Access the full text.
Sign up today, get DeepDyve free for 14 days.
A. Swengel (1990)
Monitoring butterfly populations using the Fourth of July Butterfly Count.American Midland Naturalist, 124
N. Pirie (1964)
Biological DiversityNature, 202
E. Fleishman, G. Austin, D. Murphy (1997)
Natural History and Biogeography of the Butterflies of the Toiyabe Range, Nevada (Lepidoptera: Papilionoidea)., 4
D. Rubinoff (2001)
Evaluating the California Gnatcatcher as an Umbrella Species for Conservation of Southern California Coastal Sage ScrubConservation Biology, 15
J. Reed (1996)
Using Statistical Probability to Increase Confidence of Inferring Species ExtinctionConservation Biology, 10
W. Howe (1874)
The Butterflies of North AmericaThe American Naturalist, 8
E. Fleishman, R. Nally, J. Fay, D. Murphy (2001)
Modeling and Predicting Species Occurrence Using Broad‐Scale Environmental Variables: an Example with Butterflies of the Great BasinConservation Biology, 15
Kevin Leftwich, P. Angermeier, C. Dolloff (1997)
Factors influencing behavior and transferability of habitat models for a benthic stream fishTransactions of The American Fisheries Society, 126
R. Blair, A. Launer (1997)
Butterfly diversity and human land use: Species assemblages along an urban gradientBiological Conservation, 80
K. McDonald, James Brown (1992)
Using Montane Mammals to Model Extinctions Due to Global ChangeConservation Biology, 6
B. Tardif, J. Desgranges (1998)
Correspondence between bird and plant hotspots of the St Lawrence river and influence of scale on their locationBiological Conservation, 84
M. Morris, E. Pollard, T. Yates (1993)
Monitoring butterflies for ecology and conservation
S. Manel, H. Williams, S. Ormerod (2001)
Evaluating presence-absence models in ecology: the need to account for prevalenceJournal of Applied Ecology, 38
A. Guisan, N. Zimmermann (2000)
Predictive habitat distribution models in ecologyEcological Modelling, 135
C. Kremen (1992)
Assessing the Indicator Properties of Species Assemblages for Natural Areas Monitoring.Ecological applications : a publication of the Ecological Society of America, 2 2
James Miller, P. Cale (2000)
BEHAVIORAL MECHANISMS AND HABITAT USE BY BIRDS IN A FRAGMENTED AGRICULTURAL LANDSCAPEEcological Applications, 10
E. Fleishman, G. Austin, Andrew Weiss (1998)
AN EMPIRICAL TEST OF RAPOPORT'S RULE: ELEVATIONAL GRADIENTS IN MONTANE BUTTERFLY COMMUNITIESEcology, 79
E. Ranta, H. Rita, M. Crawley (1994)
GLIM for EcologistsJournal of Animal Ecology, 63
L. Jackson, Anett Trebitz, K. Cottingham (2000)
An Introduction to the Practice of Ecological Modeling, 50
James Brown (1978)
The theory of insular biogeography and the distribution of boreal birds and mammalsGreat Basin naturalist memoirs, 2
G. Bell (2001)
Neutral macroecology.Science, 293 5539
A. Magurran, M. Rosenzweig (1996)
Species Diversity in Space and Time.Journal of Applied Ecology, 33
G. Austin, D. Murphy (1987)
Zoogeography of Great Basin butterflies: patterns of distribution and differentiationThe Great Basin naturalist, 47
C. Young (1997)
Ecology And Conservation Of ButterfliesBiodiversity & Conservation, 6
J. Kerr, T. Southwood, J. Cihlar (2001)
Remotely sensed habitat diversity predicts butterfly species richness and community similarity in CanadaProceedings of the National Academy of Sciences of the United States of America, 98
R. Nally, A. Bennett, G. Horrocks (2000)
Forecasting the impacts of habitat fragmentation. Evaluation of species-specific predictions of the impact of habitat fragmentation on birds in the box–ironbark forests of central Victoria, AustraliaBiological Conservation, 95
(1992)
Effects of climate change on biological diversity in western North America : species losses and mechanisms
M. Austin, A. Nicholls, C. Margules (1990)
Measurement of the realized qualitative niche: environmental niches of five Eucalyptus speciesEcological Monographs, 60
R. Prentice (1976)
A generalization of the probit and logit methods for dose response curves.Biometrics, 32 4
(1975)
The temporal component of butterfly species diversity. Pages 181-195 in
(1997)
Metapopulation biology
I. Hanski (1994)
A Practical Model of Metapopulation DynamicsJournal of Animal Ecology, 63
G. Schwarz (1978)
Estimating the Dimension of a ModelAnnals of Statistics, 6
(1996)
BUGS 0 . 5 . Bayesian updating using Gibbs sampling
Brown Brown (1978)
The theory of insular biogeography and the distribution of boreal mammals and birds.Great Basin Naturalist Memoirs, 2
E. Fleishman, G. Austin, D. Murphy (2001)
Biogeography of Great Basin butterflies: revisiting patterns, paradigms, and climate change scenariosBiological Journal of The Linnean Society, 74
M. Loreau, S. Naeem, P. Inchausti, J. Bengtsson, J. Grime, A. Hector, D. Hooper, M. Huston, D. Raffaelli, B. Schmid, D. Tilman, D. Wardle (2001)
Biodiversity and Ecosystem Functioning: Current Knowledge and Future ChallengesScience, 294
E. Fleishman, J. Fay, D. Murphy (2000)
Upsides and downsides: contrasting topographic gradients in species richness and associated scenarios for climate changeJournal of Biogeography, 27
J. Prendergast, Rachel Quinn, John Lawton, B. Eversham, David Gibbons (1993)
Rare species, the coincidence of diversity hotspots and conservation strategiesNature, 365
David Adam (1994)
Living landscapes.Science, 263 5153
M. Warren, J. Hill, J. Hill, Jeremy Thomas, J. Asher, R. Fox, B. Huntley, D. Roy, M. Telfer, S. Jeffcoate, P. Harding, G. Jeffcoate, S. Willis, J. Greatorex-Davies, D. Moss, C. Thomas (2001)
