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Detailed model of shelter areas for the Cantabrian brown bear
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a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m
w w w. e l s e v i e r. c o m / l o c a t e / e c o l i n f
Detailed model of shelter areas for the Cantabrian brown bear Pilar García⁎, Javier Lastra, Jorge Marquínez, Carlos Nores INDUROT (University of Oviedo), Campus de Mieres 33600 Mieres, Spain
AR TIC LE I N FO
ABS TR ACT
Article history:
We propose a methodology to determine critical areas for brown bear (Ursus arctos) in the
Received 5 September 2006
Cantabrian Mountains (Spain), considered as shelter areas in which there are
Received in revised form
concentrations of winter dens and groups of resting sites. We assessed a function which
1 August 2007
enabled a precise spatial distribution model to be constructed. This function helps to explain
Accepted 7 August 2007
the present division of the range of the bears into two isolated populations and to identify areas for protection with the aim of increasing their probabilities of survival or connection.
Keywords:
This has been done using a multivariate analysis and by applying the results to variables
Logistic regression
modelled on a geographic information system. We took a location containing 161 bear dens
GIS
and resting sites. These positions have been characterised by modelling variables in a grid of
Brown bear
50-m cells derived from the topography (digital model of elevations, orientations, slopes,
Corridor
altitude variability in an area around each location). The variables include those that
Shelter area model
quantify distance weighting according to difficulties associated with access to different
Endangered population
disturbance elements (infrastructures or buildings), variables that quantify distances to
Ursus arctos
refuges (forests, scrubs, and rocky areas) and their shape (perimeter, thickness, eccentricity or shape of the forest and type of rocky areas). We constructed a logistic regression model with this data that locates and highlights shelter areas with an 86% average rate of precision. This result reveals that the eastern population has shelter areas around the present range, indicating a possible expansion area; however, there is a gap measuring close to 30 km between both populations that is almost entirely lacking in shelters and that contains important infrastructures. Increasing refuge conditions in the gap to connect shelter areas will make it possible to recover the area as a corridor. © 2007 Elsevier B.V. All rights reserved.
1.
Introduction
The brown bears (Ursus arctos) of the Cantabrian Mountains represent the westernmost limit of the brown bear range in Europe and constitute the most severely threatened and probably oldest of the three European evolutionary lineages identified by Taberlet and Bouvet (1994). The Cantabrian brown bears have split into two populations – the Eastern and Western – isolated by a passage of some 40 or 50 km (Fig. 1) (Clevenger et al., 1999). This separation has widened
in recent years so that the distance between the core areas of reproductive females has doubled in the last decade (Palomero et al., 2006) and has given rise to differences in the genotypes of both populations due to genetic drifting (García-Garitagoitia et al., 2006). This isolation is usually attributed to increasing human pressure and to the loss of suitable habitat (Nores and Naves, 1993; Wiegand et al., 1998). The Eastern population is located mainly in the Castille– Leon region and houses an estimate of 20–25 specimens (Campo et al., 1984; Clevenger and Purroy, 1988; Rey et al.,
Abbreviations: GIS, Geographic Information Systems; SIAPA, Environmental Information System of the Principality of Asturias; IGN, National Geographic Institute; UTM, Universal Transverse Mercator projection; ROC, Relative Operating Characteristic; ESRI, Environmental Systems Research Institute; INDUROT, Natural Resources and Land Planning Institute of the Oviedo University. ⁎ Corresponding author. E-mail address: [email protected] (P. García). 1574-9541/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoinf.2007.08.003
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Fig. 1 – Study area and the present distribution range of brown bear in the Cantabrian Mountains.
2000) with a non-significant growth rate since the eighties (Palomero et al. 2006). The Western population is more abundant. Naves et al. (1999) has estimated that this population suffered a 7% annual decline in numbers between the years 1982 and 1990, after which it stabilized at around 56 bears between 1990 and 1995. The population has tended to recover over the last decade at an annual increase of 7.7% (Palomero et al., in press). An estimate based on molecular tagging yielded a result of 118 bears in 2002 (García-Garitagoitia et al., 2003). The brown bear was legally protected in Spain in 1973. In 1990, the national cataloguing of threatened animals was approved, including the bear as an endangered species and the regional governments were forced to implement recovery plans for endangered species. In 1991, the region of Asturias, where most of the Western bear population is located, approved the Recovery Plan for the Brown Bear that was renewed in 2002. This renewal establishes the need to draw up an open inventory catalogue of critical areas for the conservation of the species. Areas where winter denning was concentrated were considered critical given their relevance to survival and reproduction of brown bears (Swenson et al., 1997; Linnell et al., 2000). The enactment of this regional recovery plan has set a research project into motion to identify potential critical areas. The method described in this paper has been used for this purpose. Hence, the aim of this work is to determine the areas that can be classified as critical for brown bear conservation, within the scope of application of the recovery plan considering “shelter areas which allow the existence of winter dens or groups of resting sites used by bears in different seasons” as such. Some authors have created modelling functions for habitats of different species by combining the use of geographic information systems (GIS) and various statistical analyses such as multiple regression (Radeloff et al., 1999) or logistic regression (Pereira and Itami, 1991; Fielding and Haworth, 1995; Gros and Rejmanek, 1999; Manel et al., 1999; Ritter and Savidge,
1999; Karl et al., 2000; Pearce and Ferrier, 2000; Bailey et al., 2002; Luck, 2002; Naves et al., 2003; Petram et al., 2004). Other authors have successfully used other techniques combined with GIS, such as machine learning, neural networks, or additive models (Huang and Jensen, 1997; Manel et al., 1999; Kobler and Adamic, 2000; Ray et al., 2002). Maquínez et al. (1997), Clevenger et al. (1997), and Naves et al. (2003) have conducted specific studies on brown bear habitat in areas close to the study area. The relationship between shelter areas for bears and environmental variables has long been discussed in Europe, with some authors using topographical variables (Zunino, 1976; Mysterud, 1983) or terrain lithology (Naves and Ruano, 1993). The relation between vegetation and dens has also been the object of study (Mysterud, 1983; Clevenger and Purroy, 1991; Naves and Palomero, 1993b). Some studies in Europe have integrated human activities and habitat characteristics of in a single work (Clevenger et al., 1992, 1997; Dupre et al., 1999; Kobler and Adamic, 2000; Naves et al., 2003; Wiegand et al., 2004; Petram et al., 2004). In this study, we have modelled variables derived from topography, vegetation, rocky characteristics, and human activities, and we have searched for relations between these variables and shelter areas for the bear. The maps of shelter areas for bears elaborated by means of this methodology could constitute a useful management tool for protected natural areas and for regulating activities plans to be carried out in the zone.
2.
Methods
2.1.
