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Ecological Modelling 231 (2012) 101– 112

Contents lists available at SciVerse ScienceDirect

Ecological Modelling

jo ur n al homep ag e: www.elsev ier .com/ locate /eco lmodel

ulti-taxa population connectivity in the Northern Rocky Mountains

amuel A. Cushmana,∗, Erin L. Landguthb

USDA Forest Service Rocky Mountain Research Station, 2500 S. Pine Knoll Dr, Flagstaff, AZ 86001, USAUniversity of Montana, Division of Biological Sciences, Missoula, MT 59812, USA

r t i c l e i n f o

rticle history:eceived 28 June 2011eceived in revised form 7 February 2012ccepted 14 February 2012

eywords:onnectivityorridornimal movementesistant kernelmbrella species

a b s t r a c t

Effective broad-spectrum biodiversity conservation requires that conservation strategies simultaneouslymeet the needs of multiple species. However, little is known about how maintaining habitat connectiv-ity for one species or species group may also act as an umbrella for other species. We evaluated thedegree to which predicted connected habitat for each of 144 different hypothetical organisms expressingrange of dispersal abilities and ecological responses to elevation, roads and land cover function as anindicators of connected habitat for the others in the U.S. Northern Rocky Mountains. We used resistantkernel modeling to map the extent of the study area predicted to be connected by dispersal for eachspecies. At relatively large dispersal abilities there was extensive overlap between connected habitat formost organisms and much of the study area is predicted to provide connected habitat for all hypotheticalorganisms simultaneously. In contrast, at low to medium dispersal abilities there was much less intersec-tion of habitat connected by dispersal. We found that habitat specialists with limited dispersal ability are

weak indicators of others, and likewise are weakly indicated by others. We evaluated the effectiveness ofthree carnivores as connectivity umbrellas for many species. All three carnivore species performed sig-nificantly worse as connectivity umbrellas than the average across the simulated species. These speciesare associated with high elevation forested habitats. It is the low elevation and non-forest habitats thatare most at risk of habitat loss and fragmentation in the study area, suggesting that a carnivore umbrellamay miss many species most at risk.

. Introduction

Evaluating population connectivity and mapping linkage zonesor protection have emerged as among the highest priorityonservation actions in the face of increasing habitat loss and frag-entation and the threat of climate change (Noss, 1983; Beier and

oe, 1992; Beier and Noss, 1998; Beier and Brost, 2010; Wassermant al., 2012). Biodiversity conservation is more effective when con-ervation strategies simultaneously meet the needs of multiplepecies (Lambeck, 1997; Roberge and Angelstam, 2004). If one canelect effective “umbrella species” (Noss, 1991; Fleishman et al.,000; Roberge and Angelstam, 2004) whose protection also confersrotection to a large number of other species, then the challenge ofddressing each species individually could be significantly reduced.he umbrella species concept originated with proposition that pro-ecting a species with large area requirements may effectively con-erve many other species inhabiting the same landscape. Roberge

nd Angelstam (2004) refined the concept into what they termedhe “extended umbrella,” which explicitly considers connectivity,nd proposes that landscapes sufficiently connected for umbrella

∗ Corresponding author. Tel.: +1 406 546 6379.E-mail address: scushman@fs.fed.us (S.A. Cushman).

304-3800/$ – see front matter. Published by Elsevier B.V.oi:10.1016/j.ecolmodel.2012.02.011

Published by Elsevier B.V.

species should also provide extensive habitat connectivity for manyother species as well. Little is known, however, about how main-taining connectivity and linkage zones for one species or speciesgroup may also act as an umbrella for other species (Rosenberget al., 1997; Beier and Noss, 1998; Roberge and Angelstam, 2004).Most studies of population connectivity have focused on singlespecies, or on groups of closely related taxa (e.g. Rosenberg et al.,1997; Beier and Noss, 1998; Hess and Fischer, 2001).

A number of researchers have proposed carnivores as focalspecies for habitat and connectivity protection, given their typi-cally large area requirements and high mobility (Eisenberg, 1988;East, 1981; Soule, 1987; Peterson, 1988; Shafer, 1990; Noss 1993).However, the effectiveness of carnivores as connectivity umbrellashas rarely been formally evaluated. Beier et al. (2009a,b) evalu-ated how well connectivity for eight focal species was indicatedby connectivity for three carnivore species (puma, badger and SanJoaquin kit fox). They found that corridors for each of the carni-vore species performend weakly as corridors for other focal species.These species are generally highly-mobile habitat generalists andthus likely will often be inadequate umbrellas for other species

(Beier et al., 2009a,b; Minor and Lookingbill, 2010).

The few multi-species evaluations of habitat connectivityfocused on the effectiveness of narrow, linear habitat corridors(Haddad et al., 2003; Beier et al., 2009a,b). While evaluating the

1 logical Modelling 231 (2012) 101– 112

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Table 1Description of factors and levels combined to create 36 landscape resistancehypotheses.

Factor Level Code Description

LandcoverHigh Selectivity FH Low resistance forest; high

resistance non-forest*Low Selectivity FL Low resistance forest;

moderate resistancenon-forest

Null FN No relationship withlandcover classes

RoadsHigh Resistance RH High resistance due to

roadsLow Resistance RL Low resistance due to roadsNull RN No relationship with roads

ElevationHigh Elevation EH Minimum resistance at

high elevationMiddle Elevation EM Minimum resistance at

middle elevationLow Elevation EL Minimum resistance at low

elevation

02 S.A. Cushman, E.L. Landguth / Eco

fficacy of particular conservation corridors is important, a generalvaluation of connectivity umbrella species should not be restrictedo narrow, linear corridors. It is also important to evaluate the syn-ptic population connectivity of multiple species and determineow well the pattern of connected habitat of one species indicatesonnected habitat for others, and if we can identify types of specieshat are likely to function as effective connectivity umbrellas.

