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The internal dynamic processes of local populations are affected by the quality of resources in the area that they occupy (Caughley et al. 1988; Pulliam 1988). When local populations function within a population system, however, their characteristics will also be influenced by interactions with other populations (Andrewartha & Birch 1954; den Boer 1968; Pulliam 1988). As in any population system, connectivity is of fundamental importance to the functioning of pest systems, but the need to consider connectivity has at times been overlooked in management programmes. An isolated pest population that experiences an increase in mortality over natality (e.g. because of local control procedures) may be driven to extinction because it is unlikely to be buffered by a rescue effect (Brown & Kodric-Brown 1977). Conversely, highly connected pest populations are likely to receive substantial numbers of immigrants. Extinction under a local control regime is improbable, and even if a local population is driven to extinction the patch is liable to be quickly recolonized. However, highly connected populations may be vulnerable to broad-scale control measures such as the targeted introduction and transmission of diseases designed to increase mortality, or act as vectors for immunocontraceptive agents. The dynamics of pest population systems are thus critically influenced by long-term patterns of dispersal. Understanding the factors that influence connectivity is vital for the pest's effective management, and there is a need for simple and flexible tools to allow managers to assess connectivity.
Several studies have examined the effects of habitat heterogeneity on connectivity in population systems. They have included examinations of the effects of variation in patch quality (Fleishman et al. 2002; Bonte et al. 2003), matrix composition (Ricketts 2001) and corridor effectiveness (Aars & Ims 2000; Mech & Hallett 2001). These studies have been predominantly set within a metapopulation paradigm, in which small habitat patches are encompassed by a broad region of unsuitable habitat. This approach has proved highly effective in many studies in which the matrix is a far larger component of the landscape than habitat and habitat patches tend to be relatively isolated. However some species, notably pest species, may have the capacity to utilize much of the landscape in which they live. In these systems, interactions among spatially structured local populations occur within a landscape in which resources are spread relatively continuously across large regions, albeit with variation in resource quality. Note that we distinguish here between the relatively continuous distribution of resources on the landscape, and the natural groupings of organisms that form populations that utilize the resources. While much of the landscape could be utilized, a real landscape is also likely to contain structural features that impede free movement of individuals.
In semi-arid regions of Australia the wild rabbit Oryctolagus cuniculus L. provides an example of a species living in such a heterogeneous landscape. Whilst most of the landscape here can support rabbit populations, they cannot establish warrens in forest, and forests also impede dispersal (Stodart & Parer 1988; Myers et al. 1994). Thus while a landscape consists of both suitable and unsuitable patches, many of the suitable patches will be contiguous. Analysing connectivity in such a system using traditional models is therefore difficult.
The aim of this present study was to develop a simple habitat heterogeneity model in order to investigate the hypothesis that connectivity within a broadly spread pest population system can be influenced by the spatial configuration of contiguous suitable patches interspersed with unsuitable patches. The utility of the model was assessed by applying it to a case study of a wild rabbit population system in Australia. The model was validated with population genetic data, which is appropriate because the level of population genetic structuring within a system will be influenced largely by the level of interaction among its constituent populations through time (Wright 1951; Slatkin 1987). The insights gained from this study should not only enable more efficient management of wild rabbits in Australia, but of many other pests that rely on resource patches that are broadly spread across a region.
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The results presented here show that connectivity among local populations in a spatially structured pest system can be influenced by habitat heterogeneity. Genetic distance among wild rabbit populations in the Mitchell region increased as inferred levels of interaction based on pairwise connectivity decreased, and, as predicted, populations that exhibited very low connectivity levels were also genetically isolated within the system. This study provides evidence that interactions among local wild rabbit populations in this region were strongly influenced by the spatial distribution of soil quality and forests.
It is important to note that while the distribution of forests strongly isolated two local populations in the system (Polworth and Verniew), the habitat heterogeneity model also explained genetic structuring amongst sites in more continuous habitat when the isolated sites were omitted from analysis. Therefore, the spatial distribution of both suitable and unsuitable patches explains connectivity in this system. For example, Glenlea showed a significant genetic difference to Glenalba and forest was only sparsely distributed between these two sites. However, there was a sequence of intermediate and poor soil patches between these sites that limited the connectivity between the local populations occupying them.
Although geographical distance is often used as an indicator of connectivity within population systems, it does not appear to be a major factor affecting connectivity among the populations studied here. Some populations that are widely separated (e.g. Claravale and Glenalba; Fig. 1) show much higher interaction (Table 1) than adjacent populations (e.g. Claravale and Verniew). This is consistent with the analysis of Wilson, Fuller & Mather (2002), who found that genetic structuring within this system could not be explained by a distance model. Interestingly, Wilson, Fuller & Mather (2002) also found that the Maranoa river, which bisects the study site, did not influence structuring, because rabbits are capable of swimming and the river flow rates are insufficient to restrict rabbit movement. The finding that connectivity in this system is mainly driven by habitat heterogeneity has substantial implications for the management of pest population systems.