Rapid responses of British butterflies to opposing forces of climate and habitat changeNature, 414
A. Pullin (1995)
Ecology and Conservation of Butterflies
E. Fleishman, G. Austin, D. Murphy (2001)
Regular ArticlesBiogeography of Great Basin butterflies: revisiting patterns, paradigms, and climate change scenariosBiological Journal of The Linnean Society, 74
F. Crome, Mervyn Thomas, L. Moore (1996)
A Novel Bayesian Approach to Assessing Impacts of Rain Forest LoggingEcological Applications, 6
Charles Riley, SmiCie GonjeCman, William Edwards, Second Series (2015)
THE BUTTERFLIES OF NORTH AMERICA.Science, 9 209S
T. Lawlor (1998)
Biogeography of great basin mammals: Paradigm lost?Journal of Mammalogy, 79
C. Thomas, H. Mallorie (1985)
Rarity, species richness and conservation: Butterflies of the Atlas Mountains in MoroccoBiological Conservation, 33
P. Harding, J. Asher, T. Yates (1995)
Butterfly monitoring 1 — recording the changes
(1986)
Diversity , rarity , and conservation in Mediterranean - climate regions
S. Germaine, B. Wakeling (2001)
Lizard species distributions and habitat occupation along an urban gradient in Tucson, Arizona, USABiological Conservation, 97
A. Moilanen (2000)
The equilibrium assumption in estimating the parameters of metapopulation models.Journal of Animal Ecology, 69
R. Nally, E. Fleishman, J. Fay, D. Murphy (2003)
Modelling butterfly species richness using mesoscale environmental variables: model construction and validation for mountain ranges in the Great Basin of western North AmericaBiological Conservation, 110
R. MacNally, A. Bennett (1997)
Species-specific predictions of the impact of habitat fragmentation: Local extinction of birds in the box-ironbark forests of central Victoria, AustraliaBiological Conservation, 82
M. Cowley, R. Wilson, J. León-Cortés, D. Gutiérrez, C. Bulman, C. Thomas (2000)
Habitat‐based statistical models for predicting the spatial distribution of butterflies and day‐flying moths in a fragmented landscapeJournal of Applied Ecology, 37
A. Moilanen (1999)
PATCH OCCUPANCY MODELS OF METAPOPULATION DYNAMICS: EFFICIENT PARAMETER ESTIMATION USING IMPLICIT STATISTICAL INFERENCEEcology, 80
P. Angermeier, M. Winston (1999)
characterizing fish community diversity across virginia landscapes: prerequisite for conservationEcological Applications, 9
G. Perry, J. Roughgarden (1997)
Anolis lizards of the Caribbean : ecology, evolution, and plate tectonicsCopeia, 1997
Mac Nally Mac Nally, Fleishman Fleishman, Fay Fay, Murphy Murphy (2002)
Modeling butterfly species richness using mesoscale environmental variables: model construction and validation.Biological Conservation, 111
C. Hawkins, R. Norris, J. Hogue, J. Feminella (2000)
DEVELOPMENT AND EVALUATION OF PREDICTIVE MODELS FOR MEASURING THE BIOLOGICAL INTEGRITY OF STREAMSEcological Applications, 10
I. Hanski, A. Moilanen, T. Pakkala, M. Kuussaari (1996)
The Quantitative Incidence Function Model and Persistence of an Endangered Butterfly MetapopulationConservation Biology, 10
C. Boggs, D. Murphy (1997)
Community Composition in Mountain Ecosystems: Climatic Determinants of Montane Butterfly Distributions, 6
Abstract: Ecologists often seek to predict species distributions as functions of abiotic environmental variables. Statistical models are useful for making predictions about the occurrence of species based on variables derived from remote sensing or geographic information systems. We previously used 14 topographically based environmental variables from 49 locations in the Toquima Range ( Nevada, U.S.A. ) and species inventories conducted over 4 years ( 1996–1999 ) to model logistically the occurrence of resident butterfly species. To test the models, we collected new validation data in 39 locations in the nearby Shoshone Mountains in 2000–2001. We used a series of “classification rules” based on conventional logistic and Bayesian criteria to assess the success rates of predictions. The classification rules represented a gradient of stringency in the “certainty” with which predictions were made. More stringent rules reduced the number of predictions made but greatly increased the success rate of predictions. For comparisons of classification rules making similar numbers of predictions, conventional logistic and Bayesian criteria produced similar outcomes. Success rates for predicted absences were uniformly higher than for predicted presences. Increasing the temporal extent of data from 1 to 2 years elevated success rates for predicted presences but decreased success rates for predicted absences, leaving the overall success rates essentially the same. Although species occurrence rates ( the proportion of locations in which each species was found ) were correlated between the modeling and validation data sets, occurrence rates for many species increased or decreased substantially; erroneous predictions were more likely for those taxa. Model fit ( measured by the explained deviance ) was an indicator of the probable success rate of predicted presences but not of predicted absences or overall success rates. We suggest that classification rules for predicting likely presences and absences may be decoupled to improve overall predictive success. Our general framework for modeling species occurrence is applicable to virtually any taxonomic group or ecosystem.
Conservation Biology – Wiley
Published: Jun 1, 2003
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.