Quantitative analysis of population connectivity is often basedn analysis of landscape resistance maps, which represent the resis-ance to organism movement as functions of multiple variablesrom a variety of spatial scales (Spear et al., 2005; Vignieri, 2005;ushman et al., 2006; Fig. 1). These resistance models are essentialoundations for applied analyses of population connectivity, suchs the identification of corridors and barriers. However, resistanceaps are not in themselves sufficient to answer many questions of

reatest concern. For example, pixel level resistance to movementoes not provide sufficient information to evaluate the existence,trength and location of barriers and corridors (Cushman et al.,008). The resistance model is the foundation for these analyses,ut it is explicit consideration of connectivity across the resistanceurface that provides the key information for conservation andanagement.Least cost path analysis is a commonly used approach which

rovides specific predictions of the location of optimal move-ent routes between pairs of locations (e.g. Cushman et al., 2008).owever, there are a number of limitations in using least costath analysis in efforts to predict synoptic landscape connectiv-

ty. Most notably, it is unclear how effective protecting a singleeast-cost path between a pair of locations would be in provid-ng functional connectivity across the full landscape (Fig. 2a). Anlternative to single least cost path analysis is the least cost cor-idor (Adriaensen et al., 2003) which calculates a low cost routencluding all cells in the landscape for which routes connectinghe source and destination are less costly than particular thresh-ld cost (Fig. 2c). This reduces the limitation of restriction to theingle least cost path, but is still limited by its dependence on thepecification of single source and destination locations. If one’s goals to describe the synoptic pattern of connectivity across a popula-ion then it is usually necessary to evaluate connectivity for eachocation relative to the full extent of the population. The limita-ion to single sources and destinations has been lessened by thedoption of factorial approaches to calculate least cost paths amongany pairs of source and destination locations (e.g. Cushman et al.,

008; Schwartz et al., 2009; Cushman et al., 2010a,b; Landgutht al., 2011; Fig. 2b). This greatly improves depiction of synopticandscape connectivity across the full population network. How-ver, even factorial least cost path analysis includes the unrealisticssumption that animals know and utilize the single least cost path.

more general approach to connectivity modeling would con-ider all possible routes among all occupied habitat cells weightedy their cost and incorporating species-specific dispersal abilities.he resistant kernel approach to modeling landscape connectiv-ty is designed specifically to accomplish this (Compton et al.,007).

Resistant kernel connectivity modeling has a number of advan-ages as a robust approach to assessing population connectivity for

ultiple wildlife species. First, unlike corridor prediction efforts,t is spatially synoptic and provides prediction and mapping ofxpected migration rates for every pixel in the study area extent,ather than only for a few selected “linkage zones” or between

limited set of sources and destinations (Compton et al., 2007;ig. 2d). Second, scale dependency of dispersal ability can be

irectly included to assess how species of different vagilitiesill be affected by landscape change and fragmentation under

range of scenarios (e.g. Cushman et al., 2010a,b). Third, it isomputationally efficient, enabling simulation and mapping across

Null EN No relationship withelevation

the entire vast geographical extents for a large combination ofspecies (e.g. Cushman et al., 2010a,b, 2011).

In this paper we illustrate the use of resistant kernel connectivitymodeling evaluate multi-taxa connectivity and the effectiveness ofconnectivity umbrella species in the United States northern RockyMountains. We address four specific objectives. First, we modelpopulation connectivity for 144 different hypothetical organisms,to identify species likely to be most at risk due to limited popula-tion connectivity. Second, we quantify the cumulative intersectionof predicted dispersal habitat across species at four levels of disper-sal ability to evaluate the degree to which connectivity for multiplespecies can be simultaneously protected at particular locationsin the study area. Third, we quantify pair-wise intersections ofconnectivity predictions across all pairs of modeled species to eval-uate how well habitat connectivity for each hypothetical organismindicates connectivity for others. Fourth, we evaluate the degreeto three carnivore species, including American black bear (Ursusamericanus), wolverine (Gulo gulo), and American marten (Martesamericana), function as a connectivity umbrella for species withmore limited dispersal and different ecological characteristics.

2. Methods

2.1. Study area

The study area includes Montana and northern Idaho in theUnited States Rocky Mountains (Fig. 1). The study area containslarge areas of federally owned land, including U.S. Forest Service,National Park Service, U.S. Fish and Wildlife Service and Bureau ofLand Management. The study area also includes extensive privateland, mainly in the large valleys which lie between major mountainranges. The human population in the study area is growing morerapidly than most areas of the United States (U.S. Census Data), andis concentrated in these valley locations. In addition, an extensivenetwork of highways bisects the study area, potentially impedingmovement of native species.

2.2. Resistance surfaces

We selected 36 resistance surfaces for analysis from the poolof 108 models evaluated in Cushman et al. (2006). The selected

S.A. Cushman, E.L. Landguth / Ecological Modelling 231 (2012) 101– 112 103

Fig. 1. Study area extent of analysis and an example resistance model. The gray scale colormap indicates the local resistance in model FHEMRH, corresponding to the mostsupported black bear resistance model from Cushman et al. (2006). Dark areas are low resistance, light areas are high resistance. Red lines indicate major highways. Thee yscalet

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xtent of the study area in this analysis corresponds to the area covered by the grahe reader is referred to the web version of the article.)

