Reconstructing the observation process to correct for changing detection probability of a critically endangered species

Effective conservation decision-making requires robust estimates of population trends. It is often assumed that, as long as monitoring methods remain consistent over time, trends in relative abundance are valid proxies for actual abundance. However, if the bias and uncertainty of relative abundance estimates change over time, this can have a serious impact on the validity of monitoring programmes. We developed a simple model for the retrospective assessment of the likely error and bias in abundance estimates from aerial surveys of the saiga antelope. Due to dramatic reductions in group size and density, current estimates of abundance are probably substantially lower than the true population size, and the level of uncertainty surrounding these estimates precludes their use for mon- itoring trends. This has implications for the Government of Kazakhstan's ability to monitor progress towards their agreed conservation goals. The method is potentially widely applicable to species for which historical data on relative abundance and group size are available.

[1]  W. Hochachka,et al.  Trends in trappability and stop-over duration can confound interpretations of population trajectories from long-term migration ringing studies , 2008, Journal of Ornithology.

[2]  K. Lorenzen,et al.  Aggregated yield and fishing effort in multispecies fisheries: an empirical analysis , 2006 .

[3]  J. Laake,et al.  Aerial line transect survey to estimate abundance of marsh deer (Blastocerus dichotomus) (Illiger, 1815) , 2005 .

[4]  Adrian C. Newton,et al.  Bird Ecology and Conservation: A Handbook of Techniques , 2004 .

[5]  C. Walters.  Folly and fantasy in the analysis of spatial catch rate data , 2003 .

[6]  Frank P. Howe,et al.  A SEVEN-YEAR COMPARISON OF RELATIVE-ABUNDANCE AND DISTANCE-SAMPLING METHODS , 2003 .

[7]  E. Milner‐Gulland,et al.  Conservation: Reproductive collapse in saiga antelope harems , 2003, Nature.

[8]  E. J. Milner-Gulland,et al.  Rangeland degradation in Kazakhstan during the Soviet era: re-examining the evidence , 2003 .

[9]  Kenneth H. Pollock,et al.  Large scale wildlife monitoring studies: statistical methods for design and analysis , 2002 .

[10]  E. Milner‐Gulland,et al.  Dramatic declines in saiga antelope populations , 2001, Oryx.

[11]  T. Clutton‐Brock,et al.  The relative roles of density and climatic variation on population dynamics and fecundity rates in three contrasting ungulate species , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[12]  Stephens,et al.  Consequences of the Allee effect for behaviour, ecology and conservation. , 1999, Trends in ecology & evolution.

[13]  E. Milner‐Gulland,et al.  The ecology and management of the Saiga antelope in Kazakhstan , 1998 .

[14]  E. Milner‐Gulland.  A population model for the management of the saiga antelope , 1994 .

[15]  P. Bayliss,et al.  Correcting bias in aerial survey population estimates of feral livestock in Northern Australia using the double-count technique , 1989 .

[16]  Michael D. Samuel,et al.  Visibility Bias during Aerial Surveys of Elk in Northcentral Idaho , 1987 .

[17]  Lyman L. McDonald,et al.  Size Bias in Line Transect Sampling , 1987 .

[18]  K. Pollock,et al.  Correction of Visibility Bias in Aerial Surveys Where Animals Occur in Groups , 1981 .

[19]  G. Jolly.  Sampling Methods for Aerial Censuses of Wildlife Populations , 1969 .

[20]  R. Harris.  Wild Ungulate Surveys in Grassland Habitats: , 2010 .

[21]  A. M. Kitchen,et al.  An evaluation of survey methods for monitoring swift fox abundance , 2002 .

[22]  S. Buckland.  Introduction to distance sampling : estimating abundance of biological populations , 2001 .

[23]  G. Caughley,et al.  A Correction Factor for Counting Emus from the Air, and its Application to Counts in Western Australia , 1982 .