Determining how animal populations relate to their environment is the fundamental basis for understanding how population processes are affected by changes in landscape characteristics (Kareiva & Wennergren 1995). Current declines in biodiversity, widely attributed to habitat loss and fragmentation, have motivated ecologists to improve predictions regarding the responses of wildlife populations to changes in the amount and configurations of available habitat (Wiens 1996; Balmford, Green & Jenkins 2003). This requires identification of key environmental features that are directly linked to species’ long-term probabilities of persistence in a landscape after a fragmentation event (Franklin & Noon 2002). Research on this topic has developed from a focus on metrics related to habitat patches (i.e. patch size, isolation) to a landscape-level approach (i.e. connectivity, % habitat cover), establishing a broad base of knowledge on how landscape characteristics can drive the dynamics of species’ distributions at different scales (Haila 2002; Laurance 2008). Several decades of research have mainly focused on how the long-term persistence of a species in a given habitat patch is related to patch size and isolation (Andren 1994; Hanski 1999; Driscoll 2007).
There is no clear consensus on the roles that patch size and isolation play on the distribution of species in fragmented landscapes (Bender, Contreras & Fahrig 1998; Debinski & Holt 2000; Ewers & Didham 2006). A recent meta-analysis, using multiple taxa across different continents, concluded that patch size and isolation are ‘poor predictors’ of which species are likely to persist in a habitat patch (Prugh et al. 2008). Several reasons for the inconsistent predictive abilities of patch-related measures were tested. The composition of the inter-patch habitat matrix was found to be the strongest predictor of species’ sensitivity to patch size and isolation. These results are consistent with mounting theoretical and empirical evidence suggesting that a significant number of species in heterogeneous landscapes might occupy, opportunistically use and even persist in the inter-patch habitat matrix (Vandermeer & Carvajal 2001; Bender & Fahrig 2005; Driscoll 2005; Debinski 2006; Revilla & Wiegand 2008). For these species, patch-related measures are highly unlikely to reflect sensitivity to habitat loss and fragmentation given that their habitat use is not restricted to a single ‘habitat patch’. To date, most research addressing the effects of matrix habitat use has focused on species richness, or the presence or absence of a single or a subset of ‘habitat specialist’ species, for both focal and matrix habitats (i.e.Wethered & Lawes 2003; Antongiovanni & Metzger 2005).
To improve our understanding of how populations respond to changes in their environment, it is necessary to estimate how species dynamically use focal habitat together with the surrounding non-habitat matrix. This information is crucial for conservation and wildlife management (Blaum & Wichmann 2007; Fahrig 2007; Franklin & Lindenmayer 2009), especially in regions where it is more feasible to improve the quality of the matrix than to increase the proportion of focal habitat (Rudel 2006; Perfecto & Vandermeer 2008).
The impact of changes in the amount and configuration of available habitat are traditionally modelled by examining the probability that a habitat patch is occupied using two main approaches: the colonization-extinction (CE) approach widely used in metapopulation biology (Hanski 1992, 1999) and the birth–immigration–death–emigration (BIDE) approach, mainly applied in landscape ecology (Fahrig 2002). For the CE model, patch occupancy is dependent on population-level colonization and extinction dynamics. As habitat area decreases, colonization of unoccupied habitat patches decreases, and is assumed to be a function of the size and number of near-by occupied patches (Hanski 1999). This approach assumes that only focal habitat patches are occupied and matrix habitats are used for dispersal, although recent work on matrix effects applying the CE framework have incorporated ease of movement through different matrix types (Hein et al. 2004; Ovaskainen 2004). In contrast, the BIDE model predicts that as habitat area deceases, colonization of the matrix by individuals increases, where mortality is assumed to be higher (Fahrig 2002). The result is an overall reduction in population size, decreasing immigration into and increasing emigration rates out of habitat patches (Fahrig 2002). Although the BIDE framework addresses matrix effects at an individual-level through assumed mortality in matrix habitats (similarly to the CE approach), it has yet to jointly incorporate occupancy dynamics of both focal and matrix habitats.
These approaches are currently applied to make general predictions regarding the probability that a sample unit (patch) is occupied by an individual or species (i.e. occupancy) and the related dynamics: colonization (i.e. probability that a sample unit is occupied given it was unoccupied in the previous sampling period) and extinction (the probability that a sample unit is unoccupied given that it was occupied in the previous sampling period) (Hanski 1992; MacKenzie et al. 2006). However, most of the predictions based on occupancy dynamics in fragmented landscapes under the BIDE and CE models assume that an individual or species, respectively, was absent when not observed in a habitat patch (Moilanen 2002; MacKenzie 2006). Detectability of animals is less than perfect and can vary by habitat type: species can be present in a habitat patch, but not detected (Williams, Nichols & Conroy 2002; Mackenzie & Royle 2005). This separation of true occupancy from apparent absences is important in making predictions about the occupancy dynamics of contrasting habitat types; differences in occupancy rates between focal habitat patches and the surrounding matrix could be masked or biased by habitat-dependent differences in detectability (Boulinier et al. 1998; Moilanen 2002; Driscoll 2007). Current methodology in occupancy modelling accounts for the likelihood of false absences by incorporating detection probabilities, generating more accurate predictions with regards to the true state of occupancy and related dynamics (Royle & Link 2006; Nichols et al. 2008). Although much work on occupancy dynamics accounts for detectability (i.e. Boulinier et al. 2001; Hames et al. 2001; Ferraz et al. 2007; Radford & Bennett 2007; Francois, Alexandre & Julliard 2008), occupancy dynamics between habitat types is not contrasted specifically.
In this paper, we quantify differences in species’ use of both focal and matrix habitat types by estimating probabilities of occupancy, colonization and detection in forest and intervening NF habitats, for a community of bird species in southwestern Costa Rica. We developed a multi-species hierarchical community model that estimates species and habitat-specific occupancy, colonization and detection. We tested a priori expectations on how occupancy and colonization of forest and non-forest habitats should vary among pre-determined categories of forest dependency (Stiles 1985). The framework we present can be applied to estimate species-specific and community level use of multiple habitat types in a given region. The results from the model can help define which species are largely restricted to a focal habitat type of interest, meeting the assumptions of both CE and BIDE approaches, and are thus most likely to be adversely affected by habitat loss and fragmentation.