andscape-resistance models represented combinations of theffects of three landscape features of resistance to movement: ele-ation, roads, and land cover (Cushman et al., 2006). Resistancef these features was modeled across four levels for elevation andhree levels for roads and land cover (Table 1). The four levels forhe feature elevation (E), consisted of a null model (EN), in whichhere was no resistance penalty for elevation in the resistance sur-ace, and three inverse-Gaussian resistance models, with minimumesistance of 1 at 500 (EL), 1000 (EM), and 1500 m (EH) elevationbove sea level respectively, 500 m standard deviation, and maxi-um resistance of 10 (Cushman et al., 2006). Similarly, three levels

and cover were modeled. The first level was the null model (FN)n which forest cover had no effect in the resistance surface. Theemaining two levels were models in which we posited that land-cape resistance is minimum in closed canopy forest and increasesn non-forest cover types. In the forest high (FH) level we stipulatedigh relative resistance for crossing non-forest cover types, repre-enting a condition where an individual organism strongly favorsovement through forest, while in the forest low (FL) level non for-

st classes have lower landscape resistance (Appendix B). Finally,hree levels for the roads (R) were used, consisting of a null modelRN) where there was no effect for resistance of roads, a model withelatively strong effect of roads on resistance (RH), and a model withelatively lower effect of roads on resistance (RL; Table 1). Isolation

y Euclidean distance was included as a 36th model. The landscapeesistance models corresponding to each feature and level wereombined into the 36 landscape-resistance models by addition asn Cushman et al. (2006). To improve computational efficiency the

resistance map. (For interpretation of the color information in this figure legend,

36 resistance models were resampled to 270 m pixel size by bilinearinterpolation. Cushman and Landguth (2010) showed that coarsen-ing the pixels size in floating point resistance grids has little effecton the strength of landscape genetic relationships, suggesting thatconnectivity models are quite robust to coarsening of pixel grain.

2.3. Focal umbrella species

Cushman et al. (2006) mapped resistance to black bear geneflow in a sub-region of the study area using a multi-model leastcost-path analysis based on molecular genetics. They correlatedpatterns of genetic similarity with landscape attributes (slope, ele-vation, land cover, and roads) using a factorial design. Their analysisidentified land cover and elevation as major factors affecting geneflow. Subsequently, Short Bull et al. (2011) repeated the Cushmanet al. (2006) study in 11 different landscapes in the study area to testthe generality of this model. They found that landscape resistancefor black bear across the study area is consistently related to ele-vation, human development, land cover and roads, consistent withCushman et al. (2006). We utilize the Cushman et al. (2006) resis-tance map for black bear (model FHEMRH) to predict connectivityfor this species and evaluate its efficacy as a connectivity umbrella.Cushman et al. (2006) found that the range of significant geneticautocorrelation for black bear in their study was approximately

40,000 cost units. Therefore, we employ 40,000 m as a dispersalthreshold for the umbrella connectivity analysis of black bear.

Schwartz et al. (2009) mapped landscape resistance for wolver-ine across the full extent of the study area using least cost path

104 S.A. Cushman, E.L. Landguth / Ecological Modelling 231 (2012) 101– 112

Fig. 2. Comparison of four methods of connectivity modeling. The colormap in figure a represents the resistance of the landscape to movement for one of the 36 resistancemodels (FHEHRH), in a gradient from low resistance (blue) to high resistance (red). (a–d) show connectivity predictions from each of four methods, with the degree ofconnectivity predicted proportional to the height of the surface. (a) A single least cost path connecting the locations indicated by the two white dots, (b) a factorial leastc stancew ally acc ately

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ost path analysis of all least cost paths among 453 locations distributed in low resihite dots, (d) cumulative resistance kernel surface showing connectivity synoptic

ost distance dispersal threshold. Note–the extent in this illustration is an approxim

nalysis and molecular genetics. Their analysis confirmed that areasith persistent spring snow provide low resistance to wolverineispersal, and areas without spring snow provide high resis-ance. We utilize an elevation-based surrogate for the Schwartzt al. (2009) resistance model (EH), with 40,000 m dispersalhreshold.

Wasserman et al. (2010) used multi-model landscape geneticnference to identify the factors that influence gene flow in Amer-can marten in a sub-section of the study area. They found thatenetic differentiation of American marten is driven by resistanceradients of elevation. Their empirically identified resistance models identical to our EH resistance model. We use the EH resistance

odel with a 20,000 m dispersal threshold to evaluate the efficacyf American marten as an umbrella species.

.4. Resistant kernel modeling

We utilize a resistant kernel approach (Compton et al., 2007) forredicting habitat connectivity for each of our 36 resistance modelsi.e., hypothetical species groups). The resistant kernel approacho connectivity modeling is based on least-cost dispersal from aefined set of source locations cumulatively across a landscape. Theources in our case are all pixels in the study area with resistance of

(lowest resistance and highest quality dispersal habitat) for eachf the 36 resistance models. This assumes that dispersing organismsriginate from all cells of optimal dispersal habitat, and that noispersers originate from sub-optimal dispersal habitat.

The resistance surface values are used as weights in the dispersalunction, such that the expected density of dispersing organisms in

pixel is down-weighted by the cumulative cost from the source,

ollowing the least-cost route (Compton et al., 2007; Cushmant al., 2010a,b). We wrote an ESRI ArcGrid script to calculate theesistant kernel (Rk) density. The analysis begins with the spec-fication of a resistance model describing the cost of movement

(resistance = 1) cells, (c) least cost corridor between the locations indicated by theross the study area from all cells with low resistance (resistance = 1) at a 20,000 m

4500 km2 area in northern Idaho near Bonners Ferry.

across each location in the study area (Fig. 3a). The model thenselects a single source cell and uses the ArcGrid COSTDISTANCEfunction to produce a map of the movement cost from that sourceup to a specified dispersal threshold on the specified resistancemap. The cost distance from the source is converted to an esti-mate of relative density by applying the dispersal function. Thedispersal function utilized in our analyses predicts that the rel-ative density of dispersing individuals decreases linearly withcumulative movement cost away from the source, up to the max-imum dispersal ability of the species. Expected relative densityof dispersing individuals is calculated by inverting the cumula-tive movement cost grid and scaling such that the maximumvalue for each individual kernel is one. Thus, a relative densityof 1 is given to the source location itself, and decreases to zeroat the maximum dispersal cost threshold (Fig. 3b). The modeliteratively calculates expected relative density of dispersers fromall source cells. The kernels surrounding all sources are summedto give the total expected relative density at each pixel acrossthe full landscape (Fig. 3c). The results of the model are sur-faces of expected density (relative to that of an isolated sourcecell) of dispersing organisms at any location in the landscape.We reclassified the cumulative kernel maps to a value of 1for allcells with non-zero cumulative kernel value, and defined all areaswith value 1 in these reclassified maps as connected habitat (CH;Fig. 3d).

