Subtitle:

Przegląd Geograficzny T. 93 z. 3 (2021)

Publisher:

IGiPZ PAN

Place of publishing:

Warszawa

Description:

24 cm

Abstract:

Because of the scale and speed of species extinctions conservationists require methods that facilitate decision making. Therefore, a wide range of habitat and population viability analysis (PVA) software has been developed. Given the diversity of available programs it is currently challenging to decide which program is the most appropriate for a particular problem and what has to be considered when interpreting and comparing results from different approaches. Previous comparisons of PVA software addressed more generic questions such as data requirements, assumptions and predictive accuracy. In contract, we focus on a more applied problem that is still unresolved: how do simple habitat models and PVA software packages affect the ranking of alternative management scenarios? We addressed this problem by comparing different packages (LARCH, META-X, VORTEX and RAMAS GIS). As a test case, we studied the impact of alternative landscape development scenarios (river regulation, grassland restoration, reforestation and renaturalisation) for the Vistula valley, Poland, on the natterjack toad (Bufo calamita). In this context we also aimed to assess whether the use of at least two different PVA packages can enable users to better understand the differences in model predictions, which would imply a greater awareness and critical use of the packages. Our model selection represents different approaches to population viability analysis, including habitat, local population and stochastic patch occupancy models. The models can be evaluated in regard to the complexity of parameters and to the way the landscape is handled. We used RAMAS GIS to create a habitat model (RAMASh) and a detailed spatially explicit stochastic metapopulation model (RAMASp) which combined served as a complete “virtual” dataset for parameterisation of other programs. As an example of a stochastic patch occupancy model, we selected the META-X software. For a more independent comparison we added VORTEX – another package that includes explicit population dynamics, similar to RAMAS. Additionally, we included the habitat model LARCH because this type of model is often used by policy makers. We compared the metapopulation structure produced by RAMASh and LARCH. Scenario ranking according to the predicted carrying capacity in both programs was exactly the same, because the quantitative results for each scenario were almost identical in both programs. However, the metapopulation structure showed big differences between the programs, especially in the number of small populations. The analyses of results of different PVA programs (RAMASp, VORTEX and META-X) showed that absolute values of viability measures partly differed among these programs. Slight differences in population growth rate in RAMASp and VORTEX were amplified by stochasticity and resulted in visibly lower values of final abundance in VORTEX than in RAMASp. Also the absolute values of intrinsic mean time to extinction showed some discrepancies in VORTEX and META-X. These results are in agreement with findings of previous PVA comparisons, which emphasizes that absolute values of viability measures produced by any single model should be treated with caution. Nevertheless, despite these differences the rankings of the scenarios were the same in all three programs. However the order of the scenarios was different than in habitat models. In addition, these rankings were robust to the choice of viability measure. Taken together, these results emphasize that scenario ranking delivered by PVA software is robust and thus very useful for conservation management. Furthermore, we recommend using at least two PVA software packages in parallel, as this forces user to scrutinize the simplifying assumptions of the underlying models and of the viability metrics used.

References:

