Influence of the temporal resolution of data on the success of indicator species models of species richness across multiple taxonomic groups

Influence of the temporal resolution of data on the success of indicator species models of... Indicator species models may be a cost-effective approach to estimating species richness across large areas. Obtaining reliable distributional data for indicator species (and therefore reliable estimates of species richness) often requires longitudinal data, that is, surveys for indicator species repeated for several years or time steps. Maximum information must be extracted from such data. We used genetic algorithms and a Bayesian approach to compare the influence of presence/absence data and reporting rate data (the proportion of survey years in which a species was present) on models of species richness based on indicator species. Using data on birds and butterflies from the Great Basin (Nevada, USA), we evaluated models of species richness for one taxonomic group based on indicator species drawn from the same taxonomic group and from a different group. We also evaluated models of combined species richness of both taxonomic groups based on indicator species drawn from either group. We identified suites of species whose occurrence patterns explained as much as 70% of deviance in species richness of a different taxonomic group. Validation tests revealed strong correlations between observed and predicted species richness, with 83–100% of the observed values falling within the 95% credible intervals of the predictions. Whether reporting rate data improved the explanatory and predictive ability of cross-taxonomic models depended on the taxonomic group of the indicator species. The discrepancy in predictive ability was smaller for same-taxon models. Our methods provide a manager with the means to maximize the information obtained from longitudinal survey data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biological Conservation Elsevier

Influence of the temporal resolution of data on the success of indicator species models of species richness across multiple taxonomic groups

Loading next page...
 
/lp/elsevier/influence-of-the-temporal-resolution-of-data-on-the-success-of-Z7VfjZX5PR
Publisher
Elsevier
Copyright
Copyright © 2005 Elsevier Ltd
ISSN
0006-3207
DOI
10.1016/j.biocon.2005.02.013
Publisher site
See Article on Publisher Site

Abstract

Indicator species models may be a cost-effective approach to estimating species richness across large areas. Obtaining reliable distributional data for indicator species (and therefore reliable estimates of species richness) often requires longitudinal data, that is, surveys for indicator species repeated for several years or time steps. Maximum information must be extracted from such data. We used genetic algorithms and a Bayesian approach to compare the influence of presence/absence data and reporting rate data (the proportion of survey years in which a species was present) on models of species richness based on indicator species. Using data on birds and butterflies from the Great Basin (Nevada, USA), we evaluated models of species richness for one taxonomic group based on indicator species drawn from the same taxonomic group and from a different group. We also evaluated models of combined species richness of both taxonomic groups based on indicator species drawn from either group. We identified suites of species whose occurrence patterns explained as much as 70% of deviance in species richness of a different taxonomic group. Validation tests revealed strong correlations between observed and predicted species richness, with 83–100% of the observed values falling within the 95% credible intervals of the predictions. Whether reporting rate data improved the explanatory and predictive ability of cross-taxonomic models depended on the taxonomic group of the indicator species. The discrepancy in predictive ability was smaller for same-taxon models. Our methods provide a manager with the means to maximize the information obtained from longitudinal survey data.

Journal

Biological ConservationElsevier

Published: Aug 1, 2005

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off