Iowa Cooperative Fish and Wildlife Research Unit

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A Comprehensive Biological Inventory Database for the Iowa Aquatic GAP Project

2004-01-01 , Kane, Kevin , McNeely, Robin , Pierce, Clay , Kane, Kevin , Pierce, Clay , Iowa Cooperative Fish and Wildlife Research Unit

Before the implementation of the Iowa Aquatic Gap Analysis, project coordinators had no sense of the breadth of biological sampling data available for fish. However, it was considered important to have the most extensive biological data set possible. We were able to systematically compile a fish inventory database that we believe satisfies this objective. Other Aquatic GAP projects may find themselves in a similar situation and thus benefit from our approach to compiling a comprehensive biological inventory database.

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An Aquatic Gap Analysis of Iowa, 2005 Final Report

2005-06-30 , Kane, Kevin , Pierce, Clay , Loan-Wilsey, Anna , Pierce, Clay , McNeely, Robin , Iowa Cooperative Fish and Wildlife Research Unit

The Iowa Aquatic Gap Analysis Project (IAGAP) began in 2001 to identify areas in the state where fish species richness lacked adequate protection under existing land ownership and management regimes. Another ma in goal of the project was to create fish prediction data for Iowa streams and rivers.

To accomplish these goals, the Iowa GAP team prepared an assortment of datasets that led to the creation of three main datasets: Iowa streams and rivers; Iowa fish habitat models for 157 species; Iowa land stewardship (ownership and management).

When the project began, there were few statewide datasets available that provided the type of data needed for this project. Conse quently, much effort was devoted to building the previously mentioned key data layers at a sufficiently fine scale and resolution for subsequent analysis. The exception to this statement was land stewardship; it had been created for the terrestrial GAP project. It need ed minimal editing to serve as a dataset for IAGAP. At the completion of the project, these data became freely available, with the intent that they will be used by those responsible for managing the state’s valuable natural resources, and by the public, so that everyone can be better informed. With this in mind, we emphasize that these data are dyna mic, and in some places, already out of date. Nonetheless, the data and analyses which constitute IAGAP represent an important first step toward understanding the status of fish biodiversity and conservation in Iowa.

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Assessment of Environmental Services of CREP Wetlands in Iowa and theMidwestern Corn Belt

2010-04-02 , Otis, David , Kane, Kevin , Crumpton, William , Green, David , Loan-Wilsey, Anna , McNeely, Robin , Kane, Kevin , Johnson, Rex , Cooper, Tom , Vandever, Mark , Iowa Cooperative Fish and Wildlife Research Unit

This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009.

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Final Report Summaries: Iowa Gap Analysis Project

2004-01-01 , Kane, Kevin , Kane, Kevin , Iowa Cooperative Fish and Wildlife Research Unit

The Iowa Gap Analysis Project (IA-GAP) began in 1997 to identify areas in the state where vertebrate species richness lacked adequate protection under existing land ownership and management regimes.

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Partnering in Great Plains Regional GAP

2000-01-01 , Kaufman, Glennis , Kane, Kevin , Cully, Jack , Henebry, Geoffrey , Jenks, Jonathan , Kane, Kevin , Klaas, Ervin , Merchant, James , Smith, Vickie , Strong, Larry , Iowa Cooperative Fish and Wildlife Research Unit

Partnering within the Great Plains states has been very important to the success of the Great Plains Regional GAP project. Over $3.5 million have been received in monetary and in-kind support from partners for the direct funding of basic layers of gap analysis as well as spin-offs of the GAP projects important to partners in the Great Plains (Table 1). Successes in basic gap analysis efforts would not have been possible without the direct and financial support of our many partners in the Great Plains. In addition to the financial aspects of partnering, contributions of partners have demonstrated their interest in our work and the value of creating high-quality, state-of-the-art products. Our partnering support, both in direct and in-kind financial aspects, also has stimulated several spin-off projects within the basic GAP effort, thereby facilitating future partnering with various agencies and organizations.

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A Gap Analysis of Iowa, 2003 Final Report

2004-01-26 , Kane, Kevin , Klaas, Erwin , Kane, Kevin , Andersen, Katherine , Brown, Patrick , McNeely, Robin , Iowa Cooperative Fish and Wildlife Research Unit

The Iowa Gap Analysis Project (IA GAP) began in 1997 to identify areas in the state where vertebrate species richness lacked adequate protection under existing land ownership and management regimes.

To accomplish this goal, the IA GAP team prepared an assortment of datasets that led to three main pieces of information: Iowa vegetation types; Iowa vertebrate/habitat relationship models for 288 species; Iowa land stewardship (ownership and management).

When the project began, there were few stat ewide datasets available that provided the type of data needed for this project. Conse quently, much effort was devoted to building the previously mentioned key da ta layers at a sufficiently fine scale and resolution for subsequent analysis. At the completion of the project, these data became freely available, with the intent that they will be used by those responsible for managing the state’s valuable natural resources, and by the public, so that every one can be better informed. With this in mind, we emphasize that these data are dynamic, and in some places, already out-of date. Nonetheless, the data and analyses that constitute IA GAP represent an important first step toward understanding the st atus of vertebrates and land cover in Iowa and planning for the conser vation of their biodiversity

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Miniature temperature data loggers increase precision and reduce bias when estimating the daily survival rate for bird nests

2021-12-20 , Niemi, Jarad , Schulte-Moore, Lisa , Klaver, Robert , Natural Resource Ecology and Management , Statistics , Iowa Cooperative Fish and Wildlife Research Unit

Demographic studies of many bird species are challenging because their nests are cryptic, resulting in few nests being found. To maximize statistical power, methods are needed that minimize disturbance while yielding as much information per nest as possible. One way to meet these objectives is to use miniature thermal data loggers to precisely date nest fates. Our objectives, therefore, were to (1) examine the possible effect of thermal data loggers on nest success through hatching by grass- and shrub-nesting songbirds that differed in their parasite egg-accepting and -rejecting behavior, (2) examine the effect of using daily temperature data versus less frequent nest-visit data on statistical power, bias, and precision when estimating the daily survival rate (DSR) for nests, and (3) compare these two approaches using a simulation study and field data. We monitored the survival of nests located in agricultural landscapes and used a binomial logistic regression with main effects for data-loggers and parasite-accepting or -rejecting status and their interaction. We also compared maximum likelihood–derived DSR for differences in estimated rates, precision, and sample sizes with both data collected in the field and simulated with varying sample sizes and visit frequencies. We found no evidence that thermal data loggers had any effect on hatching rates either for all species or for parasite egg-accepting and -rejecting species, separately. Both our simulation and analysis of real nest data indicated that use of data loggers increased the statistical power from each nest studied by increasing effective sample sizes and precision of DSR estimates compared to in-person visits. We also found a negative bias in DSR estimates with longer visit intervals, which use of data-loggers removed. Both the results of simulated- and field-data analyses suggest that future studies of nest survival can be improved by automated nest monitoring by removing a source of bias and providing more time to find additional nests.