Akçakaya, H.R. (2000). Viability Analyses with Habitat-Based Metapopulation Models. Population Ecology, 42(1), 45‑53. https://doi.org/10.1007/s101440050043
Akçakaya, H.R. (2005). RAMAS GIS: linking spatial data with population viability analysis Version 5 (Software manual). Setauket, New York: Appl. Biomath.
Akçakaya, H.R., McCarthy, M.A., & Pearce, J.L. (1995). Linking Landscape Data with Population Viability Analysis: Management Options for the Helmeted Honeyeater Lichenostomus Melanops Cassidix. Biological Conservation, 73(2), 169‑176. https://doi.org/10.1016/0006‑3207(95)90045‑4
Akçakaya, H.R., Radeloff, V.C., Mlandenoff, D. J., & He, H.S. (2004a). Integrating landscape and metapopulation modeling approaches: viability of the sharp-tailed grouse in a dynamic landscape. Conservation. Biology, 18, 526‑537. https://doi.org/10.1111/j.1523‑1739.2004.00520.x
Akçakaya, H.R., Burgman, M.A., Kindvall, O., Wood, C.C., Sjögren-Gulve, P., Hatfield, J.S., & McCarthy, M.A. (2004b). Species conservation and management: case studies. New York: Oxford University Press.
Akçakaya, H.R., & Sjögren-Gulve, P. (2000). Population viability analyses in conservation planning: an overview. Ecological Bulletins, 48, 9‑21. https://doi.org/10.1016/j.baae.2004.03.001
Andrzejewski, R., (2003). Płazy i gady w KPN. Kampinoski Park Narodowy, tom 1, 617‑620.
Baguette, M. (2004). The Classical Metapopulation Theory and the Real, Natural World: A Critical Appraisal. Basic and Applied Ecology, 5(3), 213‑224. https://doi.org/doi: 10.1016/j.baae.2004.03.001
Beissinger, S., & McCullough, D. (red.). (2002). Population viability analysis. Chicago: University of Chicago Press.
Blicharski, M. (2002). Bogate stanowisko ropuchy paskówki Bufo calamita pod Warszawą. Kulon, 7(1‑2), 113‑115.
Boyce, M.S. (1992). Population viability analysis. Annual review of ecology, evolution, and systematics, 23, 481‑506.
Brainerd, S., Kastdalen, L., & Seiler, A. (red.). (2007). Habitat modelling − a tool for managing landscape. Sunnerstra: Norwegian Institute for Nature Research.
Brans, J.P., & Vincke, Ph. (1985). A Preference Ranking Organisation Method: (The PROMETHEE Method for Multiple Criteria Decision-Making). Management Science, 31(6), 647‑656. https://doi.org/10.1287/mnsc.31.6.647
Brook, B.W., Burgman, M.A., Akcakaya, H.R., O'Grady, J.J., & Frankham, R. (2002). Critiques of PVA Ask the Wrong Questions: Throwing the Heuristic Baby out with the Numerical Bath Water. Conservation Biology, 16(1), 262‑263. https://doi.org/10.1046/j.1523‑1739.2002.01426.x
Brook, B.W., Cannon, J.R., Lacy, R.C., Mirande, C., & Frankham, R. (1999). Comparison of the Population Viability Analysis Packages GAPPS, INMAT, RAMAS and VORTEX for the Whooping Crane (Grus americana). Animal Conservation, 2(1), 23‑31. https://doi.org/10.1111/j.1469‑1795.1999.tb00045.x
Brook, B.W., O'Grady, J.J., Chapman, A.P., Burgman, M.A., Akcakaya, H.R., & Frankham, R. (2000). Predictive Accuracy of Population Viability Analysis in Conservation Biology. Nature, 404(6776), 385‑387. https://doi.org/10.1038/35006050
Bruinderink, G.G., van der Sluis, T., Lammertsma, D., Opdam, P., & Pouwels, R. (2003). Designing a coherent ecological network for large mammals in northwestern Europe. Conservation Biology, 17, 549‑557. https://doi.org/10.1046/j.1523‑1739.2003.01137.x
Burgman, M.A., Ferson, S., & Akçakaya, H.R. (1993). Risk assessment in conservation biology. London: Chapman & Hall.
Cabeza, M. (2003). Habitat loss and connectivity of reserve networks in probability approaches to reserve design. Ecology Letters, 6, 665‑672. https://doi.org/10.1046/j.1461‑0248.2003.00475.x
