The virtual ecologist approach: Simulating data and observers

Oikos

Volumn 119, Issue 4, 2010, Pages 622-635

  Zurell, Damaris ^a   Berger, Uta ^b   Cabral, Juliano S ^a   Jeltsch, Florian ^c   Meynard, Christine N ^d   Münkemüller, Tamara ^e   Nehrbass, Nana ^f   Pagel, Jörn ^g   Reineking, Björn ^h   Schröder, Boris ^b   Grimm, Volker ^a,b  

^a UNIVERSITY OF POTSDAM   (Germany)

^b DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

^c INSTITUT DES SCIENCES DE L EVOLUTION   (France)

^d UNIVERSITÉ JOSEPH FOURIER   (France)

^e HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH UFZ   (Germany)

^f UNIVERSITY OF BAYREUTH   (Germany)

^g LEIBNIZ CENTRE FOR AGRICULTURAL LANDSCAPE RESEARCH ZALF   (Germany)

^h Stünz Mö ^*  (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL; BEHAVIORAL ECOLOGY; DATA PROCESSING; DATA QUALITY; ECOLOGICAL APPROACH; ECOLOGICAL MODELING; EMPIRICAL ANALYSIS; MOVEMENT; OPTIMIZATION; POPULATION DISTRIBUTION; SAMPLING; STATISTICAL ANALYSIS;

ANIMALIA;

EID: 77952498139     PISSN: 00301299     EISSN: 16000706     Source Type: Journal    
DOI: 10.1111/j.1600-0706.2009.18284.x     Document Type: Article

References (70)