Study area
The present range of the Cantabrian brown bear covers some 7100 km2 (Wiegand et al., 1998) of which 3960 are located in Asturias, in the region for which we have elaborated the
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model. Much of this area is merely potential however, as it possesses good habitat conditions, but the species is only sporadically present. The area selected for this paper represents a total 6222 km2, of which 4230 km2 are within the official brown bear range area (potential or actual) considered by the recovery plan; the remaining area is adjacent to it. This study area represents the core area for the Western Cantabrian brown bear population including at least 75% of the known reproductive groups and constitutes the limit of the Eastern population (Palomero et al., 2006). This zone was selected because of its uniqueness within the distribution area, including the potential corridor between both Cantabrian nuclei and the current existence of a detailed cartographic data base of environmental variables necessary for this study. It is available at SIAPA (INDUROT, 1989–2001) at a scale of 1:25,000 that includes geological, plant, topographical, and road data. From a biogeographical perspective, the study area is located within the Orocantabrian province in the Eurosiberian region, on the border with the Mediterranean domain, which is dominant in the Iberian Peninsula. Twenty-five percent of the territory is covered by a natural wooded surface, mainly beech (Fagus sylvatica) and oaks (Quercus petraea and Q. pyrenaica), in addition to abundant chestnut trees (Castanea sativa); relatively few (2%) forest crops (Pinus sp pl and Eucalyptus globulus), 8% piornales (Genista florida and Cytisus scoparius) and tall shrubbery, 32% heath (Erica sp. pl) and gorse (Genista hispanica), 23% herbaceous plants, 3% rocky outcrops, and 2% sub-Alpine or Alpine territory, and the remaining artificial or highly modified areas. Another important aspect of the area is its geological and geomorphological heterogeneity. The lithologies are quite varied and exhibit vast areas with a predominance of mainly siliceous precambric–ordvicic materials (slate and sandstone with some calcareous or coal intercalations) in the western zone. The central and eastern parts are characterized by an abundance of calcareous materials (limestone and dolomite and numerous layers of coal) interspersed with siliceous materials (sandstone, quartzite, and slate), dating mainly from the Silurian–Carboniferous Periods. The zone is characterized by its sharp relief and abrupt areas; in these areas the geological pre-quaternary substrata emerge directly, with unaltered mantle, surface formations, or edafic covering, comprising almost 10% of the zone. We can divide these abrupt areas into two types: those that reveal a more accused relief due to resistant lithology (mainly quartzites and some calcareous scarps), covering approximately 4% of the study area on the one hand, and the rest, consisting of less resistant, rocky outcrops (sandstones, slates, and limestone) that account for another 6% of the area under study. The study area has elevations ranging from 50 to 2500 m. The human population density is low (an average of 25 inhabitants for km2). The human population is distributed in small nuclei with large unoccupied areas which coexist with the bear with few problems.
2.2.
Sampling method
The existing information regarding the location characteristics of winter bear dens (Naves and Palomero, 1986, 1987,
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1989) was reviewed. Sampling was arbitrarily timed and most of the sites described turned out to be winter dens, although it was not always possible to positively distinguish whether they were winter dens or resting sites. Finally, we have examined 161 locations with a variable precision from 1 to 100 m (25 with 100 m precision and the remaining with 1 m precision). Additionally, a series of 600 random points (with X, Y coordinates) has been generated between the latitude and longitude limits of the area studied. Those that fall outside the range of the species and those that fall within a distance of less than 500 m from whichever winter bear dens were eliminated, leaving a total of 355 random points that were surveyed and confirmed in the field as ‘no presence of bear dens’ within 500 m of them. These absence/presence locations have been characterized in a grid consisting of 50 × 50-m cells. Eighty percent of the data have been used in developing the model and the remaining 20% have been reserved for validation.
2.3.
Measuring independent variables
The variables used in this work have been chosen from among the most widely used variables. Some authors have detected relations between the dens and slope, rocky outcrops, elevation, orientation, the presence of forests, and roads or other human disturbances (Naves and Palomero, 1993b; Huber and Roth, 1997; Groff et al., 1998; Linnell et al., 2000; McLoughlin et al., 2002; Gaines, 2003; Ciarniello et al., 2005). These are also widely used variables in bear habitat studies (Clark et al., 1993; Naves and García, 1996; Clevenger et al., 1997; Kobler and Adamic, 2000; Gibeau et al., 2002). Because the territory under study is very abrupt and the landscape is highly fragmented, variables describing the degree of forest fragmentation (cthickness, cexen, carea) and the ruggedness of the rock (crock) have been taken into account. We used the Arc-Info GIS 7.1.2 grid module (ESRI Inc) to model the distribution of the variables within the study area. Layers of vegetation, lithology, rocky outcrops, infrastructures, and topography from SIAPA have been used as background information to create the variables used. This information was collected between 1989 and 1999. Fifty-meter cells referenced on the UTM georeference system, zone 30, international ellipsoid, have been used for the layers and have been grouped into four types according to significance (Table 1).
2.4.
Topographical derived layers
The Digital Elevation Model (tDEM) used was derived from the digitization of the contour lines and points of the national topographical map 1:25000 of the National Geographic Institute (IGN). This model was made with an irregular, triangulated network and then converted into a 50-m square lattice. The models of slope (tslope), aspect taspect), and elevation variability-in a 10-cell focal area-(tvarDEM) are derivatives of the model (tDEM). The taspect and tslope variables were created using the Arcinfo aspect and slope grid module functions, respectively (Burrough, 1986).
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Table 1 – Description of layers used in developing model for brown bear shelter areas GIS thematic layers Layer
Meaning
Topographical derived layers tDEM Digital elevation model taspect Digital aspect model tslope Digital slope model tvarDEM Variation on the digital elevation model in the area around a positive localization Disturbance elements accessibility layers ahouse Building density measurement of the closest cell with houses aroad Indirect traffic density measurement of the closest infrastructure cell depending on the type of route Refuge distance layers dforest Euclidian distance measurement to the closest forest cell drock Euclidian distance measurement to the closest rocky outcrop cell Refuge descriptor layers crock Class of rocky soil cthickness Diameter of the maximum circle within the closest forest cexcen Ellipse eccentricity contained within the closest forest carea Closest forest area
2.5.
Disturbance elements accessibility layers
These layers quantify the accessibility to elements of clearly negative incidence for the presence of the brown bear, such as human settlements or roadway infrastructures. These elements have been extracted from the topographic map with a scale of 1:5000 and converted to a grid with a 50-m cell size. The costdistance command of Arc-Info (ESRI, 1992) was used to calculate the cost to cross from one cell a to another b (costd_ab), using slope as cost units. Accessibility to disturbance elements is constructed by calculating the least accumulated cost for each cell to the disturbance element cells. The lesser distance is measured not in geographical units, but in slope units. Thus, itineraries with the same distance, but greater slope are considered to be further away. This type of analysis was performed for each 50-m pixel for the entire study area. Three accessibility variables have been calculated: to the closest roadway, pathway, and human settlement. Using this accessibility matrix, we can locate the closest disturbance element for each cell of terrain. Features of this closest cell can be extracted, in order to create other derived variables: 1) density of buildings in the closest constructed cell (ahouse) and 2) type of infrastructure (motorway, national and district roads…) of the cell (aroad), an indicator of the degree of impact of the cell with the closest infrastructures. The built-up density has been created from a map of points where each point represents a building and carries an attribute of the occupied area. Using this data, the density for each cell of the grid can be calculated.
2.6.
Refuge distance layers
These layers quantify the distance to elements which a priori would seem to be positive for the presence of bears or to resting sites such as rocky outcrops and forests. Based upon the existent vegetation and rocky outcrops maps (SIAPA), the Euclidean distance has been calculated from each cell to the forest (dforest) and to the nearest rocky outcrop (drock), without factoring in slope as in this case it does not imply difficulty of access (the bear is capable of crossing extremely strong slopes). Furthermore, other distances to refuge-related places such as the Euclidean distance from each cell to the limestone, quartzite, or mixed outcrop or to the nearest thicket, have been calculated for the location of critical areas; however, they have turned out to be statistically inadequate and consequently, have not been integrated into the estimate.
2.7.
Refuge descriptors layers
These layers describe certain features of the closest rocky outcrops and forests. crock describes how abrupt the rocky layers are. Given that the forest in the area was highly fragmented (García et al., 2005), which represents a negative factor for the species (Naves and García, 1996), other variables have been used to measure the shape and size of the nearest forest: cthickness measured the diameter of the largest sized circle contained within the closest forest; cexcen, the eccentricity of the ellipse which the closest forest contains; carea, the area of the closest forest, and cperimeter the perimeter of the closest forest. GIS makes it relatively easy for us to obtain the values of different variables for each cell into which the territory has been divided. We have used the values of cells corresponding to the locations and to the random points taken into consideration for the statistical analysis.
2.8.