Chardon, J.P., Foppen, R.P.B., & Geilen, N. (2000). LARCH-RIVER: A Method to Assess the Functioning of Rivers as Ecological Networks. European Water Management, 3(6), 35‑43.
Clark, T.W., Backhouse, G.N., & Lacy, R.C. (1991). Report of a workshop on population viability assessment as a tool for the threatened species management and conservation. Australian Zoologist, 27, 28‑35. https://doi.org/10.7882/az.1991.004
Cushman, S. (2006). Effects of habitat loss and fragmentation on amphibians: A review and prospectus. Biological Conservation, 128(2), 231‑240. https://doi.org/10.1016/j.biocon.2005.09.031
Dobson, A.P., Bradshaw, A.D., & Baker, A.J.M. (1997). Hopes for the future: restoration ecology
Drechsler, M., Frank, K., Hanski, I., O'Hara, R.B., & Wissel, C. (2003). Ranking Metapopulation Extinction Risk: From Patterns in Data to Conservation Management Decisions. Ecological Appl
Elith, J., & Leathwick, J.R. (2009). Species distribution models: ecological explanation and prediction across space and time. Annual review of ecology, evolution, and systematics, 40, 677‑697. https://doi.org/10.1146/annurev.ecolsys.110308.120159
Ellner, S.P., Fieberg, J., Ludwig, D., & Wilcox, C. (2002). Precision of Population Viability Analysis. Conservation Biology, 16(1), 258‑261. https://doi.org/10.1046/j.1523‑1739.2002.00553.x
Elżanowski, A., Ciesiołkiewicz, J., Kaczor, M., Radwańska, J., & Urban, R., (2008). Amphibian road mortality in Europe: a meta-analysis with new data from Poland. European Journal of Wildlife Research, 55(1), 33‑43. https://doi.org/10.1007/s10344‑008‑0211-x
Frank, K., Lorek, H., Sonnenschein, M., Wissel, C., & Grimm, V. (2003). META-X - Software for Metapopulation Viability Analysis. Berlin: Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978‑3-642‑55723‑1
Franz, K.W. (2011). Metapopulation viability analysis of the Natterjack Toad (Bufo calamita): a comparative assessment of PVA software packages and management scenarios. Rozprawa doktorska. Uniwersytet Warszawski.
Franz, K.W., Romanowski, J., & Grimm, V. (2011). Modele siedliskowe i analiza żywotności populacji. Wiadomości Ekologiczne, 57, 97‑108.
Franz, K.W., Romanowski, J., Johst, K., & Grimm, V. (2013). Ranking Landscape Development Scenarios Affecting Natterjack Toad (Bufo Calamita) Population Dynamics in Central Poland. PLoS ONE, 8(5), e64852. https://doi.org/10.1371/journal.pone.0064852
Fulton, E.A., Boschetti, F., Sporcic, M., Jones, T., Little, L.R., Dambacher, J.M., Gray, R., Scott, R., &Gorton, R. (2015). A Multi-Model Approach to Engaging Stakeholder and Modellers in Complex Environmental Problems. Environmental Science & Policy, 48, 44‑56. https://doi.org/10.1016/j.envsci.2014.12.006
Grimm, V., & Wissel, C. (2004). The Intrinsic Mean Time to Extinction: A Unifying Approach to Analysing Persistence and Viability of Populations. Oikos, 105(3), 501‑511. https://doi.org/10.1111/j.0030‑1299.2004.12606.x
Hanski, I. (1994). A Practical Model of Metapopulation Dynamics. Journal of Animal Ecology, 63(1), 151‑162. https://doi.org/10.2307/5591
Harris, R.B., Metzgar, L.H., & Bevins, C.D. (1986). GAPPS - Generalized Animal Population Projection System - User's Manual. Missoula, MT: Montana Cooperative Wildlife Research Unit Publ.
Hijmans, R.J., & Graham, C.H. (2006). The ability of climate envelope models to predict the effect of climate change on species distributions. Global change biology, 12(12), 2272‑2281. https://doi.org/10.1111/j.1365‑2486.2006.01256.x