1. Albert C H, Thuiller W. Favourability functions versus probability of presence: advantages and misuses. Ecography 2008, 31:417-422.
2. Araújo M B, Rahbek C. How does climate change affect biodiversity? Science 2006, 313:1396-1397.
3. Austin M P. Evaluation of statistical models used for predicting plant species distributions: role of artificial data and theory. Ecol. Modell. 2006, 199:197-216.
4. Berger U. Virtual biologists observe virtual grasshoppers: an assessment of different mobility parameters for the analysis of movement patterns. Ecol. Modell. 1999, 115:119-127.
5. Bolker B M. Ecological models and data in R 2008, Princeton Univ. Press
6. Burnham K P, Anderson D R. Model selection and multimodel inference: a practical-theoretic approach 2002, Springer
7. Cabral J S, Schurr F M. Estimating demographic models for the range dynamics of plant species. Global. Ecol. Biogeogr 2009, doi: 10.1111/j.l466-8238.2009.00492.x
8. Crist T O, Wiens J A. Individual movements and estimation of population size in darkling beetles (Coleoptera: Tenebrionidae). J. Anim. Ecol. 1995, 64:733-746.
9. DeAngelis D L, Mooij W M. Individual-based modelling of ecological and evolutionary processes. Annu. Rev. Ecol. Evol. Syst. 2005, 36:147-168.
10. Dormann C F. Promising the future? Global change projections of species distributions. Basic Appl. Ecol. 2007, 8:387-397.
11. Dormann C F. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 2007, 30:609-628.
12. Fischer J, Lindenmayer D B. Treating the nestedness temperature calculator as a black box can lead to false conclusions. Oikos 2002, 99:193-199.
13. Fortin M-J. Spatial autocorrelation and sampling design in plant ecology. Vegetatio 1989, 83:209-222.
14. Gotelli N J. Patterns and causes of species richness: a general simulation model for macroecology. Ecol. Lett. 2009, 12:873-886.
15. Greve M, Chown S L. Endemicity biases nestedness metrics: a demonstration, explanation and solution. Ecography 2006, 29:347-356.
16. Grimm V. Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future? Ecol. Modell. 1999, 115:129-148.
17. Grimm V, Railsback S F. Individual-based modeling and ecology 2005, Princeton Univ. Press
18. Grimm V. Individual-based modelling and ecological theory: synthesis of a workshop. Ecol. Modell. 1999, 115:275-282.
19. Grimm V. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 2005, 310:987-991.
20. Gruber B. A new method for estimating visitation rates of cryptic animals via repeated surveys of indirect signs. J. Appl. Ecol. 2008, 45:728-735.
21. Gu W, Swihart R K. Absent or undetected? Effects of non-detection of species occurence on wildlife-habitat models. Biol. Conserv. 2004, 116:195-203.
22. Guisan A, Zimmermann N E. Predictive habitat distribution models in ecology. Ecol. Modell. 2000, 135:147-186.
23. Halle S, Halle B. Modelling activity synchronisation in free-ranging microtine rodents. Ecol. Modell. 1999, 115:165-176.
24. Hanski I. Metapopulation dynamics. Nature 1998, 396:41-49.
25. Hanski I. Metapopulation ecology 1999, Oxford Univ. Press
26. Hanski I. Estimating the parameters of survival and migration of individuals in metapopulations. Ecology 2000, 81:239-251.
27. Hardy O J. Testing the spatial phylogenetic structure of local communities: statistical performances of different null models and test statistics on a locally neutral community. J. Ecol. 2008, 96:914-926.
28. Higgins C L. The role of stochastic processes in producing nested patterns of species distributions. Oikos 2006, 114:159-167.
29. Hilker F M. Parameterizing, evaluating and comparing metapopulation models with data from individual-based simulations. Ecol. Modell. 2006, 199:476-485.
30. Hirzel A, Guisan A. Which is the optimal sampling strategy for habitat suitability modelling. Ecol. Modell. 2002, 157:331-341.
31. Hirzel A H. Assessing habitat-suitability models with a virtual species. Ecol. Modell. 2001, 145:111-121.
32. Jiménez-Valverde A, Lobo J M. Threshold criteria for conversion of probability of species presence to either-or presence-absence. Acta Oecol. 2007, 31:361-369.
33. Kraft N J B. Trait evolution, community assembly, and the phylogenetic structure of ecological communities. Am. Nat. 2007, 170:271-283.
34. Legendre P. The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography 2002, 25:601-615.
35. de Marco P. Spatial analysis improves species distribution modelling during range expansion. Biol. Lett. 2008, 4:577-580.
36. Mac Nally R. Interaction strengths and spatial scale in community ecology: simulated quadrat-sampling and confinement experiments involving animals of different mobilities. Ecol. Modell. 2001, 144:139-152.
37. McCarthy M A. Reliability of relative predictions in population viability analysis. Conserv. Biol. 2003, 17:982-989.
38. Meynard C N, Quinn J F. Predicting species distributions: a critical comparison of the most common statistical models using artificial species. J. Biogeogr. 2007, 34:1455-1469.
39. Moilanen A. Patch occupancy models of metapopulation dynamics: efficient parameter estimation using implicit statistical inference. Ecology 1999, 80:1031-1043.
40. Moilanen A. The equilibrium assumption in estimating the parameters of metapopulation models. J. Anim. Ecol. 2000, 69:143-153.
41. Moilanen A. Implications of empirical data quality for metapopulation model parameter estimation and application. Oikos 2002, 96:516-530.
42. Moisen G G, Frescino T S. Comparing five modelling techniques for predicting forest characteristics. Ecol. Modell. 2002, 157:209-225.
43. Nehrbass N. Empirical and virtual investigation of the population dynamics of an alien plant under the constraints of local carrying capacity: Heracleum mantegazzianum in the Czech Republic. Persp. Plant Ecol. Evol. Syst. 2006, 7:253-262.
44. Nott M P. Effects of abiotic factors on population dynamics of the Cape sable seaside sparrow and continental patterns of herpetological species richness: an appropriately scaled landscape approach 1998, Univ. of Tennessee
45. Paruelo J M, Tomasel F. Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models. Ecol. Modell. 1997, 98:173-186.
46. Parysow P, Gertner G. Virtual experimentation: conceptual models and hypothesis testing of ecological scenarios. Ecol. Modell. 1997, 98:59-71.
47. Parysow P, Gertner G. The role of interactions in hypothesis testing of ecological scenarios with process models. Ecol. Modell. 1999, 116:107-124.
48. Perner J, Schueler S. Estimating the density of ground-dwelling arthropods with pitfall traps using a nested cross-array. J. Anim. Ecol. 2004, 73:469-477.
49. Pompe S. Climate and land use change impacts on plant distributions in Germany. Biol. Lett. 2008, 4:564-567.
50. Rachowicz L J. Evaluating at-sea sampling designs for marbled murrelets using a spatially explicit model. Ecol. Modell. 2006, 196:329-344.
51. Railsback S F. What can habitat preferences models tell us? Tests using a virtual trout population. Ecol. Appl. 2003, 13:1580-1594.
52. Ratcliffe N. Resolving the population status of Ascension frigatebird Fregata aquila using a 'virtual ecologist' model. Ibis 2008, 150:300-306.
53. Real R. Obtaining environmental favourability functions from logistic regression. Environ. Ecol. Stat. 2006, 13:237-245.
54. Reese G C. Factors affecting species distribution predictions: a simulation modeling experiment. Ecol. Appl. 2005, 15:554-564.
55. Reineking B, Schröder B. Constrain to perform: regularization of habitat models. Ecol. Modell. 2006, 193:675-690.
56. Sabo J L, Gerber L R. Predicting extinction risk in spite of predator-prey oscillations. Ecol. Appl. 2007, 17:1543-1554.
57. Skarpaas O. Optimizing dispersal study design by Monte Carlo simulation. J. Appl. Ecol. 2005, 42:731-739.
58. Stickel L F. A comparison of certain methods of measuring ranges of small mammals. J. Mammal. 1954, 35:1-15.
59. Thomas C D. Extinction risk from climate change. Nature 2004, 427:145-148.
60. Thuiller W. Patterns and uncertainties of species' range shifts under climate change. Global Change Biol 2004, 10:2020-2027.
61. Thuiller W. Predicting global change impacts on plant species' distributions: future challenges. Persp. Plant Ecol. Evol. Syst. 2008, 9:137-152.
62. Tyre A J. Inferring process from pattern: can territory occupancy provide information about life history parameters? Ecol. Appl. 2001, 11:1722-1737.
63. Tyre A J. Improving precision and reducing bias in biological surveys: estimating false-negative error rates. Ecol. Appl. 2003, 13:1790-1801.
64. Ulrich W, Gotelli N J. Disentangling community patterns of nestedness and species co-occurrence. Oikos 2007a, 116:2053-2061.
65. Ulrich W, Gotelli N J. Null model analysis of species nestedness patterns. Ecology 2007b, 88:1824-1831.
66. Wiegand T. Using pattern-oriented modelling for revealing hidden information: a key for reconceiling ecological theory and application. Oikos 2003, 100:209-222.
67. Wissel C. Aims and limits of ecological modelling exemplified by island theory. Ecol. Modell. 1992, 63:1-12.
68. Wootton J T. Effects of birds on sea urchins and algae - a lower-intertidal trophic cascade. Ecoscience 1995, 2:321-328.
69. Wunder J. Predicting tree mortality from growth data: how virtual ecologists can help real ecologists. J. Ecol. 2008, 96:174-187.
70. Zurell D. Static species distribution models in dynamically changing systems: How good can predictions really be? Ecography 2009, 32:733-744.