Statistical analysis
Using the statistical software package SPSS v14 and the multiple logistic regression technique, we have related the presence/absence of the brown bear (dependent variable) to values corresponding to the previously described thematic layers (independent variables). Multiple logistic regression is a commonly used multivariant analysis technique to create habitat prediction models for different species (Fielding and Haworth, 1995; Manel et al., 1999; Karl et al., 2000; Pearce and Ferrier, 2000; Bailey et al., 2002; Luck, 2002). This technique facilitates distribution by means of the probability function that obtains a binary variable on the basis of non-normal independent or even qualitative variables. Logistic regression requires that the data and variables to be used meet a series of criteria: 1) the sample must be random; which has already been commented on, 2) there must be a monotonous relation between the different variables that is supported by the data as in Fig. 2, in which the proportion of data in each variable is depicted depending on the absence/ presence of shelter areas and where no behaviour is observed that would suggest any lack of monotony between each
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Fig. 2 – The proportion of data in each variable is not indicative of lack of monotony between said variables and the proportion of presence.
variable and the proportion of presence, and 3) mutual noncollinearity of the variables, which is corroborated by the FIV collinearity statistic(Table 2). The variables to be incorporated into the model were selected using the ‘forward stepwise conditional’ criterion. Hence and in order to assure the robustness of the model, 100 repeat Montecarlo samplings of different sizes were carried out (50, 70, 80, 90, and 95%) using the sub-sampling technique.
As a result, the following variables were found to be the most significant in relation to the number of times they were selected: tvarDEM (100%), tslope (99%), drock (99%), crock (97%), tDEM (96%), and dforest (73%). The remaining variables were
Table 3 – Coefficients and statistics for the regression function developed Coefficients Coefficient Standard Wald Significance and regression value deviation statistics
Table 2 – Collinearity statistics Statistics of collinearity Non-categorical variables tDEM tslope dforest drock tvarDEM
Tolerance
VIF
.219 .302 .099 −.153 .127
5.222 7.370 1.351 − 2.208 2.625
b0-constant b1-tDEM b3-tslope b4-tvarDEM b7-dforest b8-drock b9-crock Nagelkerke R2 Cox and Snell R2
−11.138 0.004 0.076 −0.003 0.781 0.544 −1.722 0.781 0.544
3.012 0.001 0.027 0.027 0.001 0.001 0.404
13.7 28.8 16.0 8.0 9.3 15.9 18.1
0.000 0.000 0.000 0.005 0.002 0.000 0.000
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Fig. 3 – Shelter areas for denning brown bears in Asturias, obtained by the logistic regression model. selected fewer than 35% of the times and hence, were eliminated from the analysis.
3.
Results
3.1.
The model
The model is defined by the following expression: FðxÞ ¼ b1 tDEM þ b2 tslope b3 dforest b4 drock b5 crock þ b6 tvarDEM þ b0 where b1… b6 represent the coefficients obtained for each independent variable and b0 is a constant (Table 3). A model was constructed based on this function (Fig. 3). This model correctly predicts 94.9% of the absences and 87.0% of the presences, for a threshold of 0.5. The entire adjustment of the model is specified by Nagelkerke (0.78) and Cox–Snell (0.54) statistics.
3.2.
Selection threshold
The function of the logistic regression developed gives us a probability value for consideration as being a critical area for each 2500-m2 cell of the territory which varies between 0 and 1. It is the decision of the expert to decide what value within this range will be useful as the threshold to distinguish the selected zones (considered as critical areas for bears) from the non-selected ones (considered as non-critical). Most of the papers use 0.5 as a cut-off (Tabacknick and Fidell, 1996; Manel et al., 1999; Pearce and Ferrier, 2000; Luck, 2002), However, this cut point will decrease sensitivity and increase specificity (Jiménez-Valverde and Lobo, 2007). The choice of threshold (Fielding and Bell, 1997; Fielding, 2002; Pearson et al., 2004) must be accompanied by a decision based on a more in-depth study of the effect the threshold variation has on the precision of the model by means of ROC graphics (relative operating characteristics) (Metz, 1978). This is a curve which is created using pairs of sensitivity (true positive fraction, y-axis) and specificity (false positive fraction, x-axis) values calculated at different cut-offs (Fig. 4).
The percentage of sites correctly predicted by the territory model to be occupied or unoccupied varies with a change in the probability threshold value (Table 4). As a result of this, we found that with a 0.5 probability value, the model correctly predicted between 88–97% of the positive sites and between 92–94% of the negative sites. Correct positive predictions and sensitivity increase as the threshold value decreases, while the correct negative predictions and specificity increase as the cut-off value increases. By having more absences than presences makes the model tend to classify absences better. A sensitivity/specificity analysis can provide a cut-off that offsets this bias. The ROC curve (Fig. 4) displays that a 0.8 cut-off point corresponds to an approximate sensitivity of 75% and a specificity of 2%, whereas sensitivity increases up to 88% and specificity rises to 3% with the data that had been reserved for validation. Furthermore, the practical objective for which the model was created imposes additional responsibility. The political and legal application of the results on the layout of the territory forces us to increase the chosen threshold to a much more restrictive level, in an attempt to achieve high specificity.
Fig. 4 – ROC curve.
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Table 4 – Statistics of predicted values for a different threshold of probability Threshold probability
True presences correctly predicted (%)
True absences correctly predicted (%)
Validation dataset
Model dataset
Validation dataset
Model dataset
Validation dataset
Model dataset
Validation dataset
98 97 96 92 88 87 84 75 58
100 100 97 97 97 94 91 88 76
77 84 86 91 92 94 96 98 99
84 85 90 92 94 98 98 98 100
2 3 4 8 12 13 16 25 42
0 0 3 3 3 6 9 12 24
24 17 14 10 8 6 5 2 2
18 16 11 10 8 3 3 3 2
Therefore, insofar as the results at different levels are concerned (Table 4, Fig. 4), we have selected a 0.8 probability value as the decision threshold for this paper. This high restriction level sacrifices the consideration of critical areas for those zones where uncertainty is greater and lowers the extension of these areas from 727 to 378 km2. The limit that regional laws impose upon these zones makes it necessary to increase security levels when determining the cut-off.
Testing the model
To assess the degree of final discrimination of the model between the zones with the presence or absence of areas that are critical for the brown bear, four indices have been calculated (Lindenmayer et al., 1990; Pearce et al., 1994; Pearce and Ferrier, 2000; Luck, 2002): the true positive fraction or sensitivity, as the percentage of true presences correctly identified; the true negative fraction, as the percentage of true absences correctly identified; the false positive fraction or specificity, as the percentage of true presences incorrectly identified and the false negative fraction, as the percentage of true absences incorrectly identified. These indices have been applied to the datasets used in developing the model and to the sets of data reserved for validation (Table 5).
Table 5 – Hosmer–Lemeshow test Decile
Absent
Present
Total
Observed Estimated Observed Estimated 1 2 3 4 5 6 7 8 9 10
50 50 50 50 49 49 37 18 0 2
CHISQUARE Results
True absences incorrectly predicted (%)
Model data set 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
3.3.
True presences incorrectly predicted (%)
10.271
50 50 49.99 49.95 49.70 48.12 39.14 13.82 3.48 .76
0 0 0 0 1 1 13 32 50 51
DEGREES OF FREEDOM 8
.00 .00 .00 .04 .29 1.87 10.85 36.17 46.51 52.23
50 50 50 50 50 50 50 50 50 53
SIGNIFICANCE .247
From these indices, one can obtain a means of accuracy such as the general percentage of places correctly predicted, which in this case was 86.5%, for the data used in developing the model, and 93% for the data reserved for validation (Table 4). The ROC graph (Fig. 4) gives us a new means by which to quantify the function's discriminant capacity, independently of the threshold value chosen and which is given as the area under the ROC curve (Fielding and Bell, 1997; Pearce and Ferrier, 2000). This curve was created with 504 pairs and yields a 94% discrimination value. Another measurement of goodness-of-fit has been obtained by means of the Hosmer–Lemeshow test (Hosmer and Lemeshow, 1989), which basically consists of dividing the probability coverage into “deciles-of-risk” and then calculates the distribution of both the presence and absence of bears predicted by the equation and the true, observed values. Both the expected and observed distributions are contrasted by means of a chi-square test. The results obtained (Table 5) reveal that the test was not significant (p = 0.247 with 8 degrees of freedom).