We wished to bracket the range of dispersal abilities of mostanimal species in the study area. Accordingly, we ran the modelsfor each of the 36 resistance models across four levels of disper-sal ability (D), corresponding to maxima of the COSTDISTANCEfunction of 5000, 10,000, 20,000 and 40,000 cost units (Fig. 4).

These reflect dispersal abilities in optimal habitat that range from5 to 40 km. In heterogeneous habitat the dispersal kernel will bereduced in extent as a function of cumulative cost away from thesource cell in all directions (as seen in Fig. 2b). The combination of

S.A. Cushman, E.L. Landguth / Ecological Modelling 231 (2012) 101– 112 105

Table 2Description of landscape resistance models used in the analysis and extent of connected habitat under each model at four dispersal thresholds. Model Description pro-vides information on what landscape variables are included in each model. Model Number corresponds to the model numbers in Figs. 2 and 3, Figs. S1–S3. Proportion5000–proportion of the study area occupied by connected dispersal habitat for each resistance model given a 5000 m dispersal threshold; Proportion 10,000–proportion ofthe study area occupied by connected dispersal habitat for each resistance model given a 10,000 m dispersal threshold; Proportion 20,000–proportion of the study area occu-pied by connected dispersal habitat for each resistance model given a 20,000 m dispersal threshold; Proportion 40,000–proportion of the study area occupied by connecteddispersal habitat for each resistance model given a 40,000 m dispersal threshold.

Modelnumber

Modelacronym

Model description Proportion5000

Proportion10,000

Proportion20,000

Proportion40,000

1 eh Minimum resistance at high elevations (1500 m) 0.360 0.438 0.526 0.6702 ehfh Minimum resistance in forest (strong) at high

elevations0.298 0.382 0.465 0.547

3 ehfl Minimum resistance in forest (weak) at highelevations

0.307 0.393 0.477 0.565

4 ehrh Minimum resistance at high elevations with highresistance of roads

0.353 0.431 0.520 0.615

5 ehrl Minimum resistance at high elevations withweak resistance of roads

0.354 0.432 0.521 0.616

6 el Minimum resistance at low elevations (500 m) 0.760 0.836 0.910 0.9677 elfh Minimum resistance at in forest (strong) at low

elevations0.274 0.405 0.568 0.745

8 elfl Minimum resistance in forest (weak) at lowelevations

0.299 0.453 0.640 0.819

9 elrh Minimum resistance at low elevations with highresistance of roads

0.750 0.830 0.907 0.966

10 elrl Minimum resistance at low elevations with weakresistance of roads

0.751 0.831 0.907 0.966

11 em Minimum resistance at middle elevations(1000 m)

0.798 0.857 0.901 0.940

12 emfh Minimum resistance in forest (strong) at middleelevations

0.449 0.535 0.617 0.704

13 emfl Minimum resistance in forest (weak) at middleelevations

0.470 0.566 0.656 0.749

14 emrh Minimum resistance at middle elevations withhigh resistance of roads

0.789 0.853 0.900 0.939

15 emrl Minimum resistance at middle elevations withweak resistance of roads

0.791 0.854 0.900 0.940

16 fh Minimum resistance in forest (strong) 0.541 0.624 0.726 0.84017 fhehrh Minimum resistance in forest (strong) at high

elevations with high resistance of roads0.290 0.376 0.458 0.542

18 fhehrl Minimum resistance in forest (strong) at highelevations with weak resistance of roads

0.291 0.377 0.460 0.544

19 fhelrh Minimum resistance in forest (strong) at lowelevations with high resistance of roads

0.256 0.390 0.554 0.735

20 fhelrl Minimum resistance in forest (strong) at lowelevations with weak resistance of roads

0.259 0.393 0.556 0.737

21 fhemrh Minimum resistance in forest (strong) at middleelevations with high resistance of roads

0.437 0.529 0.611 0.700

22 fhemrl Minimum resistance in forest (strong) at middleelevations with weak resistance of roads

0.439 0.530 0.612 0.700

23 fhrh Minimum resistance in forest (strong) with highresistance of roads

0.531 0.615 0.718 0.834

24 fhrl Minimum resistance in forest (strong) with lowresistance of roads

0.533 0.617 0.719 0.835

25 fl Minimum resistance in forest (weak) 0.877 0.955 0.989 0.99926 flehrh Minimum resistance in forest (weak) at high

elevations with high resistance of roads0.299 0.386 0.471 0.560

27 flehrl Minimum resistance in forest (weak) at highelevations with weak resistance of roads

0.300 0.387 0.472 0.561

28 flelrh Minimum resistance in forest (weak) at lowelevations with high resistance of roads

0.280 0.436 0.626 0.812

29 flelrl Minimum resistance in forest (weak) at lowelevations with weak resistance of roads

0.283 0.438 0.628 0.813

30 flemrh Minimum resistance in forest (weak) at middleelevations with high resistance of roads

0.458 0.559 0.650 0.745

31 flemrl Minimum resistance in forest (weak) at middleelevations with weak resistance of roads

0.460 0.560 0.651 0.745

32 flrh Minimum resistance in forest (weak) with highresistance of roads

0.861 0.950 0.987 0.999

33 flrl Minimum resistance in forest (weak) with lowresistance of roads

0.864 0.951 0.987 0.999

34 rh High resistance of roads 0.997 1 1 135 rl Weak resistance of roads 0.998 1 1 136 Null Uniform resistance across the landscape 0.999 1 1 1

106 S.A. Cushman, E.L. Landguth / Ecological Modelling 231 (2012) 101– 112

Fig. 3. Steps in the resistant kernel analysis. (a) a resistance map (FHEMRH) that indicates unit cost for every location in the landscape, (b) a single resistant kernel with2 e creatl ray, an4

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0,000 m cost distance dispersal threshold, (c) the cumulative resistant kernel surfacess than 3), (d) reclassified kernel map showing the extent of connected habitat in g500 km2 area in northern Idaho near Bonners Ferry.

