How accurate are population models? Lessons from landscape‐scale tests in a fragmented system

Abstract

There is a growing debate about the ability of Population Viability Analysis (PVA) to predict the risk of extinction. Previously, the debate has focused largely on models where spatial variation and species movement are ignored. We present a synthesis of the key results for an array of different species for which detailed tests of the accuracy of PVA models were completed. These models included spatial variation in habitat quality and the movement of individuals across a landscape. The models were good approximations for some species, but poor for others. Predictive ability was limited by complex processes typically overlooked in spatial population models, these being interactions between landscape structure and life history attributes. Accuracy of models could not be determined a priori, although model tests indicated how they might be improved. Importantly, model predictions were poor for some species that are among the beststudied vertebrates in Australia. This indicated that although the availability of good life history data is a key part of PVA other factors also influence model accuracy. We were also able to draw broad conclusions about the sorts of populations and life history characteristics where model predictions are likely to be less accurate. Predictions of extinction risk are often essential for real-world population management. Therefore, we believe that although PVA has been shown to be less than perfect, it remains a useful tool particularly in the absence of alternative approaches. Hence, tests of PVA models should be motivated by the cycle of testing and improvement.

Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics

“…One issue that is beginning to receive some attention is the degree of realism required in the assumptions of the dispersal process to be able to capture the spatial dynamics of a system (Conroy et al, 1995 ;Moilanen & Hanski, 1998;Moilanen & Nieminen, 2002;Lindenmayer et al, 2003). The level of complexity incorporated will vary according to the aims of the model.…”

“…However, as the improvement was only small, the extra detail was concluded to be unimportant (Moilanen & Hanski, 1998). In another study, the ability of models with simple assumptions, such as simplistic dispersal, to predict patch occupancy and population size of several vertebrate populations varied markedly between taxa (Lindenmayer et al, 2003). Dispersal processes, such as conspecific attraction, could have explained the poor fit for some animals (Lindenmayer et al, 2003).…”

Birds as indicators of sustainable forest management

“…PVA has been conducted on many bird species (Akcakaya et al 1995, Noon and McKelvey 1996, Burgman 2000, McCarthy et al 2000. A key, but not sufficient, component of a successful PVA process is the availability of good life history data (Lindenmayer et al 2003), as is often available for birds.…”

“…There have been numerous criticisms of PVA because of the amount of uncertainty built into the process and, therefore, the potential inaccuracy of predictions (Taylor 1995, Beissinger and Westphal 1998, Ludwig 1999; however, recent evaluations have indicated that despite large levels of uncertainty, PVA provides a useful framework for decision making that is almost always an improvement over ad hoc approaches (Brook et al 2000, Burgman 2000, Lindenmayer et al 2003.…”

The decline of the grey partridge in Europe: comparing demographies in traditional and modern agricultural landscapes

“…While the pressure for answers and the urgency to implement conservation measures for endangered species usually leaves the applied ecologist with no other alternatives to this practical approach-as extensive field experiments are costly, require skilled workers and rarely produce significant results in the short run, scenario analyses based on weak assumptions eventually cast doubts on the effectiveness and usefulness of PVA studies (Harcourt, 1995;Brook et al, 1997;Vucetich and Waite, 1998). In these cases, sensitivity analysis needs to be performed with respect to uncertain parameters and model structure (Lindenmayer and Lacy, 2002;Lindenmayer et al, 2003) as advocated by ; Kangas and Kurki (2000) and Todd et al (2001).…”