4.
Discussion and conclusions
The method described herein has enabled a detailed map of potential shelter areas for the brown bear to be created of their core area in Spain. This has been possible thanks to the geographic accuracy and the high quality of the initial data, as well as the detailed management of this data in a 50 m-cell grid. The model maps out the zones where bear currently exist, as well as others that possess adequate conditions for bear and has pointed out that some areas have not been prospected and that it is possible that other bear dens exist in those areas. The shelter areas appear to overlap in the bear range, especially in the reproductive areas (Naves et al., 2003; Palomero et al., 2006), which may have to do with the use the females make of winter dens to give birth to their cubs. The Western bear population has an important amount of potential shelter areas that largely coincide with their present distribution. As for the zones that are east of the present range area, the model reveals that they are not likely to have denning sites, which is a handicap for the geographical spread of bears under present conditions, particularly towards the space that separates them from the Eastern population over some 30 km in length in a straight line (Fig. 3).
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On the contrary, the high value of shelter in the study area to the north of the present Eastern population reveals a large area of potential distribution of the species (Fig. 3) and suggests that this does not currently constitute a limiting factor to the possibility of this population's expansion. The natural habitat is also calculated to be of good quality according to different methods (Marquínez et al., 1997; Naves et al., 2003). The problems for this population arise from their lower birth rate (the mean litter size was consistently lower among the Eastern population than the Western one: 1.66 vs. 2.24, respectively, in the 1980s (Palomero et al., 1993) and 1.45 vs. 1.8 in the 1990s (Palomero et al., 2006) and from the scant number of individuals (fewer than three reproductive females were detected per year in the entire population; Palomero et al., 2006), which could render them genetically non-viable if the connection between the two subpopulations is not re-established (Wiegand et al., 1998; Rey et al., 2000). The Cantabrian bear population split into two nuclei during the first half of the twentieth century (Nores and Naves, 1993; Clevenger et al., 1999). This rupture has been interpreted as the consequence of the development of communication infrastructures and recent human pressure (Nores and Naves, 1993; Clevenger and Purroy, 1991; Palomero et al., 1993; Wiegand et al., 1998; Naves et al., 2003). The habitat quality models developed for the area (Marquínez et al., 1997; Naves et al., 2003) do not reveal any loss of quality in food availability or plant coverage eastwards of the main infrastructures, but they do for shelter (dens) in a large area several kilometres to the east. The model points to this space without bears as an area with poor bear den potential. The model predominantly selects variables that are related to the refuge potential provided by the terrain's topographic conditions or its lithology, which we can consider the territory's prime nature. Of the five variables selected, three are topographic variables (mde, varmde, and slope) and two are refugerelated (eubq and euroca). Only the distance to the forest is not dependent on the territory's primary nature and could easily be modified to increase refuge areas. This result is consistent with the one in Slovenia obtained by Petram et al. (2004), who found that the type of terrain is the most important variable affecting the selection of winter dens. The most widely used dens are those that are least accessible to humans. The poor natural conditions for the establishment of a shelter area in the area that separates both nuclei appear to represent an important impediment to bear passage connecting both populations. On the one hand, the females are more philopatric than the males and they need dens in which to give birth and, secondly, the concentrations of reproductive females attract the males to their surroundings, especially during heat (Palomero et al., 2006). This behaviour might explain why the gap between both populations' dens is an area where bear presence is scarce. Males are responsible for dispersion and communication between areas. However, if the core areas, where females and dominant males are more abundant, are far apart from each other, the possibility of the males, even peripheral young males, (Swenson et al., 1998; Jerina et al., 2003) spanning the distance between both populations also decreases. This may have been the cause for the genetic differentiation between both Cantabrian nuclei (García-Garitagoitia et al., 2006). One must remember that the gap between bear populations coincides with a siliciclastic lithology corresponding to
the Central Carboniferous Basin (Julivert, 1971), where abrupt rocky outcrops are scarce, since winter dens in the Cantabrian Mountain Range, as in the Pyrenees, are preferably located in natural caves or, after that, in digging dens made by the animals themselves, but not at the base of trees or hollow trunks, as seen in other European populations (Naves and Palomero, 1993a). The same lithological constraints could similarly affect population splits in other mountain species, such as chamois (Rupicapra pyrenaica) (Pérez-Barbería et al., 1996) and, to a lesser extent, capercaillie (Tetrao urogallus) (Obeso, 2003). Even though the rupture in these populations might also be accounted for by human disturbances, lithology appears to be a better explanation for the decrease in the potentiality of the range of medium-size and small mammals in this same area, such as pine marten (Martes martes), snow vole (Chionomys nivalis), and edible dormouse (Glis glis) (Palomo and Gisbert, 2002, pp. 486–488). Smaller species are not as affected by infrastructures and general human avoidance as are large vertebrates. In any case, thus far, human disturbance was the only justification to explain the origin of the fracture in the populations and the non-permeability for bears, but this paper reveals the lack of shelters as an unsuitable habitat factor that increases the isolation between both nuclei. If we add the fragmentation in the landscape (Obeso and García, 1990) associated with farming, then the reason for the gap in the mountain corridor is better explained. The preservation of corridors between populations is essential to minimizing genetic isolation (Harrison, 1992; Beier, 1993). In the case of the Cantabrian bear population, the reestablishment of a corridor between the two nuclei is critical, given the scant number of bears in both nuclei and the low genetic variability detected (García-Garitagoitia et al., 2006). The results afforded by the model reveal how the present gap in the bear distribution area has poor shelter capacity and this may represent the major impediment to re-uniting the two populations. This new perspective can have an impact on management plans for the Cantabrian brown bear. Given that the Western population has sufficient population density (Wiegand et al., 1998) and the natural habitat in the corridor is of good quality (Naves et al., 2003), shelter capacity would be an issue of supreme importance in explaining the current difficulties involved in connecting both nuclei and achieving communication between them. In light of the variables considered in the analysis, actions in the passage should focus on decreasing forest fragmentation, which would have a positive impact on refuge capacity. Other variables such as those related to relief or rocky outcrops are practically impossible to improve, as they are intrinsic to the terrain, albeit it would be useful to facilitate the settling of females in the closer refuge areas, which would be conducive to getting the males to pass from one nucleus to another in order to achieve the genetic exchange between both populations. Insofar as variables related to human settlements and communication infrastructures are concerned, it is unlikely that they will be improved, given that they affect social and economical interests. Therefore, actions should focus on decreasing the barrier effect of transportation infrastructures and buildings, securing sufficient and efficient crossing points, recovering the natural vegetation cover, connecting closer shelter areas following sub-optimal shelter area (comprised over the probability threshold 0.5) in the
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gap, avoiding the expansion of infrastructures and diverting future urbanization developments (of lesser importance in the past) towards other areas where the presence of bears is improbable or that have no value as corridors, as proposed by other studies (Churszcz et al., 2003; Kaczensky et al., 2003).
Acknowledgments We wish to acknowledge the Government of Asturias for providing data relating to known shelters and lairs and for the interest shown in applying these results to conservation policies for the Cantabrian bear population. We also wish to thank G. Palomero for his data and ideas for this paper, and A. Colubi and G. Plantamura for their valuable contribution in revising this manuscript.