Hokit, D.G., Stith, B.M., & Branch, L.C. (2001). Comparison of Two Types of Metapopulation Models in Real and Artificial Landscapes. Conservation Biology, 15(4), 1102‑1113. https://doi.org/10.1046/j.1523‑1739.2001.0150041102.x
Kindvall, O. (2000). Comparative Precision of Three Spatially Realistic Simulation Models of Metapopulation Dynamics. Ecological Bulletins, 48, 101‑110.
Lacy, R.C. (1993). VORTEX: A Computer Simulation Model for Population Viability Analysis. Wildlife Research, 20, 45‑65. https://doi.org/10.1071/WR9930045
Lindenmayer, D.B., Burgman, M.A., Akcakaya, H.R., Lacy, R.C., & Possingham, H.P. (1995). A Review of the Generic Computer-Programs Alex, Ramas/Space and Vortex for Modeling the Viability of Wildlife Metapopulations. Ecological Modelling, 82(2), 161‑174. https://doi.org/10.1016/0304‑3800(94)00085-V
Lindenmayer, D.B., Clark, T.W., Lacy, R.C., & Thomas, V.C. (1993). Population viability analysis as a tool in wildlife conservation policy: with reference to Australia. Environmental Management, 17(6), 745‑758. https://doi.org/10.1007/BF02393895
Lindenmayer, D.B., Possingham, H.P., Lacy, R.C., McCarthy, M.A., & Pope, M.L. (2003). How Accurate Are Population Models? Lessons from Landscape-Scale Tests in a Fragmented System. Ecology Letters, 6(1), 41‑47. https://doi.org/10.1046/j.1461‑0248.2003.00391.x
Mace, G.M., & Lande, R. (1991). Assessing extinction threats: towards a re-evaluation of IUCN threatened species categories. Conservation Biology, 5, 148‑157. https://doi.org/10.1111/J.1523‑1739.1991.TB00119.X
McCarthy, M.A., Andelman, S.J., & Possingham, H.P. (2003). Reliability of relative predictions in population viability analysis. Conservation Biology, 17, 982‑989. https://doi.org/10.1046/j.1523‑1739.2003.01570.x
McCarthy, M.A., & Thompson, C. (2001). Expected Minimum Population Size as a Measure of Threat. Animal Conservation, 4(4), 351‑355. https://doi.org/10.1017/S136794300100141X
Mills, L.S., Hayes, S.G., Baldwin, C., Wisdom, M.J., Citta, J., Mattson, D.J., & Murphy, K. (1996). Factors Leading to Different Viability Predictions for a Grizzly Bear Data Set. Conservation Biology, 10(3), 863‑873. https://doi.org/10.1046/j.1523‑1739.1996.10030863.x
Mills, L.S., & Smouse, P.E. (1994). Demographic Consequences of Inbreeding in Remnant Populations. The American Naturalist, 144(3), 412‑431. https://doi.org/10.1086/285684
Murphy, J.M., Sexton, D.M., Barnett, D.N., Jones, G.S., Webb, M.J., Collins, M., & Stainforth, D.A. (2004). Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature, 430(7001), 768‑772. https://doi.org/10.1038/nature02771
O'Grady, J.J., Reed, D.H., Brook, B.W., & Frankham, R. (2004). What Are the Best Correlates of Predicted Extinction Risk? Biological Conservation, 118(4), 513‑520. https://doi.org/10.1016/j.biocon.2003.10.002
Opdam, P., Verboom, J., & Pouwels, R. (2003). Landscape cohesion: an index for the conservation potential of landscapes for biodiversity. Landscape Ecology, 18, 113‑126. https://doi.org/10.1023/A: 1024429715253
Pe'er, G., Matsinos, Y., Johst, K., Franz, K.W., Turlure, C., Radchuk, V., Malinowska, A., Curtis, J.M.R., Naujokaitis-Lewis, I., Wintle, B.A., & Henle, K. (2013). A Protocol for Better Design, Application, and Communication of Population Viability Analyses. Conservation Biology, 27, 644‑656. https://doi.org/10.1111/cobi.12076
Pellet, J., Maze, G., & Perrin, N. (2006). The Contribution of Patch Topology and Demographic Parameters to Population Viability Analysis Predictions: The Case of the European Tree Frog. Population Ecology, 48(4), 353‑361. https://doi.org/10.1007/s10144‑006‑0003‑7