6 resistance models and 4 dispersal abilities (5000, 10,000, 20,000,0,000) define the full set of 144 hypothetical species (Table 2).

.5. Intersection analysis

We computed the cumulative spatial intersection of predictedispersal habitat across species at each level of dispersal ability.his was done by summing the predicted connectivity rasters forach of the 36 models at each of the four dispersal abilities. Thealue of the summed grids indicate the number of simulated speciesredicted to have connected habitat at each location under thatispersal ability.

Next we computed the full factorial of pair-wise intersectionsf predicted connected habitat between all pairs of hypotheticalpecies within each level of dispersal ability. We computed thextent of intersection as the proportion of the extent of predicted

onnected habitat for each member of the intersected pair. Wesed this factorial intersection to compute the indicator ability andmbrella efficacy of each species. A species’ performance as an indi-ator of another species’ connectivity habitat can be measured by

ig. 4. Example of the effect changing the dispersal threshold has on the kernel connectivihreshold, (b) cumulative kernel surface for the same extent and same resistance model

anges from high expected density of dispersing organisms to low. Areas of no color showinaking them barriers. The resistance map used is FHEMRH and the extent is an approxi

he color information in this figure legend, the reader is referred to the web version of th

ed by summing all the individual kernels originating at all low resistance (resistanced breaks in connectivity in black. The extent in this illustration is an approximately

the extent of the connected habitat for the indicated species thatintersects the connected habitat of the indicator. We defined threelevels of indicator performance. A species was defined as a weakindicator of another if it intersected less than 50% of the connectedhabitat of the indicated species. A species was defined as a moder-ate indicator if it intersected between 50% and 75% of the connectedhabitat of the indicated species. We defined a strong indicator asone that intersected more than 75% of the indicated species’ con-nected habitat.

We evaluated the efficacy of each species as an umbrella forother species with the same dispersal ability by computing theaverage indicator ability (% of intersection with connected habi-tat of the indicated species) across all other of the 35 resistancemodels. We defined efficacy of umbrella species across the samelevels as indicator classes, with a weak umbrella species havingless than 50% average intersection across the 35 other resistance

models at that dispersal ability. A moderate umbrella species hadbetween 50% and 75% intersection, and a strong umbrella specieswould have greater than 75% average intersection with the other35 resistance models at that dispersal ability.

ty predictions. (a) Cumulative kernel surface at the 10,000 m cost distance dispersalat the 40,000 m cost distance dispersal threshold. The color ramp from red to blueg gray scale background are predicted to have zero density of dispersing organisms,

mately 4500 km2 area in northern Idaho near Bonners Ferry. (For interpretation ofe article.)

logical Modelling 231 (2012) 101– 112 107

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Table 3Proportion of pair-wise combinations of species that were weak, moderate or strongindicators across the four dispersal abilities.

5000 10,000 20,000 40,000

significantly worse than average umbrella species (p = 2.2 × 10−10).Finally, black bear (FHEMRH, 40,000) was ranked 25th of 36 speciesand was also significantly worse than average as a connectivityumbrella species (p = 0.041).

Table 4Proportion of the 35 hypothetical species that were weak, moderate or strong con-nectivity umbrellas across the four dispersal ablities.

S.A. Cushman, E.L. Landguth / Eco

Finally, we evaluated the efficacy of the three carnivore focalpecies by computing the significance of difference between thembrella ability (as defined above) of that species in comparison tohe umbrella ability of the other 35 hypothetical species with theame modeled dispersal ability. We did this by computing a z-testf the significance of difference between the umbrella score of theocal species compared to the distribution of umbrella scores of thether 35 species at that dispersal ability.

. Results

.1. Predicted connected habitat across taxa

At 5000 m dispersal ability, on average 52.9% of the study areaas occupied by connected habitat across the 36 resistance models

Table 2). The resistance model in which resistance was lowest atow elevation forest, with highly resistant roads (FHELRH) had theowest predicted extent of connected habitat (25.6%), while in the

odels in which resistance was only a function of roads and the nullodel connected habitat nearly completely covered the study area.t the 10,000 m dispersal threshold the average extent of connectedabitat across the 36 models increased to 61.6% of the study area,ith model FHEHRH having the lowest extensiveness, at about

7.6% of the study area. The average extent of habitat connectedy dispersal increased to 70% at 20,000 m dispersal threshold, withhe resistance model FHEHRH having the least extent (45.8% of thetudy area). At the largest dispersal threshold (40,0000 m) the aver-ge extensiveness of connected habitat across models was 78.9% ofhe study area, and FHEHRH remained the lowest extent at 54.2%.

.2. Cumulative intersection of connected habitat across taxa

At the 5000 m dispersal threshold, 23% of the study area extentad 10 or less taxa with overlapping predicted connected habi-at (of the 36 models) and only 7% of the study area was coveredy 31 or more overlapping models (Fig. 5). The proportional over-

ap increased dramatically with dispersal ability. For example, theroportion of the study area with 10 or less taxa with overlap-ing habitat decreased to 13% at the 10,000 m threshold, 8% athe 20,000 m threshold and only 3% at the 40,000 m thresholdFig. 5). Likewise, the proportion of the study area with more than0 taxa with overlapping connected habitat increased to 20%, 35%nd 77% as dispersal threshold increased to 10,000 m, 20,000 m,nd 40,000 m, respectively. At low dispersal abilities (5000 m and0,000 m), areas with a high number of intersections (red in Fig. 6)re restricted to middle elevation forested areas. As the dispersalhreshold increases much more extensive areas of the landscapeecome occupied by very high intersection of predicted connectedabitat, with gaps only remaining in low elevation non-forestedreas and a few high elevation non-forested areas (Fig. 6).