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a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m
w w w. e l s e v i e r. c o m / l o c a t e / e c o l i n f
Detailed model of shelter areas for the Cantabrian brown bear Pilar García⁎, Javier Lastra, Jorge Marquínez, Carlos Nores INDUROT (University of Oviedo), Campus de Mieres 33600 Mieres, Spain
AR TIC LE I N FO
ABS TR ACT
Article history:
We propose a methodology to determine critical areas for brown bear (Ursus arctos) in the
Received 5 September 2006
Cantabrian Mountains (Spain), considered as shelter areas in which there are
Received in revised form
concentrations of winter dens and groups of resting sites. We assessed a function which
1 August 2007
enabled a precise spatial distribution model to be constructed. This function helps to explain
Accepted 7 August 2007
the present division of the range of the bears into two isolated populations and to identify areas for protection with the aim of increasing their probabilities of survival or connection.
Keywords:
This has been done using a multivariate analysis and by applying the results to variables
Logistic regression
modelled on a geographic information system. We took a location containing 161 bear dens
GIS
and resting sites. These positions have been characterised by modelling variables in a grid of
Brown bear
50-m cells derived from the topography (digital model of elevations, orientations, slopes,
Corridor
altitude variability in an area around each location). The variables include those that
Shelter area model
quantify distance weighting according to difficulties associated with access to different
Endangered population
disturbance elements (infrastructures or buildings), variables that quantify distances to
Ursus arctos
refuges (forests, scrubs, and rocky areas) and their shape (perimeter, thickness, eccentricity or shape of the forest and type of rocky areas). We constructed a logistic regression model with this data that locates and highlights shelter areas with an 86% average rate of precision. This result reveals that the eastern population has shelter areas around the present range, indicating a possible expansion area; however, there is a gap measuring close to 30 km between both populations that is almost entirely lacking in shelters and that contains important infrastructures. Increasing refuge conditions in the gap to connect shelter areas will make it possible to recover the area as a corridor. © 2007 Elsevier B.V. All rights reserved.
1.
Introduction
The brown bears (Ursus arctos) of the Cantabrian Mountains represent the westernmost limit of the brown bear range in Europe and constitute the most severely threatened and probably oldest of the three European evolutionary lineages identified by Taberlet and Bouvet (1994). The Cantabrian brown bears have split into two populations – the Eastern and Western – isolated by a passage of some 40 or 50 km (Fig. 1) (Clevenger et al., 1999). This separation has widened
in recent years so that the distance between the core areas of reproductive females has doubled in the last decade (Palomero et al., 2006) and has given rise to differences in the genotypes of both populations due to genetic drifting (García-Garitagoitia et al., 2006). This isolation is usually attributed to increasing human pressure and to the loss of suitable habitat (Nores and Naves, 1993; Wiegand et al., 1998). The Eastern population is located mainly in the Castille– Leon region and houses an estimate of 20–25 specimens (Campo et al., 1984; Clevenger and Purroy, 1988; Rey et al.,
Abbreviations: GIS, Geographic Information Systems; SIAPA, Environmental Information System of the Principality of Asturias; IGN, National Geographic Institute; UTM, Universal Transverse Mercator projection; ROC, Relative Operating Characteristic; ESRI, Environmental Systems Research Institute; INDUROT, Natural Resources and Land Planning Institute of the Oviedo University. ⁎ Corresponding author. E-mail address: [email protected] (P. García). 1574-9541/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoinf.2007.08.003
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Fig. 1 – Study area and the present distribution range of brown bear in the Cantabrian Mountains.
2000) with a non-significant growth rate since the eighties (Palomero et al. 2006). The Western population is more abundant. Naves et al. (1999) has estimated that this population suffered a 7% annual decline in numbers between the years 1982 and 1990, after which it stabilized at around 56 bears between 1990 and 1995. The population has tended to recover over the last decade at an annual increase of 7.7% (Palomero et al., in press). An estimate based on molecular tagging yielded a result of 118 bears in 2002 (García-Garitagoitia et al., 2003). The brown bear was legally protected in Spain in 1973. In 1990, the national cataloguing of threatened animals was approved, including the bear as an endangered species and the regional governments were forced to implement recovery plans for endangered species. In 1991, the region of Asturias, where most of the Western bear population is located, approved the Recovery Plan for the Brown Bear that was renewed in 2002. This renewal establishes the need to draw up an open inventory catalogue of critical areas for the conservation of the species. Areas where winter denning was concentrated were considered critical given their relevance to survival and reproduction of brown bears (Swenson et al., 1997; Linnell et al., 2000). The enactment of this regional recovery plan has set a research project into motion to identify potential critical areas. The method described in this paper has been used for this purpose. Hence, the aim of this work is to determine the areas that can be classified as critical for brown bear conservation, within the scope of application of the recovery plan considering “shelter areas which allow the existence of winter dens or groups of resting sites used by bears in different seasons” as such. Some authors have created modelling functions for habitats of different species by combining the use of geographic information systems (GIS) and various statistical analyses such as multiple regression (Radeloff et al., 1999) or logistic regression (Pereira and Itami, 1991; Fielding and Haworth, 1995; Gros and Rejmanek, 1999; Manel et al., 1999; Ritter and Savidge,
1999; Karl et al., 2000; Pearce and Ferrier, 2000; Bailey et al., 2002; Luck, 2002; Naves et al., 2003; Petram et al., 2004). Other authors have successfully used other techniques combined with GIS, such as machine learning, neural networks, or additive models (Huang and Jensen, 1997; Manel et al., 1999; Kobler and Adamic, 2000; Ray et al., 2002). Maquínez et al. (1997), Clevenger et al. (1997), and Naves et al. (2003) have conducted specific studies on brown bear habitat in areas close to the study area. The relationship between shelter areas for bears and environmental variables has long been discussed in Europe, with some authors using topographical variables (Zunino, 1976; Mysterud, 1983) or terrain lithology (Naves and Ruano, 1993). The relation between vegetation and dens has also been the object of study (Mysterud, 1983; Clevenger and Purroy, 1991; Naves and Palomero, 1993b). Some studies in Europe have integrated human activities and habitat characteristics of in a single work (Clevenger et al., 1992, 1997; Dupre et al., 1999; Kobler and Adamic, 2000; Naves et al., 2003; Wiegand et al., 2004; Petram et al., 2004). In this study, we have modelled variables derived from topography, vegetation, rocky characteristics, and human activities, and we have searched for relations between these variables and shelter areas for the bear. The maps of shelter areas for bears elaborated by means of this methodology could constitute a useful management tool for protected natural areas and for regulating activities plans to be carried out in the zone.
2.
Methods
2.1.