Possingham, H.P., & Davies, I. (1995). ALEX: A Model for the Viability Analysis of Spatially Structured Populations. Biological Conservation, 73(2), 143‑150. https://doi.org/10.1016/0006‑3207(95)90039-X
Radchuk, V., Johst, K., Groeneveld, J., Turlure, C., Grimm, V., & Schtickzelle, N. (2014). Appropriate Resolution in Time and Model Structure for Population Viability Analysis: Insights from a Butterfly Metapopulation. Biological Conservation, 169, 345‑354. https://doi.org/10.1016/j.biocon.2013.12.004
Rannap, R., Lõhmus, A. & Jakobson, K. (2007). Consequences of coastal meadow degradation: The case of the natterjack toad (Bufo Calamita) in Estonia. Wetlands, 27, 390.
Reed, J.M., Mills, L.S., Dunning, J.B., Menges, E.S., Mckelvey, K.S., Frye, R., Beissinger, S.R., Anstett, M.C., & Miller, P. (2002). Emerging Issues in Population Viability Analysis. Conservation Biology, 16(1), 7‑19. https://doi.org/10.1046/j.1523‑1739.2002.99419.x
Romanowski, J. (2007). Vistula River Valley as the Ecological Corridor for Mammals. Polish Journal of Ecology, 55(4), 805‑819.
Romanowski, J., Kowalczyk, K., & Rau, K. (2008). Population Viability Modelling and Potential Threats to the Beaver in the Vistula River Valley, Poland. Annales Zoologici Fennici 45(4), 323‑328. https://doi.org/10.5735/086.045.0413
Sala, O.E., Chapin, F.S., Armesto, J.J., Berlow, E., Bloomfield, J., Dirzo, R., Huber-Sanwald, E. et al. (2000). Global biodiversity scenarios for the year 2100. Science, 287(5459), 1770‑1774. https://doi.org/10.1126/science.287.5459.1770
Shea, K., Runge, M.C., Pannell, D., Probert, W. J., Li, S.L., Tildesley, M., & Ferrari, M. (2020). Harnessing multiple models for outbreak management. Science, 368(6491), 577‑579. https://doi.org/10.1126/science.abb9934
Sjögren-Gulve, P., & Hanski, I. (2000). Metapopulation Viability Analysis Using Occupancy Models. Ecological Bulletins, 48, 53‑71. https://doi.org/10.2307/20113248
Van der Sluis, T., Romanowski, J., Bouwma, I.M., & Matuszkiewicz, J. (2007). Comparison of Scenarios for the Vistula River, Poland. W: S.-K. Hong, N. Nakagoshi, B. Fu, & Y. Morimoto (red.), Landscape Ecological Applications in Man-Influenced Areas Linking Man and Nature Systems (s. 417‑433). Dordrecht, The Netherlands: Springer.
Zurell, D., Jeltsch, F., Dormann, C.F., & Schroder, B. (2009). Static Species Distribution Models in Dynamically Changing Systems: How Good Can Predictions Really Be? Ecography, 32(5), 733‑744. https://doi.org/10.1111/j.1600‑0587.2009.05810.x

Relation:

Przegląd Geograficzny

Volume:

93

Issue:

3

Start page:

365

End page:

385

Detailed Resource Type:

Article

Format:

application/octet-stream

Resource Identifier:

oai:rcin.org.pl:231904 ; 0033-2143 (print) ; 2300-8466 (on-line) ; 10.7163/PrzG.2021.3.3

Source:

CBGiOS. IGiPZ PAN, sygn.: Cz.181, Cz.3136, Cz.4187 ; click here to follow the link

Language:

pol

Language of abstract:

eng

Rights:

Creative Commons Attribution BY 4.0 license

Terms of use:

Copyright-protected material. [CC BY 4.0] May be used within the scope specified in Creative Commons Attribution BY 4.0 license, full text available at: ; -

Digitizing institution:

Institute of Geography and Spatial Organization of the Polish Academy of Sciences

Original in:

Central Library of Geography and Environmental Protection. Institute of Geography and Spatial Organization PAS

Projects co-financed by:

Programme Innovative Economy, 2010-2014, Priority Axis 2. R&D infrastructure ; European Union. European Regional Development Fund

Access:

Open