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Since the introduction of the gray squirrel Sciurus carolinensis to Britain, the species has adapted to the British landscape colonizing England, Wales, and parts of Scotland and Ireland (Pepper and Patterson 2001). The population has caused negative effects upon forestry, through damage associated with bark-stripping behavior, and native biodiversity (Kenward 1983; Gurnell and Mayle 2003; Mayle et al. 2007). In particular, the gray squirrel expansion has occurred simultaneously with the decline and replacement of native red squirrel Sciurus vulgaris populations. Interspecific competition for resources with the gray squirrel, disease, habitat loss, and fragmentation, are all contributing to the massive decline of the red squirrel within the United Kingdom (Gurnell et al. 2004). In particular, gray squirrel presence in mixed and broadleaved woodland is seen to reduce the reproductive rate and juvenile recruitment of red squirrels (Gurnell et al. 2004). Over time, this results in reduced red squirrel population size and the localized extinction of the red squirrels in that area (Gurnell et al. 2004).
It is suggested that gray squirrels have a decreased sensitivity to habitat fragmentation compared to other Sciurid species (Koprowski 2005), and are capable of crossing all, but the most extreme of land cover types (Bryce et al. 2005). Although red and gray squirrels are capable of traversing open ground (Gurnell et al. 2006), evidence suggests that dispersing Sciurid's will commonly use riparian corridors and valley bottoms as dispersal routes with tree cover being the most influencing factor (Middleton 1930; Wauters et al. 1994, 2010; Bakker and Vuren 2004; Gurnell et al. 2006).
Anecdotal evidence, presence data, and radio tracking have shown that linear landscape elements such as hedgerows, tree rows, road verges, fences, and walls are used by red and gray squirrels during interhabitat patch movements (Middleton 1930; Taylor et al. 1971; Fitzgibbon 1993; Wauters et al. 1997; C. D. Stevenson, K. Watts, O. T. Nevin, and A. D. Ramsey, unpubl. data). Gray squirrels may utilize different land cover types and landscape elements to aid movements, nevertheless there are certain landscape types which are more likely to be used. Being able to predict how these are used during gray squirrel movements will aid management efforts.
The landscape between habitat patches, the landscape matrix, is comprised of different land cover types, which may facilitate or impede species movements (Taylor et al. 1993). When faced with habitat fragmentation, the behavioral and physiological interactions with the landscape are important in determining dispersal and movements (Taylor et al. 1993; Ricketts 2001). The perceptual range of a species to detect particular landscape elements mediates decision making whilst dispersing (Zollner and Lima 2005). Where habitat patches are out of a species perceptual range, landscape elements may act as cues directing a species through the heterogeneous landscape (Pe'er and Kramer-Schadt 2008). The permeability of certain landscape features may also be associated with increased security, shelter, and a food resource (Verboom and van Apeldoorn 1990; Zollner 2000; Bakker and Vuren 2004), whereas others may be related to higher predation and mortality risk (Nixon et al. 1980; Tischendorf and Fahrig 2000). Many studies have found that certain permeable landscape features and linear elements may act as stepping stones and corridors for species movement (Nixon et al. 1980; Beier and Noss 1998; Manning et al. 2006; Bailey 2007; Davies and Pullin 2007; Gelling et al. 2007). The effects of habitat fragmentation on species movement are therefore species and landscape specific (Tischendorf and Fahrig 2000).
Many studies have used GIS least-cost modeling to assess the functional connectivity of fragmented habitat patches (Villalba et al. 1998; Ferreras 2001; Adriaensen et al. 2003; Chardon et al. 2003; Coulon et al. 2004; Driezen et al. 2007; Epps et al. 2007; Gonzales and Gergel 2007; Walker et al. 2007; LaRue and Nielsen 2008; Janin et al. 2009; Watts et al. 2010; Sawyer et al. 2011). In particular, Villalba et al. (1998), Verbeylen et al. (2003), and Gonzales and Gergel (2007) all used least-cost modeling to assess connectivity for Sciurid species. Whilst Stevenson et al. (in review) used least-cost modeling to specifically predict gray squirrel movements. During least-cost modeling, land cover types are assigned a resistance or permeability score which is based upon the facilitating or impeding effects upon species movement (Adriaensen et al. 2003). Three types of least-cost models are defined; least-cost networks (LCN), buffered least-cost path (LCP), and least-cost corridor (LCC). LCN identify functional habitat networks which include patches of habitat and a buffer of permeable surrounding landscape which could potentially be utilized for movement based upon defined permeability values and a dispersal distance (Watts et al. 2010). LCP analysis is a common type of least-cost modeling which shows a single least-cost route between a start and end point (Sawyer et al. 2011). Whereas, LCC are formed by combining multiple LCP which are buffered by the landscape resistance values at each side of the LCP. Beier et al. (2008) suggests that LCC are most appropriate for identifying connectivity as they account for alternative movement routes (Beier et al. 2008; LaRue and Nielsen 2008; Sawyer et al. 2011).