Study area
The present range of the Cantabrian brown bear covers some 7100 km2 (Wiegand et al., 1998) of which 3960 are located in Asturias, in the region for which we have elaborated the
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model. Much of this area is merely potential however, as it possesses good habitat conditions, but the species is only sporadically present. The area selected for this paper represents a total 6222 km2, of which 4230 km2 are within the official brown bear range area (potential or actual) considered by the recovery plan; the remaining area is adjacent to it. This study area represents the core area for the Western Cantabrian brown bear population including at least 75% of the known reproductive groups and constitutes the limit of the Eastern population (Palomero et al., 2006). This zone was selected because of its uniqueness within the distribution area, including the potential corridor between both Cantabrian nuclei and the current existence of a detailed cartographic data base of environmental variables necessary for this study. It is available at SIAPA (INDUROT, 1989–2001) at a scale of 1:25,000 that includes geological, plant, topographical, and road data. From a biogeographical perspective, the study area is located within the Orocantabrian province in the Eurosiberian region, on the border with the Mediterranean domain, which is dominant in the Iberian Peninsula. Twenty-five percent of the territory is covered by a natural wooded surface, mainly beech (Fagus sylvatica) and oaks (Quercus petraea and Q. pyrenaica), in addition to abundant chestnut trees (Castanea sativa); relatively few (2%) forest crops (Pinus sp pl and Eucalyptus globulus), 8% piornales (Genista florida and Cytisus scoparius) and tall shrubbery, 32% heath (Erica sp. pl) and gorse (Genista hispanica), 23% herbaceous plants, 3% rocky outcrops, and 2% sub-Alpine or Alpine territory, and the remaining artificial or highly modified areas. Another important aspect of the area is its geological and geomorphological heterogeneity. The lithologies are quite varied and exhibit vast areas with a predominance of mainly siliceous precambric–ordvicic materials (slate and sandstone with some calcareous or coal intercalations) in the western zone. The central and eastern parts are characterized by an abundance of calcareous materials (limestone and dolomite and numerous layers of coal) interspersed with siliceous materials (sandstone, quartzite, and slate), dating mainly from the Silurian–Carboniferous Periods. The zone is characterized by its sharp relief and abrupt areas; in these areas the geological pre-quaternary substrata emerge directly, with unaltered mantle, surface formations, or edafic covering, comprising almost 10% of the zone. We can divide these abrupt areas into two types: those that reveal a more accused relief due to resistant lithology (mainly quartzites and some calcareous scarps), covering approximately 4% of the study area on the one hand, and the rest, consisting of less resistant, rocky outcrops (sandstones, slates, and limestone) that account for another 6% of the area under study. The study area has elevations ranging from 50 to 2500 m. The human population density is low (an average of 25 inhabitants for km2). The human population is distributed in small nuclei with large unoccupied areas which coexist with the bear with few problems.
2.2.
Sampling method
The existing information regarding the location characteristics of winter bear dens (Naves and Palomero, 1986, 1987,
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1989) was reviewed. Sampling was arbitrarily timed and most of the sites described turned out to be winter dens, although it was not always possible to positively distinguish whether they were winter dens or resting sites. Finally, we have examined 161 locations with a variable precision from 1 to 100 m (25 with 100 m precision and the remaining with 1 m precision). Additionally, a series of 600 random points (with X, Y coordinates) has been generated between the latitude and longitude limits of the area studied. Those that fall outside the range of the species and those that fall within a distance of less than 500 m from whichever winter bear dens were eliminated, leaving a total of 355 random points that were surveyed and confirmed in the field as ‘no presence of bear dens’ within 500 m of them. These absence/presence locations have been characterized in a grid consisting of 50 × 50-m cells. Eighty percent of the data have been used in developing the model and the remaining 20% have been reserved for validation.
2.3.
Measuring independent variables
The variables used in this work have been chosen from among the most widely used variables. Some authors have detected relations between the dens and slope, rocky outcrops, elevation, orientation, the presence of forests, and roads or other human disturbances (Naves and Palomero, 1993b; Huber and Roth, 1997; Groff et al., 1998; Linnell et al., 2000; McLoughlin et al., 2002; Gaines, 2003; Ciarniello et al., 2005). These are also widely used variables in bear habitat studies (Clark et al., 1993; Naves and García, 1996; Clevenger et al., 1997; Kobler and Adamic, 2000; Gibeau et al., 2002). Because the territory under study is very abrupt and the landscape is highly fragmented, variables describing the degree of forest fragmentation (cthickness, cexen, carea) and the ruggedness of the rock (crock) have been taken into account. We used the Arc-Info GIS 7.1.2 grid module (ESRI Inc) to model the distribution of the variables within the study area. Layers of vegetation, lithology, rocky outcrops, infrastructures, and topography from SIAPA have been used as background information to create the variables used. This information was collected between 1989 and 1999. Fifty-meter cells referenced on the UTM georeference system, zone 30, international ellipsoid, have been used for the layers and have been grouped into four types according to significance (Table 1).
2.4.
Topographical derived layers
The Digital Elevation Model (tDEM) used was derived from the digitization of the contour lines and points of the national topographical map 1:25000 of the National Geographic Institute (IGN). This model was made with an irregular, triangulated network and then converted into a 50-m square lattice. The models of slope (tslope), aspect taspect), and elevation variability-in a 10-cell focal area-(tvarDEM) are derivatives of the model (tDEM). The taspect and tslope variables were created using the Arcinfo aspect and slope grid module functions, respectively (Burrough, 1986).
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Table 1 – Description of layers used in developing model for brown bear shelter areas GIS thematic layers Layer
Meaning
Topographical derived layers tDEM Digital elevation model taspect Digital aspect model tslope Digital slope model tvarDEM Variation on the digital elevation model in the area around a positive localization Disturbance elements accessibility layers ahouse Building density measurement of the closest cell with houses aroad Indirect traffic density measurement of the closest infrastructure cell depending on the type of route Refuge distance layers dforest Euclidian distance measurement to the closest forest cell drock Euclidian distance measurement to the closest rocky outcrop cell Refuge descriptor layers crock Class of rocky soil cthickness Diameter of the maximum circle within the closest forest cexcen Ellipse eccentricity contained within the closest forest carea Closest forest area
2.5.
Disturbance elements accessibility layers
These layers quantify the accessibility to elements of clearly negative incidence for the presence of the brown bear, such as human settlements or roadway infrastructures. These elements have been extracted from the topographic map with a scale of 1:5000 and converted to a grid with a 50-m cell size. The costdistance command of Arc-Info (ESRI, 1992) was used to calculate the cost to cross from one cell a to another b (costd_ab), using slope as cost units. Accessibility to disturbance elements is constructed by calculating the least accumulated cost for each cell to the disturbance element cells. The lesser distance is measured not in geographical units, but in slope units. Thus, itineraries with the same distance, but greater slope are considered to be further away. This type of analysis was performed for each 50-m pixel for the entire study area. Three accessibility variables have been calculated: to the closest roadway, pathway, and human settlement. Using this accessibility matrix, we can locate the closest disturbance element for each cell of terrain. Features of this closest cell can be extracted, in order to create other derived variables: 1) density of buildings in the closest constructed cell (ahouse) and 2) type of infrastructure (motorway, national and district roads…) of the cell (aroad), an indicator of the degree of impact of the cell with the closest infrastructures. The built-up density has been created from a map of points where each point represents a building and carries an attribute of the occupied area. Using this data, the density for each cell of the grid can be calculated.
2.6.
Refuge distance layers
These layers quantify the distance to elements which a priori would seem to be positive for the presence of bears or to resting sites such as rocky outcrops and forests. Based upon the existent vegetation and rocky outcrops maps (SIAPA), the Euclidean distance has been calculated from each cell to the forest (dforest) and to the nearest rocky outcrop (drock), without factoring in slope as in this case it does not imply difficulty of access (the bear is capable of crossing extremely strong slopes). Furthermore, other distances to refuge-related places such as the Euclidean distance from each cell to the limestone, quartzite, or mixed outcrop or to the nearest thicket, have been calculated for the location of critical areas; however, they have turned out to be statistically inadequate and consequently, have not been integrated into the estimate.
2.7.
Refuge descriptors layers
These layers describe certain features of the closest rocky outcrops and forests. crock describes how abrupt the rocky layers are. Given that the forest in the area was highly fragmented (García et al., 2005), which represents a negative factor for the species (Naves and García, 1996), other variables have been used to measure the shape and size of the nearest forest: cthickness measured the diameter of the largest sized circle contained within the closest forest; cexcen, the eccentricity of the ellipse which the closest forest contains; carea, the area of the closest forest, and cperimeter the perimeter of the closest forest. GIS makes it relatively easy for us to obtain the values of different variables for each cell into which the territory has been divided. We have used the values of cells corresponding to the locations and to the random points taken into consideration for the statistical analysis.
2.8.
