Habitat Loss and Extinction Threshold

Loss and fragmentation of natural habitats due to human land use is the most important reason for the current catastrophically high rate of loss of biodiversity on Earth. Population viability depends, among other things, on the environmental carrying capacity and hence on the total amount of habitat available. Additionally, the spatial configuration of habitat may influence viability, because most species have limited migration ranges and hence not all habitat in a highly fragmented landscape is readily accessible. Metapopulation models have been used to address the population dynamic consequences of habitat loss and fragmentation.

In the Levins model, habitat loss has been modeled by assuming that fraction 1 — h of the habitat patches becomes unsuitable for occupancy and reproduction, while fraction h remains suitable. Habitat loss reduces the colonization rate to cp(h — p), because the model assumes that fraction 1 — h of the migrants land on unsuitable habitat and perish (the model thus most literally applies to species with longrange passive migration and no habitat selection). The species persists in a patch network if h exceeds the threshold value S set by the extinction-proneness and colonization capacity of the species. An interesting implication of this result is that, at equilibrium, the fraction of suitable but unoccupied patches (h — p*) is constant and equals the amount of habitat at the extinction threshold (h = S). This is a potentially helpful result, because it suggests a way of measuring the value of the extinction threshold given knowledge of h and p*. The model is however exceedingly simple and hence not really suitable for quantitative predictions.

The spatially realistic metapopulation models combine the metapopulation perspective of the Levins model with a description of the spatial distribution of habitat in a fragmented landscape and how the landscape structure influences the extinction and colonization processes. In the model described by eqn [5], the metapopulation capacity AM replaces the fraction of suitable patches h in the Levins model, and the threshold condition for metapopulation persistence is accordingly given by AM > S. The novelty here is that AM takes into account not only the amount of habitat in the landscape but also how the remaining habitat is distributed among the individual habitat patches and how the spatial configuration of habitat influences extinction and colonization rates and hence metapopulation viability.

The metapopulation capacity can be computed for multiple landscapes and their relative capacities to support viable metapopulations can be compared even without knowledge of the threshold value S: the greater the value of Am, the better (Figure 3). One complication is regional stochasticity, which can only be included in the calculations if there is empirical knowledge about the spatial scale and the strength of stochasticity. Without regional stochasticity, it always helps to have as short distances as possible between habitat patches. With regional stochasticity, metapopulation viability is likely to be maximized by intermediate distances between the habitat patches: long enough to reduce the correlation in local dynamics, but not too long to reduce migration too greatly.

Transient Dynamics and Extinction Debt

A change in the structure of a fragmented landscape, for instance a reduction in the area of some habitat patches, will

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