This study aims to use a combination of LCN, LCP, and LCC modeling to identify potential gray squirrel movement paths. To assess these alternative least-cost models, and also to add to the current knowledge of gray squirrel landscape movement, this study uses global positioning system (GPS) telemetry to record gray squirrel movements. Gray squirrel movements have been recorded previously by radio telemetry (see Haughland et al. 2008). Although Swihart and Nupp (1998) and Swihart et al. (2007) have investigated matrix usage by gray squirrels, to our knowledge no study has recorded high spatial and temporal resolution gray squirrel movements with GPS devices. The information gained will enable a comparison of alternative LC models and the prediction of gray squirrel movements through a fragmented landscape.
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This study has combined LCN, LCP, and LCC modeling techniques to predict gray squirrel movements within the landscape. In addition, GPS movement data were recorded and used to assess the least-cost model predictions. It also contributed to the existing knowledge of gray squirrel landscape use. Previously, recording of a dispersal path using radio telemetry has been used to successfully validate a least-cost model (Driezen et al. 2007). The same study suggested that further validation of least-cost models using different species and landscapes is needed. Previously, GPS telemetry was limited to large animals (Wauters et al. 2007; Haughland et al. 2008; See Tomkiewicz et al. 2010 for a review). However, this is the first study we know which has used GPS collars on in situ gray squirrel individuals to obtain detailed movement data within the landscape matrix.
By using a combination of radio telemetry and GPS, locations were taken for each individual and five of the nine collared gray squirrels were successfully recaptured. Due to time scales, recapture could only take place for a certain time, but gray squirrel control occurs on the study site continuously so it is anticipated that the remaining collared individuals will be recaptured. The data points collected for each squirrel were pooled and used within a chi-square analysis. Although it is suggested that the animal should be used as the test unit and that using each location point in a chi-square analysis causes pseudo replication (Aebischer et al. 1993; Kenward 2001), due to the lower anticipated recovery of individuals, and therefore small samples size using the animal as the test unit would have prejudiced the analysis.
On occasion the GPS device had difficulty in locating enough satellites for signal transfer within the dense canopy, causing a decrease in observation rate (See Rempel et al. 1995). However, the GPS data obtained have enabled assessment of gray squirrel movements within the landscape matrix and 10 interpatch movements have been highlighted. The gray squirrels within this study were translocated <1 km away from the capture site before release. Although the movements recorded with the GPS were not natal dispersal movements, the physiological and behavioral aspects of moving through different land cover types are likely to be similar. Nine out of the 10 movements were directed toward the site of capture. Only one moved in the opposite direction, but changed direction on the same day returning back to where it has started. These movements may possibly suggest a homing instinct of the squirrel back to the capture woodland/home range and show signs of landscape knowledge by the individuals.
Although the types of landscape features and land cover types used were highlighted in the results, it does not show which ones are universally preferred, just the most used within this particular landscape. The availability of land cover types and landscape features is landscape specific and use will depend upon what is available. Land cover type and feature use whilst moving between habitat patches, were ranked based upon the number of points in each compared to the availability of each. Habitat edge is ranked last because during a movement in fragmented landscapes, individuals have to move away from habitat into the landscape matrix. River corridor is ranked first followed by road/road verge then track/path. It has previously been suggested that gray squirrels use landscape features while dispersing (Middleton 1930; Taylor et al. 1971; Fitzgibbon 1993; Bryce et al. 2005). Field edge is ranked second to last most likely because these will be used if other features are not available. By recording the distance to the nearest landscape feature and nearest habitat, the GPS points within this study were found to be significantly closer to habitat patches and landscape features.