Statistical analysis
Using the statistical software package SPSS v14 and the multiple logistic regression technique, we have related the presence/absence of the brown bear (dependent variable) to values corresponding to the previously described thematic layers (independent variables). Multiple logistic regression is a commonly used multivariant analysis technique to create habitat prediction models for different species (Fielding and Haworth, 1995; Manel et al., 1999; Karl et al., 2000; Pearce and Ferrier, 2000; Bailey et al., 2002; Luck, 2002). This technique facilitates distribution by means of the probability function that obtains a binary variable on the basis of non-normal independent or even qualitative variables. Logistic regression requires that the data and variables to be used meet a series of criteria: 1) the sample must be random; which has already been commented on, 2) there must be a monotonous relation between the different variables that is supported by the data as in Fig. 2, in which the proportion of data in each variable is depicted depending on the absence/ presence of shelter areas and where no behaviour is observed that would suggest any lack of monotony between each
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Fig. 2 – The proportion of data in each variable is not indicative of lack of monotony between said variables and the proportion of presence.
variable and the proportion of presence, and 3) mutual noncollinearity of the variables, which is corroborated by the FIV collinearity statistic(Table 2). The variables to be incorporated into the model were selected using the ‘forward stepwise conditional’ criterion. Hence and in order to assure the robustness of the model, 100 repeat Montecarlo samplings of different sizes were carried out (50, 70, 80, 90, and 95%) using the sub-sampling technique.
As a result, the following variables were found to be the most significant in relation to the number of times they were selected: tvarDEM (100%), tslope (99%), drock (99%), crock (97%), tDEM (96%), and dforest (73%). The remaining variables were
Table 3 – Coefficients and statistics for the regression function developed Coefficients Coefficient Standard Wald Significance and regression value deviation statistics
Table 2 – Collinearity statistics Statistics of collinearity Non-categorical variables tDEM tslope dforest drock tvarDEM
Tolerance
VIF
.219 .302 .099 −.153 .127
5.222 7.370 1.351 − 2.208 2.625
b0-constant b1-tDEM b3-tslope b4-tvarDEM b7-dforest b8-drock b9-crock Nagelkerke R2 Cox and Snell R2
−11.138 0.004 0.076 −0.003 0.781 0.544 −1.722 0.781 0.544
3.012 0.001 0.027 0.027 0.001 0.001 0.404
13.7 28.8 16.0 8.0 9.3 15.9 18.1
0.000 0.000 0.000 0.005 0.002 0.000 0.000
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Fig. 3 – Shelter areas for denning brown bears in Asturias, obtained by the logistic regression model. selected fewer than 35% of the times and hence, were eliminated from the analysis.
3.
Results
3.1.
The model
The model is defined by the following expression: FðxÞ ¼ b1 tDEM þ b2 tslope b3 dforest b4 drock b5 crock þ b6 tvarDEM þ b0 where b1… b6 represent the coefficients obtained for each independent variable and b0 is a constant (Table 3). A model was constructed based on this function (Fig. 3). This model correctly predicts 94.9% of the absences and 87.0% of the presences, for a threshold of 0.5. The entire adjustment of the model is specified by Nagelkerke (0.78) and Cox–Snell (0.54) statistics.
3.2.
Selection threshold
The function of the logistic regression developed gives us a probability value for consideration as being a critical area for each 2500-m2 cell of the territory which varies between 0 and 1. It is the decision of the expert to decide what value within this range will be useful as the threshold to distinguish the selected zones (considered as critical areas for bears) from the non-selected ones (considered as non-critical). Most of the papers use 0.5 as a cut-off (Tabacknick and Fidell, 1996; Manel et al., 1999; Pearce and Ferrier, 2000; Luck, 2002), However, this cut point will decrease sensitivity and increase specificity (Jiménez-Valverde and Lobo, 2007). The choice of threshold (Fielding and Bell, 1997; Fielding, 2002; Pearson et al., 2004) must be accompanied by a decision based on a more in-depth study of the effect the threshold variation has on the precision of the model by means of ROC graphics (relative operating characteristics) (Metz, 1978). This is a curve which is created using pairs of sensitivity (true positive fraction, y-axis) and specificity (false positive fraction, x-axis) values calculated at different cut-offs (Fig. 4).
The percentage of sites correctly predicted by the territory model to be occupied or unoccupied varies with a change in the probability threshold value (Table 4). As a result of this, we found that with a 0.5 probability value, the model correctly predicted between 88–97% of the positive sites and between 92–94% of the negative sites. Correct positive predictions and sensitivity increase as the threshold value decreases, while the correct negative predictions and specificity increase as the cut-off value increases. By having more absences than presences makes the model tend to classify absences better. A sensitivity/specificity analysis can provide a cut-off that offsets this bias. The ROC curve (Fig. 4) displays that a 0.8 cut-off point corresponds to an approximate sensitivity of 75% and a specificity of 2%, whereas sensitivity increases up to 88% and specificity rises to 3% with the data that had been reserved for validation. Furthermore, the practical objective for which the model was created imposes additional responsibility. The political and legal application of the results on the layout of the territory forces us to increase the chosen threshold to a much more restrictive level, in an attempt to achieve high specificity.
Fig. 4 – ROC curve.
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Table 4 – Statistics of predicted values for a different threshold of probability Threshold probability
True presences correctly predicted (%)
True absences correctly predicted (%)
Validation dataset
Model dataset
Validation dataset
Model dataset
Validation dataset
Model dataset
Validation dataset
98 97 96 92 88 87 84 75 58
100 100 97 97 97 94 91 88 76
77 84 86 91 92 94 96 98 99
84 85 90 92 94 98 98 98 100
2 3 4 8 12 13 16 25 42
0 0 3 3 3 6 9 12 24
24 17 14 10 8 6 5 2 2
18 16 11 10 8 3 3 3 2
Therefore, insofar as the results at different levels are concerned (Table 4, Fig. 4), we have selected a 0.8 probability value as the decision threshold for this paper. This high restriction level sacrifices the consideration of critical areas for those zones where uncertainty is greater and lowers the extension of these areas from 727 to 378 km2. The limit that regional laws impose upon these zones makes it necessary to increase security levels when determining the cut-off.
Testing the model
To assess the degree of final discrimination of the model between the zones with the presence or absence of areas that are critical for the brown bear, four indices have been calculated (Lindenmayer et al., 1990; Pearce et al., 1994; Pearce and Ferrier, 2000; Luck, 2002): the true positive fraction or sensitivity, as the percentage of true presences correctly identified; the true negative fraction, as the percentage of true absences correctly identified; the false positive fraction or specificity, as the percentage of true presences incorrectly identified and the false negative fraction, as the percentage of true absences incorrectly identified. These indices have been applied to the datasets used in developing the model and to the sets of data reserved for validation (Table 5).
Table 5 – Hosmer–Lemeshow test Decile
Absent
Present
Total
Observed Estimated Observed Estimated 1 2 3 4 5 6 7 8 9 10
50 50 50 50 49 49 37 18 0 2
CHISQUARE Results
True absences incorrectly predicted (%)
Model data set 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
3.3.
True presences incorrectly predicted (%)
10.271
50 50 49.99 49.95 49.70 48.12 39.14 13.82 3.48 .76
0 0 0 0 1 1 13 32 50 51
DEGREES OF FREEDOM 8
.00 .00 .00 .04 .29 1.87 10.85 36.17 46.51 52.23
50 50 50 50 50 50 50 50 50 53
SIGNIFICANCE .247
From these indices, one can obtain a means of accuracy such as the general percentage of places correctly predicted, which in this case was 86.5%, for the data used in developing the model, and 93% for the data reserved for validation (Table 4). The ROC graph (Fig. 4) gives us a new means by which to quantify the function's discriminant capacity, independently of the threshold value chosen and which is given as the area under the ROC curve (Fielding and Bell, 1997; Pearce and Ferrier, 2000). This curve was created with 504 pairs and yields a 94% discrimination value. Another measurement of goodness-of-fit has been obtained by means of the Hosmer–Lemeshow test (Hosmer and Lemeshow, 1989), which basically consists of dividing the probability coverage into “deciles-of-risk” and then calculates the distribution of both the presence and absence of bears predicted by the equation and the true, observed values. Both the expected and observed distributions are contrasted by means of a chi-square test. The results obtained (Table 5) reveal that the test was not significant (p = 0.247 with 8 degrees of freedom).