.3. Connectivity indicator species

The ability of each simulated species to serve as an indicator ofonnected habitat of each of the other species with the same dis-ersal ability is visually depicted in Fig. 7 and Figs. S1–S3. Red cells

ndicate pair-wise combinations in which the indicator stronglyredicts the location of connected habitat for the indicated species.lue cells indicate pair-wise combinations in which the indicatoreakly predicts the connected habitat of the indicated species.

Connectivity of some hypothetical species were consistentlytrong indicators of connected habitat for others across all four

ispersal abilities. For example, high-elevation associated speciesere consistently strong indicators of other high elevation species,

egardless of association with forest or sensitivity to roads. Sim-larly, low elevation species were generally strong indicators of

Strong 0.52 0.53 0.65 0.77Moderate 0.18 0.28 0.30 0.23Weak 0.30 0.19 0.05 0

habitat connectivity for other low elevation associated species.Interestingly, middle elevation species were often strong indicatorsof high elevation species connectivity, but less often for connectiv-ity of low elevation associated species.

Conversely, there were a number of pair-wise combinations thatwere consistently the weakest indicators. The weakest indicatorrelationships were high elevation species as indicators of low andmiddle elevation species and low elevation species as indicators ofhigh elevation species. In addition, species with more specializeddispersal requirements were generally weak indicators of gener-alists. For example, a species that is sensitive to roads, dependenton forest cover and prefers low elevation would likely be a rela-tively weak indicator of generalist species also associated with lowelevation.

The proportions of pair-wise combinations of simulated speciesthat were weak, moderate or strong indicators changed dramati-cally across the four dispersal abilities (Table 3). Specifically, theproportion of strong indicator relationships increased from 52% to77% and weak indicator relationships drecreased from 30% to 0% asdispersal ability increased from 5000 to 40,000 m.

3.4. Connectivity umbrella species

The number of species scoring as “strong” connectivity umbrellaspecies increased from 36% to 67% percent and the number of“weak” umbrella species decreased from 25% to 0% as dispersalability increased from 5000 to 40,000 m (Table 4). The speciesreceiving the lowest connectivity umbrella score changed betweenthe 5000 m and higher dispersal abilities. At the 5000 m dispersalability species strongly associated with forest cover and low eleva-tions had the lowest umbrella scores (Table 5). At higher dispersalabilities (10,000 m to 40,000 m) there was consistent ranking of thelowest performing umbrella species, with species associated withhigh elevations, forest cover and sensitive to roads consistently theworst umbrella species.

3.5. Carnivores as connectivity umbrella species

None of the three focal carnivore species were in the top two-thirds of species in their respective dispersal ability groups interms of their connectivity umbrella score. American marten (EH,20,000 m) was ranked 26th out of the 36 species at the 20,000 m dis-persal ability, and was a significantly worse umbrella species thanthe average species at that dispersal ability (p = 2.5 × 10−−8). Thewolverine (EH, 40,000 m) was ranked 28th out of 36, and was also a

5000 10,000 20,000 40,000

Strong 0.36 0.58 0.65 0.67Moderate 0.39 0.42 0.30 0.33Weak 0.25 0.0 0.05 0

108 S.A. Cushman, E.L. Landguth / Ecological Modelling 231 (2012) 101– 112

Fig. 5. Pie charts of the proportion of the study area covered by connected habitat across models for four dispersal thresholds: (a) 5000 m, (b) 10,000 m, (c) 20,000 m, (d),40,000 m. The numbers in the charts indicate the number of overlapping connected habitats across models and the percentage indicates the percentage of the study areawith that number of overlapping models. For example in (a) the green wedge 6–10 and 23% indicates that at a 5000 m dispersal threshold 19% of the study area is coveredb interpw

4

4

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TMa

y connected habitat for between 6 and 10 of the 36 hypothetical organisms. (For

eb version of the article.)

. Discussion

.1. Beyond corridors

Many past connectivity assessments have focused on estab-ishing or protecting narrow linear corridors of habitat betweenore populations (Harris and Gallagher, 1984; Beier and Loe, 1992;arrison and Bruna, 1999). There has been much debate regarding

he utility of these small, narrow, linear features as habitat corridorsnd their ability to provide population subsidization and recol-

nization among patches (Hobbs, 1992; Simberloff et al., 1992;osenberg et al., 1997). Taylor et al. (1993) define connectivity ashe ability of organisms to move among habitat patches. However,

able 5odels receiving the five lowest umbrella species scores at each dispersal ability. Name re

bility for that species (row sum of Figs. 6, Figs. S1–S3).

5000 m 10,000 m

Name Score Name Score

FHELRH 0.414 FHEHRH 0.566

FHELRL 0.418 FHEHRL 0.569

ELFH 0.437 EHFH 0.584

FHEHRH 0.438 FLEHRH 0.594

FHEHRL 0.441 EHRH 0.599

retation of the color information in this figure legend, the reader is referred to the

instead of experiencing landscapes as categorical mosaics, it ismore likely that organisms experience their surroundings as gradi-ents of differential quality in relation to ecological and life-historycharacteristics (McGarigal and Cushman, 2005; Cushman, 2006;Cushman et al., 2010b). When connectivity is considered fromthis perspective, the narrow focus on movement among discretehabitat patches via narrow linear corridors is subsumed as a specialminor case of the general process of organisms to traversing resis-tant landscapes (Cushman et al., 2006). Focus on movement acrosscontinuously resistant landscapes also allows a shift in the scale

of focus from linear corridors between patches, which are usuallysmall relative to the vagility of the organism and the distributionof its population, to a broader scale analysis of how landscape

fers to the model names listed in Table 3 and Score is the average indicator species

