks are given to M.Sc. G. Castellanos, M.Sc.

521

H.Bárcenas, Dr. H.López, Dr. A.H. Huerta, Dr. O.M Vilchis, Biol. P.Carvajal, Dr. J.P Gallo

522

Reynoso and Dr. A. Velázquez –experts from several Mexican universities– for kindly providing

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their answers to the questionnaire for modeling. We thank Dr. Sergio Zárate and Dr. Brian

524

Napoletano for critical reading of an early version of the manuscript. We also thank two

525

anonymous reviewers for their constructive suggestion and comments, which helped us to

526

improve the manuscript.

527

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735

Figure 1. Study area and optimal habitat patches of focal species.

736

Figure 2. Map of probability of connectivity for the Mexican bobcat (Lynx rufus escuinapae).

737

Figure 3. Map of probability of connectivity for the ringtail (Bassariscus astutus).

738

Figure 4. Values of IIC for the two focal species.

739

Figure 5. Potential corridors as an example of conservartion scenario for the year 2008.

740

Figure 6. Potential corridors in the year 2008 for the two focal species.

741

Figure 7. Importance of individual habitat patches for the general connectivity of the landscape.

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721 722

742 743 744 745 746 747

Table 1. Multitemporal change in cost distances, least-cost paths and cwdToPathRatio index for the bobcat and

748

ringtail

Year

Links

Euclidean distance range (km)

Mean Euclidean distance (km)

Cost distance range (km)

Mean cost distance (km)

Mean longitude of least-cost paths (LCP; km)

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31

1975

10

0.2-53.4

22.9

23.4-4,891

2,308.80

27.4

1996

6

3.1-39.8

26.1

382-4,259

2,561.80

30.4

2000

11

3.1-39.9

21.3

373-4,127

2,091.50

25.5

2003

8

3.1-39.8

23.2

375-4,213

2,255.40

30.9

2008

7

2.7-44

22.4

379-4,759

2,407.60

30.2

1975

176

0.079-24.7

5.2

7.6-4,660

1996

174

0.026-20.3

4.7

12.5-3,813.6

2000

170

0.17-20.4

4.7

15.3-3,621.3

ip t

Mexican bobcat

2003

178

0.32-20.4

4.8

2008

156

0.216-25.3

Ringtail

5.9

15.3-3,021.5

625.7

6

6

33.2-3,818

829.5

8

Value (km)

Higher cwdToPathRatio linkc

Value (km)

us

cr

6

612.5

95.92

64.35

2 and 3

118.67

58.74

3 and 4

90.78

68.99

3 and 4

115.44

Mexican bobcat 81.07

4 and 5

68.51

1996

84.6

1 and 3

69.08

2000

85.19

1 and 2

2003

76.4

2 and 4

2008

86.19

1 and 3

d

M

1975

Ringtail

1 and 2

91.54

2 and 4

98.13

8 and 33

44.37

31 and 48

148.99

101.46

26 and 27

44.32

36 and 38

163.98

2000

102.91

24-25

40.57

35 and 37

162.59

2003

101.52

27 and 28

40.57

35 and 41

153.07

te

1975 1996

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6.7

619.2

an

Mean Lower cwdToPathRatioa cwdToPathRatio (km) linkb

665

749

102.96 45 and 49 44.87 47 and 51 157.67 2008 NOTES: a Ratio between the cost weighted distance and the Euclidian distance of least-cost paths (WHCWG,

750

2010); b Identification numbers of lower cwdToPathRatio links; c Identification numbers of higher cwdToPathRatio

751

links.

752 753 754 755 756 757 758 759 760 761

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32 762

Table 2. Main structural characteristics of habitat patches according to their individual importance for the general

763

landscape connectivity Ringtail

Mexican bobcat

Area (ha)

c

%

d

MPS (ha)

508

96.58

17,505.5

31.5

34.5

Low

9

1.711

12,218.5

22.0

Medium

5

0.951

9,921.3

High

3

0.57

10,184.5

Very high

1

0.19

5,729.6

NP

a

NP%

b

Área (ha) c % d MPS (ha) e

411

97.4

15,041.2 25.4

36.6

1357.6

3

0.71

8,444.6 14.2

2,814.9

17.9

1984.3

6

1.42

9,236.7 15.6

1,539.5

18.3

3394.8

1

0.24

5,708.1

9.6

5,708.1

10.3

5729.6

1

0.24

20,832.3 35.2

20,832.3

cr

Very low

e

te

d

M

an

us

NOTES: a NP = Number of habitat patches. b NP% = Porcentage of number of habitat patches in the importance category. c Area (ha) = Area of the importance category. d % = percentage of the total optimal habitat area occupied by the importance category. e MPS (ha)= Mean patch size per category of importance.

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764 765 766 767

NP%

b

ip t

Importance NP

a

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Figure 1. Study area and optimal habitat patches of focal specie

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Figure 2 Map of probability of connectivity for the Mexican bobc

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Figure 3 Map of probability of connectivity for the ringtail (Ba

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Figure 4 Values of IIC for the two focal species.

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Figure 5 Potential corridors as an example of conservartion scen

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Figure 6 Potential corridors in the year 2008 for the two focal

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Figure 7 Importance of individual habitat patches for the genera

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