The further the individual moves into the landscape matrix and away from habitat, the more susceptible it is to predation and increased energetic costs (Bakker and Vuren 2004). Individuals would be able to perceive a woodland patch if they were within 300 m (Zollner 2000), however, individuals are seen to use landscape features most probably to reduce their risk of predation. This avoidance of open areas behavior has been seen in previous studies (Nixon et al. 1980; Bakker and Vuren 2004) and may have been a consequence of the perceptual range of the individual to detect habitat and predation risk. As the individuals move further from woodland and cannot detect woodland patches in the matrix landscape, features will be used as guidance (Pe'er and Kramer-Schadt 2008). This study reiterates the importance of landscape feature use in gray squirrel movements and shows that features are used, although in doing so the distance traveled is longer.
When the Euclidean distance was compared with the actual movement distance, individuals were seen not to take the straightest distance between two woodland patches. Movements were significantly longer and included the use of landscape features. Chardon et al. (2003) and Verbeylen et al. (2003) both suggested that presence and absence data were better explained by a least-cost model than the Euclidean distance. Coulon et al. (2004) and Driezen et al. (2007) showed that genetic distance and radio telemetry data, respectively, also validated least-cost paths. Within this study significantly more GPS movement points were within the buffered paths and corridors than expected by chance. The results indicate that the least-cost modeling approach not only predicts movements better than the Euclidean distance but it also is able to successfully predict gray squirrel dispersal within the landscape matrix.
The GPS data were used to validate the LCN, buffered LCP, and LCC created with the OSMM. The scale and quality of the base maps used within least-cost modeling has an impact on the model outputs (Adriaensen et al. 2003; Sawyer et al. 2011). It is essential that the accuracy of the map is considered and all landscape elements which are important to the dispersal of the study species are included within the base map at an appropriate scale (Villalba et al. 1998; Verbeylen et al. 2003). If they are not included, extra digitization is required (Schadt et al. 2002; Adriaensen et al. 2003; Verbeylen et al. 2003). Hedgerows, walls, and fences, which are classed as field edges in this study, are seen to be important to gray squirrel dispersal (Middleton 1930; Taylor et al. 1971; Fitzgibbon 1993; Bryce et al. 2005), and therefore it was important to add these additional features and missing habitat to the base map.
Each of the least-cost modeling techniques used within this study provide information on the functional connectivity of gray squirrel habitat within the landscape. By using a combination of LCN, buffered LCP, and LCC modeling, an apparent progression can be seen. On the larger spatial scale, the networks identify areas of the landscape in which a species is able to disperse. This can cover substantial areas and includes all areas not just the most probable routes. To predict the most probable routes, a gray squirrel would move, the next step is to use multiple buffered LCP, LCC, or both, to gain fine-scale movements within networks. Buffered LCP's are relatively quick to produce and were assessed using GPS movement data. Although the test statistic produced the same values for LCP and LCC, LCC does accommodate variation in widths which is biologically more realistic.
Based upon previous literature and expert knowledge, Gurnell et al. (2006) used a spatial explicit population model to highlight gray squirrel incursion routes into Kielder forest, a red squirrel reserve. The model used by Gurnell et al. (2006) suggested that dispersal into the forest occurred through the use of narrow river valleys with hedgerows and woodland patches (Gurnell et al. 2006). This study has shown that least-cost modeling is also capable of predicting gray squirrel movements in the landscape. The next step will be to use least-cost modeling to identify the most probable gray squirrel movement routes in areas where red squirrel conservation occurs. This will enable gray squirrel control to be targeted in specific areas aiding their management. By using a combination of LCN, buffered LCP, and LCC modeling to assess the functional connectivity of habitat patches for the gray squirrel, potential dispersal routes have been identified.
This is the first study to use GPS telemetry on gray squirrel. Although it is acknowledged that a small number of individuals were collared, it has shown that this technique is successful in gaining information on movement to enable least-cost model validation. The techniques used within this study can be applied to different species and landscapes in addition to other conservation and management strategies. Potentially, these techniques can be used to aid red squirrel conservation and gray squirrel management by highlighting potential movement routes through the landscape.