Connectivity among local pest populations will influence probabilities of extinction, recolonization and rescue effects (Brown & Kodric-Brown 1977). To be useful as a management tool, a model should identify patterns of connectivity within a system and the critical factors that influence those patterns. In the Mitchell region it is important to know that a sequence of high-quality soil patches between wild rabbit populations will allow high connectivity, even when the populations are separated by tens of kilometres. For instance, attempting to decrease the density of rabbits in one of the highly connected populations (e.g. Bowann) by a local control measure such as baiting may be a poor allocation of resources because, even if an extinction occurred, the site would be rapidly recolonized from within the highly connected group. Conversely, release of a disease such as the Rabbit Calicivirus Disease (RCD) at a highly connected site may be efficient because it is likely to lead to transmission to the other highly connected sites. Transmission to isolated sites (Polworth and Verniew) is much less probable, where local control measures may be suitable. It is also noteworthy that retaining forests may be an important component of a regional rabbit control strategy, as large tracts of forest strongly limit connectivity. These recommendations must be tempered by the recognition that changes in population structure may occur on a temporal scale longer than that of management strategies, and that the efficacy of lethal management techniques will be strongly influenced by the absolute frequency of recolonization events. The frequency of recolonization events should therefore also be considered within an effective management strategy.
While it is possible to construct a model with many parameters in order to emulate closely one species in a particular region, there are significant advantages to constructing a model that captures the major biological processes in a population system without excessive complexity. A simple model maintains generality, allowing it to applied to a variety of systems with minor modifications. As there are few parameters, these parameters can be estimated from empirical data. Importantly, it may be possible to confirm the assumptions on which the model is based by validation with appropriate independent data, which is often difficult for complex models (Kareiva 1990). Indeed, the appropriate validation of an ecological model with a large, high-quality and independent data set, as has been done here, is rare.
The habitat heterogeneity model developed above is not, however, presented as a comprehensive population dynamic model. The model is both simple and deterministic, which allowed the few parameters to be drawn from empirical studies and the model to be clearly validated with independent data. While a simple model was appropriate to investigate the link between habitat heterogeneity and connectivity in this study, further refinements to the model could be considered. Stochasticity is an important component of natural processes and incorporating natural variation into the model, at the population and environmental levels, would undoubtedly provide further insights into connectivity patterns in pest population systems. It may also be fruitful to vary the form of certain parameters in a biologically meaningful way, for instance by varying dispersal according to population density or introducing density dependence. Implementing random walks among populations, rather than linear dispersal paths as used in the current study, is also likely to be useful. One particularly interesting extension would be to consider the effect of any biases in dispersal between males and females. Sex-biased dispersal is a characteristic of many vertebrates and one that is likely to influence connectivity patterns and thus management plans. Simulations are currently being undertaken to determine any effects on connectivity of sex-biased dispersal in rabbits.
Alternative methods such as least cost dispersal (LCD) modelling have been used to assess connectivity in metapopulations (Vos et al. 2001; Schweiger, Frenzel & Durka 2004) and are generally implemented using a geographic information system (GIS). While useful in systems in which a matrix comprises the majority of the landscape, LCD is unlikely to provide any advantage over the current method in systems where it is not necessary to consider explicitly movement cost on most patches. Such models are usually difficult to parameterize (although see Kramer-Schadt et al. 2004 for parameterization of a spatially explicit model using telemetric data). Even for patches that impede dispersal in the rabbit case study (forests), implementation of a simple resistance function proved adequate in the absence of empirical data, with the additional advantages that it was clearly a model assumption, could easily be replaced by an alternative function if appropriate, and was amenable to sensitivity analysis.
We have shown here that habitat heterogeneity can influence connectivity in a pest population system. The modelling approach used in this study could be easily adapted for use in other pest systems in specific regions by identifying and mapping important resources and landscape elements that impede dispersal. It is also important to note that this approach could equally be used in a conservation setting. Resources that affect population growth will differ among target plant and animal species, whether in conservation or control settings, and identification of these critical resources will be a key factor in correct utilization of the model. For example, the European hare Lepus europaeus has been classed as a ‘priority species of conservation concern’ by the UK government, and pastures are suboptimal habitat for this species (Smith et al. 2004). Vegetation type or structure may therefore be one measure of habitat quality here. The suggestions for population control outlined in this study could easily be adapted for this species, for example to increase connectivity among local populations if appropriate.