META-X: Generic Software for Metapopulation Viability Analysis

The major tools used to make population viability analyses (PVA) quantitative are stochastic models of population dynamics. Since a specially tailored model cannot be developed for every threatened population, generic models have been designed which can be parameterised and analysed by non-modellers. These generic models compromise on detail so that they can be used for a wide range of species. However, generic models have been criticised because they can be employed without the user being fully aware of the concepts, methods, potentials, and limitations of PVA. Here, we present the conception of a new generic software package for metapopulation viability analysis, META-X. This conception is based on three elements, which take into account the criticism of earlier generic PVA models: (1) comparative simulation experiments; (2) an occupancy-type model structure which ignores details of local population dynamics (these details are integrated in external submodels); and (3) a unifying currency to quantify persistence and viability, the ‘intrinsic mean time to extinction’. The rationale behind these three elements is explained and demonstrated by exemplary applications of META-X in the three fields for which META-X has been designed: teaching, risk assessment in the field, and planning. The conception of META-X is based on the notion that PVA is a tool to deal with rather than to overcome uncertainty. The purpose of PVA is to produce relative, not absolute, assessments of extinction risk which support, but do not supplant, management decisions.

[1]  Amy W. Ando,et al.  On the Use of Demographic Models of Population Viability in Endangered Species Management , 1998 .

[2]  R. Lacy Structure of the VORTEX simulation model for population viability analysis , 2000 .

[3]  Atte Moilanen,et al.  SIMPLE CONNECTIVITY MEASURES IN SPATIAL ECOLOGY , 2002 .

[4]  Dr. Karin Frank,et al.  META-X®-Software for Metapopulation Viability Analysis , 2003, Springer Berlin Heidelberg.

[5]  Ilse Storch,et al.  Genetic correlates of spatial population structure in central European capercaillie Tetrao urogallus and black grouse T. tetrix: a project in progress , 2000, Wildlife Biology.

[6]  Ricard V. Solé,et al.  Modeling spatiotemporal dynamics in ecology , 1998 .

[7]  Karin Frank,et al.  Ecologically Differentiated Rules of Thumb for Habitat Network Design – Lessons from a Formula , 2004, Biodiversity & Conservation.

[8]  G. Edwards‐Jones,et al.  Reintroducing capercaillie (Tetrao urogallus) into southern Scotland: identification of minimum viable populations at potential release sites , 1998, Biodiversity & Conservation.

[9]  Ilkka Hanski,et al.  Long‐Term Dynamics in a Metapopulation of the American Pika , 1998, The American Naturalist.

[10]  Scott Ferson,et al.  Risk assessment in conservation biology , 1993 .

[11]  Atte Moilanen,et al.  PATCH OCCUPANCY MODELS OF METAPOPULATION DYNAMICS: EFFICIENT PARAMETER ESTIMATION USING IMPLICIT STATISTICAL INFERENCE , 1999 .

[12]  Mark A. Burgman,et al.  Differences and Congruencies between PVA Packages: the Importance of Sex Ratio for Predictions of Extinction Risk , 2000 .

[13]  C. ter Braak,et al.  Toward Ecologically Scaled Landscape Indices , 2001, The American Naturalist.

[14]  M. Groom,et al.  The Analysis of Population Persistence: An Outlook on the Practice of Viability Analysis , 1998 .

[15]  Martin Drechsler,et al.  Trade-offs between local and regional scale management of metapopulations , 1998 .

[16]  Hugh P. Possingham,et al.  Genetics, Demography and Viability of Fragmented Populations: Population viability analysis for conservation: the good, the bad and the undescribed , 2000 .

[17]  Johan A. J. Metz,et al.  Linking local and regional dynamics in stochastic metapopulation models , 1991 .

[18]  M. Shaffer Minimum Population Sizes for Species Conservation , 1981 .

[19]  Atte Moilanen,et al.  ESTIMATING THE PARAMETERS OF SURVIVAL AND MIGRATION OF INDIVIDUALS IN METAPOPULATIONS , 2000 .

[20]  Christian Wissel,et al.  Extinction of populations by random influences , 1991 .

[21]  Martin Drechsler,et al.  Separability of Local and Regional Dynamics in Metapopulations , 1997 .

[22]  Johan A. J. Metz,et al.  Metapopulation models for impact assessment of fragmentation , 1993 .

[23]  M. Boyce Population Viability Analysis , 1992 .

[24]  V. Grimm Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future? , 1999 .

[25]  David B. Lindenmayer,et al.  A Review of the Generic Computer Programs ALEX, RAMAS/space and VORTEX for Modelling the Viability of Wildlife Metapopulations , 1995 .

[26]  Donald Ludwig,et al.  The Distribution of Population Survival Times , 1996, The American Naturalist.

[27]  Hugh P. Possingham,et al.  ALEX : A Model For The Viability Analysis Of Spatially Structured Populations , 1995 .

[28]  Atte Moilanen,et al.  Implications of empirical data quality to metapopulation model parameter estimation and application , 2002 .

[29]  H. Resit Akçakaya,et al.  Predictive accuracy of population viability analysis in conservation biology , 2000, Nature.

[30]  I. Hanski A Practical Model of Metapopulation Dynamics , 1994 .

[31]  Atte Moilanen,et al.  The equilibrium assumption in estimating the parameters of metapopulation models. , 2000 .

[32]  Christian Wissel,et al.  Modelling persistence in dynamic landscapes : lessons from a metapopulation of the grasshopper Bryodema tuberculata , 1997 .

[33]  Karin Frank,et al.  Spatial aspects of metapopulation survival – from model results to rules of thumb for landscape management , 1998, Landscape Ecology.

[34]  Ilse Storch,et al.  Minimum viable population size of capercaillie Tetrao urogallus: results from a stochastic model , 2000, Wildlife Biology.

[35]  C.J.F. ter Braak,et al.  The incidence function approach to modeling of metapopulation dynamics , 1998 .

[36]  Christian Wissel,et al.  Modelling Extinction and Survival of Small Populations , 1994 .