3. Identifying the Ecological Correlates of Extinction-Prone Species
37
may have smaller range requirements and therefore lower vulnerability to patch
reductions. Disturbance to landscapes through clearing forests or the introduction
of new food sources or irrigation may enhance survival of some species. The
presence of a trait that increases extinction risk but is not included in the model
will lead to an underestimation of vulnerability.
GLMs can generate useful extinction models, even in circumstances in which
the data quality is low. As the example shows, such an approach can inform
managers about traits that are correlated with extinction risk and hence offer clues
as to which species face elevated extinction risks. Evaluating the ecological
correlates of extinction proneness has been a useful strategy in other situations
(e.g., Laurance 1991; Pimm 1993; Angermeier 1995). In this application, the
model confirmed some recent field work on the Weka that suggested it is more
vulnerable to extinction than was supposed previously. As in all modeling situations, there is no technique that can fully compensate for poor data, and the quality
of the model will be improved if better data are gathered. Maximum likelihood
modeling within the context of GLMs provides one tool that may allow a modeler
to make good use of the limited data that are available.
Literature Cited
Amemiya T (1981) Qualitative response models: a survey. Journal of Economic Literature
19:1483–1536
Angermeier PL (1995) Ecological attributes of extinction prone species: loss of freshwater
fishes of Virginia. Conservation Biology 9:143–158
Atkinson IAE, Millener PR (1991) An ornithological glimpse into New Zealand’s prehuman past. Acta XX Congressus Internationalis Ornithologici 1:129–192
Beissinger SR, Westphal MI (1998) On the use of demographic models of population
viability in endangered species management. Journal of Wildlife Management 62:821–
841
Boyce MS (1992) Population viability analysis. Annual Review of Ecology and Systematics 23:481–506
Davis A, Bellingham M, Molloy J (1992) Who goes into the ark: how to decide which
species are the most threatened. Forest and Bird 23:38– 41
Diamond JM (1975) The island dilemma: lessons of modern biogeographic studies for the
design of natural reserves. Biological Conservation 7:129–146
Dunning JB (ed) (1993) CRC handbook of avian body masses. CRC Press, Boca Raton, FL
East R, Williams GR (1984) Island biogeography and the conservation of New Zealand’s
indigenous forest-dwelling avifauna. New Zealand Journal of Ecology 7:27–35
Gilbert CL (1986) Professor Hendry‘s econometric methodology. Oxford Bulletin of Economics and Statistics 48:288–307
Gill B (1991) New Zealand’s extinct birds. Random Century, Auckland, New Zealand
Hartley P (1997) Conservation strategies for New Zealand. New Zealand Business Roundtable, Wellington, New Zealand
IUCN (1994) International Union for the Conservation of Nature, draft red list categories.
World Conservation Union, Gland, Switzerland
Laurance WF (1991) Ecological correlates of extinction proneness in Australian tropical
rainforest mammals. Conservation Biology 5:79–89
Lyster S (1985) International wildlife law. Grotius Publications Limited, Cambridge, UK
37
may have smaller range requirements and therefore lower vulnerability to patch
reductions. Disturbance to landscapes through clearing forests or the introduction
of new food sources or irrigation may enhance survival of some species. The
presence of a trait that increases extinction risk but is not included in the model
will lead to an underestimation of vulnerability.
GLMs can generate useful extinction models, even in circumstances in which
the data quality is low. As the example shows, such an approach can inform
managers about traits that are correlated with extinction risk and hence offer clues
as to which species face elevated extinction risks. Evaluating the ecological
correlates of extinction proneness has been a useful strategy in other situations
(e.g., Laurance 1991; Pimm 1993; Angermeier 1995). In this application, the
model confirmed some recent field work on the Weka that suggested it is more
vulnerable to extinction than was supposed previously. As in all modeling situations, there is no technique that can fully compensate for poor data, and the quality
of the model will be improved if better data are gathered. Maximum likelihood
modeling within the context of GLMs provides one tool that may allow a modeler
to make good use of the limited data that are available.
Literature Cited
Amemiya T (1981) Qualitative response models: a survey. Journal of Economic Literature
19:1483–1536
Angermeier PL (1995) Ecological attributes of extinction prone species: loss of freshwater
fishes of Virginia. Conservation Biology 9:143–158
Atkinson IAE, Millener PR (1991) An ornithological glimpse into New Zealand’s prehuman past. Acta XX Congressus Internationalis Ornithologici 1:129–192
Beissinger SR, Westphal MI (1998) On the use of demographic models of population
viability in endangered species management. Journal of Wildlife Management 62:821–
841
Boyce MS (1992) Population viability analysis. Annual Review of Ecology and Systematics 23:481–506
Davis A, Bellingham M, Molloy J (1992) Who goes into the ark: how to decide which
species are the most threatened. Forest and Bird 23:38– 41
Diamond JM (1975) The island dilemma: lessons of modern biogeographic studies for the
design of natural reserves. Biological Conservation 7:129–146
Dunning JB (ed) (1993) CRC handbook of avian body masses. CRC Press, Boca Raton, FL
East R, Williams GR (1984) Island biogeography and the conservation of New Zealand’s
indigenous forest-dwelling avifauna. New Zealand Journal of Ecology 7:27–35
Gilbert CL (1986) Professor Hendry‘s econometric methodology. Oxford Bulletin of Economics and Statistics 48:288–307
Gill B (1991) New Zealand’s extinct birds. Random Century, Auckland, New Zealand
Hartley P (1997) Conservation strategies for New Zealand. New Zealand Business Roundtable, Wellington, New Zealand
IUCN (1994) International Union for the Conservation of Nature, draft red list categories.
World Conservation Union, Gland, Switzerland
Laurance WF (1991) Ecological correlates of extinction proneness in Australian tropical
rainforest mammals. Conservation Biology 5:79–89
Lyster S (1985) International wildlife law. Grotius Publications Limited, Cambridge, UK