4.
Discussion and conclusions
The method described herein has enabled a detailed map of potential shelter areas for the brown bear to be created of their core area in Spain. This has been possible thanks to the geographic accuracy and the high quality of the initial data, as well as the detailed management of this data in a 50 m-cell grid. The model maps out the zones where bear currently exist, as well as others that possess adequate conditions for bear and has pointed out that some areas have not been prospected and that it is possible that other bear dens exist in those areas. The shelter areas appear to overlap in the bear range, especially in the reproductive areas (Naves et al., 2003; Palomero et al., 2006), which may have to do with the use the females make of winter dens to give birth to their cubs. The Western bear population has an important amount of potential shelter areas that largely coincide with their present distribution. As for the zones that are east of the present range area, the model reveals that they are not likely to have denning sites, which is a handicap for the geographical spread of bears under present conditions, particularly towards the space that separates them from the Eastern population over some 30 km in length in a straight line (Fig. 3).
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On the contrary, the high value of shelter in the study area to the north of the present Eastern population reveals a large area of potential distribution of the species (Fig. 3) and suggests that this does not currently constitute a limiting factor to the possibility of this population's expansion. The natural habitat is also calculated to be of good quality according to different methods (Marquínez et al., 1997; Naves et al., 2003). The problems for this population arise from their lower birth rate (the mean litter size was consistently lower among the Eastern population than the Western one: 1.66 vs. 2.24, respectively, in the 1980s (Palomero et al., 1993) and 1.45 vs. 1.8 in the 1990s (Palomero et al., 2006) and from the scant number of individuals (fewer than three reproductive females were detected per year in the entire population; Palomero et al., 2006), which could render them genetically non-viable if the connection between the two subpopulations is not re-established (Wiegand et al., 1998; Rey et al., 2000). The Cantabrian bear population split into two nuclei during the first half of the twentieth century (Nores and Naves, 1993; Clevenger et al., 1999). This rupture has been interpreted as the consequence of the development of communication infrastructures and recent human pressure (Nores and Naves, 1993; Clevenger and Purroy, 1991; Palomero et al., 1993; Wiegand et al., 1998; Naves et al., 2003). The habitat quality models developed for the area (Marquínez et al., 1997; Naves et al., 2003) do not reveal any loss of quality in food availability or plant coverage eastwards of the main infrastructures, but they do for shelter (dens) in a large area several kilometres to the east. The model points to this space without bears as an area with poor bear den potential. The model predominantly selects variables that are related to the refuge potential provided by the terrain's topographic conditions or its lithology, which we can consider the territory's prime nature. Of the five variables selected, three are topographic variables (mde, varmde, and slope) and two are refugerelated (eubq and euroca). Only the distance to the forest is not dependent on the territory's primary nature and could easily be modified to increase refuge areas. This result is consistent with the one in Slovenia obtained by Petram et al. (2004), who found that the type of terrain is the most important variable affecting the selection of winter dens. The most widely used dens are those that are least accessible to humans. The poor natural conditions for the establishment of a shelter area in the area that separates both nuclei appear to represent an important impediment to bear passage connecting both populations. On the one hand, the females are more philopatric than the males and they need dens in which to give birth and, secondly, the concentrations of reproductive females attract the males to their surroundings, especially during heat (Palomero et al., 2006). This behaviour might explain why the gap between both populations' dens is an area where bear presence is scarce. Males are responsible for dispersion and communication between areas. However, if the core areas, where females and dominant males are more abundant, are far apart from each other, the possibility of the males, even peripheral young males, (Swenson et al., 1998; Jerina et al., 2003) spanning the distance between both populations also decreases. This may have been the cause for the genetic differentiation between both Cantabrian nuclei (García-Garitagoitia et al., 2006). One must remember that the gap between bear populations coincides with a siliciclastic lithology corresponding to
the Central Carboniferous Basin (Julivert, 1971), where abrupt rocky outcrops are scarce, since winter dens in the Cantabrian Mountain Range, as in the Pyrenees, are preferably located in natural caves or, after that, in digging dens made by the animals themselves, but not at the base of trees or hollow trunks, as seen in other European populations (Naves and Palomero, 1993a). The same lithological constraints could similarly affect population splits in other mountain species, such as chamois (Rupicapra pyrenaica) (Pérez-Barbería et al., 1996) and, to a lesser extent, capercaillie (Tetrao urogallus) (Obeso, 2003). Even though the rupture in these populations might also be accounted for by human disturbances, lithology appears to be a better explanation for the decrease in the potentiality of the range of medium-size and small mammals in this same area, such as pine marten (Martes martes), snow vole (Chionomys nivalis), and edible dormouse (Glis glis) (Palomo and Gisbert, 2002, pp. 486–488). Smaller species are not as affected by infrastructures and general human avoidance as are large vertebrates. In any case, thus far, human disturbance was the only justification to explain the origin of the fracture in the populations and the non-permeability for bears, but this paper reveals the lack of shelters as an unsuitable habitat factor that increases the isolation between both nuclei. If we add the fragmentation in the landscape (Obeso and García, 1990) associated with farming, then the reason for the gap in the mountain corridor is better explained. The preservation of corridors between populations is essential to minimizing genetic isolation (Harrison, 1992; Beier, 1993). In the case of the Cantabrian bear population, the reestablishment of a corridor between the two nuclei is critical, given the scant number of bears in both nuclei and the low genetic variability detected (García-Garitagoitia et al., 2006). The results afforded by the model reveal how the present gap in the bear distribution area has poor shelter capacity and this may represent the major impediment to re-uniting the two populations. This new perspective can have an impact on management plans for the Cantabrian brown bear. Given that the Western population has sufficient population density (Wiegand et al., 1998) and the natural habitat in the corridor is of good quality (Naves et al., 2003), shelter capacity would be an issue of supreme importance in explaining the current difficulties involved in connecting both nuclei and achieving communication between them. In light of the variables considered in the analysis, actions in the passage should focus on decreasing forest fragmentation, which would have a positive impact on refuge capacity. Other variables such as those related to relief or rocky outcrops are practically impossible to improve, as they are intrinsic to the terrain, albeit it would be useful to facilitate the settling of females in the closer refuge areas, which would be conducive to getting the males to pass from one nucleus to another in order to achieve the genetic exchange between both populations. Insofar as variables related to human settlements and communication infrastructures are concerned, it is unlikely that they will be improved, given that they affect social and economical interests. Therefore, actions should focus on decreasing the barrier effect of transportation infrastructures and buildings, securing sufficient and efficient crossing points, recovering the natural vegetation cover, connecting closer shelter areas following sub-optimal shelter area (comprised over the probability threshold 0.5) in the
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gap, avoiding the expansion of infrastructures and diverting future urbanization developments (of lesser importance in the past) towards other areas where the presence of bears is improbable or that have no value as corridors, as proposed by other studies (Churszcz et al., 2003; Kaczensky et al., 2003).
Acknowledgments We wish to acknowledge the Government of Asturias for providing data relating to known shelters and lairs and for the interest shown in applying these results to conservation policies for the Cantabrian bear population. We also wish to thank G. Palomero for his data and ideas for this paper, and A. Colubi and G. Plantamura for their valuable contribution in revising this manuscript.
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