20,000 m 40,000

Name Score Name Score

FHEHRH 0.647 FHEHRH 0.693FHEHRL 0.649 FHEHRL 0.695EHFH 0.655 EHFH 0.699FLEHRH 0.663 FLEHRH 0.714FLEHRL 0.671 FLEHRL 0.715

S.A. Cushman, E.L. Landguth / Ecological Modelling 231 (2012) 101– 112 109

F ates thf

pat

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ig. 6. Spatial depiction of the intersections quantified in Fig. 3. The color map indicor (a) 5000 m, (b) 10,000 m, (c) 20,000 m and (d) 40,000 m dispersal thresholds.

atterns of differential resistance to movement affect connectivityt scales relevant to the population-level processes that are centralo effective conservation (Berger et al., 2006; Cushman, 2006).

Our approach is explicitly based on this synoptic, gradient per-pective, and enables us to assess the total pattern of populationonnectivity, including the extent and pattern of core areas, theocation of fracture zones, barriers and corridors between core areasequires. Importantly, our approach allows us to explicitly integratehe effects of differential dispersal ability on synoptic populationonnectivity. There have been very few published studies that havetilized spatially synoptic connectivity modeling that incorporatedifferential dispersal ability. Compton et al. (2007) used the resis-ant kernel approach to rank vernal pools in Massachusetts by local,eighborhood, and regional connectivity and identify the most

unctionally connected pool complexes. Cushman et al. (2010a)valuated effects of changing population size, dispersal ability andandscape resistance on population connectivity of pond breed-ng amphibians in Massachussetts. They found that populationonnectivity was a complex interaction between dispersal ability,opulation size and the landscape factors influencing resistanceo movement. Cushman et al. (2011) evaluated the sensitivity ofabitat connectivity for three focal species and two habitat-groupscross a vast region of the United States Great Plains to dispersalbility, and found that dispersal ability had a much larger effectn connectivity for each species and species group than variationn landscape resistance. This highlighted the critical importance oforrectly specifying species dispersal ability and explicitly incor-orating it into connectivity analyses.

Our results provide synoptic mapping of population connectiv-ty for a broad range of hypothetical species, expressing a range ofispersal abilities and sensitivities to different habitat features. Thisnables us to produce maps of predicted habitat core areas, iden-ify fracture zones and barriers, and locate keystone linkages that

rovide connectivity among core areas. These connectivity mapslso provided us the means to evaluate which species were mostimited by the extent of connected habitat, and how well the dif-erent species preformed as connectivity indicators and umbrellas.

e number of overlapping models (between 0 and 36) for each cell on the landscape

Similar to Compton et al. (2007) and Cushman et al. (2011), ouranalysis provides spatially explicit predictions of connectivity thatcan be used to prioritize areas for conservation that maximallyprotect the total connectivity of the population. Our results alsosupport the findings of Cushman et al. (2010a) and Cushman et al.(2011) that dispersal ability plays an often dominant role in affect-ing the degree of connectivity across resistant landscapes. It isinteresting to note that most efforts to delineate corridors or link-age zones have not explicitly addressed the issue of dispersalability.

4.2. Multiple-species connectivity: quantifying strength of linkage

The term linkage refers to portions of a landscape intended tosupport the connectivity of multiple focal species and ecosystemprocesses (Beier and Brost, 2010). To design linkages, conserva-tion planners have often attempted to select a suite of focal speciesintended to serve as collective umbrella for the entire biota. Therehave been few evaluations of the efficacy of proposed corridors orlinkage zones for multiple species. Haddad et al. (2003) found thatnarrow, experimentally created corridors influenced the move-ment rates of ten focal species. However, it is unclear the extentto which observation of movement over short periods of time insmall, linear habitat corridors influences population processes ofimportance to conservation, such as gene flow and demographicexchange. Beier et al. (2006) Beier et al. (2007) designed larger-scalelinkage plans in California and Arizona to simultaneously meet theneeds of 10–30 focal mammals, reptiles, fishes, amphibians, plants,and invertebrates. However, these efforts did not explicitly modelhow effective the linkages would be across the range of disper-sal abilities of the focal species. Carroll et al. (2010) evaluated thehabitat umbrella performance of Spotted owls (Strix occidentalis)in providing habitat protection for 130 other native species. That

study predicted the intersection of suitable habitat for multiple taxa(similar to Grand et al., 2004) and evaluated alternative reservenetworks that provide effective protection of multiple taxa andpotential resilience to climate change. That study, however, did not

110 S.A. Cushman, E.L. Landguth / Ecological Modelling 231 (2012) 101– 112

Fig. 7. Spatial intersections among predicted connected habitat for all 36 resistance models at the 20,000 dispersal threshold, as percentage of the extent of each model’spredicted connected habitat. The diagonal (from lower left to upper right) is 1, as each model has 100% intersection with itself as a proportion of its own area. The off diagonalcells represent intersections between predicted connected habitat for each pair of resistance maps at dispersal ability 20,000, as proportions of the model shown on the Xaxis. For example cell R10, C4 represents the intersection between model 27 (FLEHRH, Table 3) and model 4 (EHRH, Table 3) as a proportion of the area of connected habitatin model 4. This intersection covers 83.5% of the area of model 4 landscape, corresponding to deep orange on this color scale. Equivalent figures for dispersal threshold 5000,10,000, and 40,000 are given in Figs. S1, S2 and S3 respectively. (For interpretation of the color information in this figure legend, the reader is referred to the web version oft

er

ooac(ta2aetenrtpc

he article.)

xplicitly evaluate population connectivity as driven by landscapeesistance and dispersal ability.

Our analysis appears to be the first that combines spatially syn-ptic connectivity modeling with explicit evaluation of the effectsf differential dispersal ability on the ability of species to act as

connectivity indicators or umbrellas for other taxa. Instead ofomputing pair-wise corridors between a priori defined sourcese.g. Beier et al., 2007), we utilized the resistant kernel approacho predict spatially synoptic patterns of connectivity and identifyll-directional dispersal (e.g. Compton et al., 2007; Cushman et al.,010a). This allows a much more complete picture of connectivitycross continuous space. Second, we evaluated scale dependencyxplicitly across four dispersal threshold distances, correspondingo an 8-fold range in dispersal ability. This enabled us to formallyvaluate the sensitivity of individual species and multi-species con-ectivity to dispersal ability. Importantly, by combining a broad

ange of alternative resistance models with multiple dispersalhresholds, we were able to quantify the relationships between dis-ersal ability and ecological characteristics in driving multi-speciesonnectivity across the Northern Rocky Mountains.

We found that the strength of multi-taxa linkages increasegreatly with dispersal ability, roughly doubling from the 5000 mto the 40,000 m dispersal threshold. The strong dependence onthe extensiveness and strength of potential multi-species link-ages on dispersal abilities highlights the importance of explicitlyevaluating connectivity in the context of the dispersal abilitiesof the focal species. Also, the relative absence of strong potentialmulti-taxa linkages at the lowest and highest elevations furtherindicates the potential vulnerability of taxa residing in these areas,and the risk of developing conservation strategies solely on thebasis of number of intersecting linkages across taxa. Specifically,species depending on the lowest and highest elevations in thestudy area are likely most vulnerable to climate change and humandevelopment. However, these portions of the study area are pre-dicted to have relatively weak linkage value based on number ofintersecting species connectivity maps. However, from a practical

conservation point of view efforts may be best invested in protect-ing habitat and maintaining connectivity for these most vulnerablespecies, despite the differing pattern of the strongest multi-specieslinkages.

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S.A. Cushman, E.L. Landguth / Eco

.3. Carnivores as connectivity umbrellas

The long term viability of large carnivores in the Rocky Moun-ains is believed to strongly depend on regional connectivity ofabitat due to the low densities, large home range size, vulner-bility to human disturbance and low fecundity of these speciesNoss et al., 1996; Weaver and Paquet, 1996; Carroll et al., 2001).imultaneously, a number of researchers have proposed usingarge carnivores as umbrellas for habitat and connectivity protec-ion plans (Eisenberg, 1988; East, 1981; Soule, 1987; Shafer, 1990;eterson, 1988; Noss, 1993; Noss et al., 1996; Carroll et al., 2001).

Beier et al. (2009a,b) found that three carnivore species wereeak umbrellas to protect connectivity of eight other focal species.

imilarly, our study indicates that American marten, wolverine andlack bear are weaker than average umbrellas in comparison tohe full suite of modeled organism. This suggests that carnivoresn general may not always be strong choices as umbrella species.he weak performance in our case was due to two factors. First,merican marten and wolverine are high elevation habitat spe-ialists. Connectivity of high elevation habitat is highly limited forobile animals in our study area, resulting in patterns of habitat

onnectivity for these species that have relatively little overlap withonnected habitat for species associated with lower elevations.econd, American black bear is a forest obligate and road sensi-ive species, resulting in connected habitat that poorly intersectspecies not dependent on forest cover for movement.

.4. Scope and limitations

Our analysis makes several assumptions that should be consid-red in interpreting the results. First, it is unknown the degree tohich the 144 hypothetical species we analyzed reflect the land-

cape resistance properties of the full range of real native speciesn the system. However, the limited empirical research that haseen done to develop robust connectivity maps for native speciesas matched models we use in our set (e.g. Cushman et al., 2006;chwartz et al., 2009; Wasserman et al., 2010; Shirk et al., 2010). Inddition, landcover, elevation and roads are major attributes of theandscape that are likely to have dominant effects on movementnd dispersal of most native animals. Proposing a large combina-ion of resistance models based on these variables, and evaluatinghem across a range of dispersal abilities we feel likely produces

set of hypothetical species that reflect the range of response ofost native species. However, there may be taxa that are poorly

epresented in our modeling effort. First, species with very limitedispersal ability (less than 5000 m) are not represented in the rangef dispersal ability we modeled. Many taxa, such as invertebrates,eptiles, amphibians and small mammals fall into this group. Also,irds tend to have large mobility and are relatively insensitive toerrestrial resistance features such as roads. Thus our results areest interpreted as applying to terrestrial animals with between000 m and 40,000 m dispersal ability.

We assumed that dispersing organisms originated from eachocation in the landscape with optimal dispersal habitat conditionsnd that no dispersers originated from locations with suboptimalonditions. Given that we are modeling connectivity for a largeange of hypothetical organisms, real distribution and abundanceata are not available. Thus, using habitat quality as a surrogateor distribution is necessary and appropriate. However, it shoulde kept in mind that real populations are often distributed idiosyn-ratically with respect to patterns of habitat quality because of aange of historical population factors. Thus, our results indicate

abitat connectivity for species associated with each resistanceodel. This is quite different than population connectivity per se,hich is highly constrained by the actual distribution and density

f the species in question. To apply these methods to predict actual

Modelling 231 (2012) 101– 112 111

population connectivity for real species would require: (1) empir-ical validation that the resistance model is correct for the speciesof concern (such as Cushman et al., 2006; Schwartz et al., 2009;Wasserman et al., 2010), (2) reliable and spatially complete infor-mation on the distribution and density of the species across thestudy area. Our results, however, by evaluating habitat connectivityfor a broad range of hypothetical species, and the sufficiency of pub-lic lands in protecting habitat connectivity, gives useful informationon which species in which parts of the ecosystem are likely to bemost in need of protection, and provides a framework for devel-oping optimal conservation strategies for those species. It wouldbe interesting for future work to evaluate the smallest subset ofspecies required to provide a connectivity umbrella for all nativespecies.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.ecolmodel.2012.02.011.

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