Assessment Indices of Littoral Habitat Condition for Lakes in Maine and New England, USA.

National Lake Assessment littoral habitat assessments physical habitat surveys shoreland development water quality standards

Journal

Lake and reservoir management
ISSN: 1040-2381
Titre abrégé: Lake Reserv Manag
Pays: United States
ID NLM: 101570555

Informations de publication

Date de publication:
10 Jul 2023
Historique:
pmc-release: 10 07 2024
medline: 16 11 2023
pubmed: 16 11 2023
entrez: 16 11 2023
Statut: ppublish

Résumé

Littoral habitat is critical for lake biota but is adversely affected by residential shoreland development through the loss and reduced structural complexity of lakeshore vegetation. There currently exists no assessment methodology for evaluating littoral habitat condition of individual lakes in northeastern US. We addressed this assessment need by creating multi-metric indices of littoral habitat condition that focus on lakeshore residential development as the primary stressor. We did this by using habitat metrics derived primarily from National Lake Assessment (NLA) Physical Habitat (PHAB) survey field observations to create Linear Discriminant Analysis (LDA) models that assign lakeshore stations into littoral habitat condition categories. Lake PHAB survey data were used from New England NLA surveys as well as state-level surveys completed in Maine, New Hampshire, and Vermont. Prediction success rates in New England models averaged 83%. The Maine LDA models, which used finer scale survey methods, had an average prediction success rate of 89%. We used 95% bootstrapped confidence intervals to make assessment designations of natural (meeting reference quality), diminished (not meeting reference quality), or intermediate (existing between natural and diminished) littoral habitat condition for each lake. Our results show that efficacious single-lake littoral habitat assessments may be completed within the framework of NLA PHAB methodology, but confidence in assessment results, and therefore better-informed management decisions, can be improved with finer-scale observation data.

Identifiants

pubmed: 37969555
doi: 10.1080/10402381.2023.2207490
pmc: PMC10642257
mid: NIHMS1919738
doi:

Types de publication

Journal Article

Langues

eng

Pagination

141-155

Subventions

Organisme : Intramural EPA
ID : EPA999999
Pays : United States

Déclaration de conflit d'intérêts

The authors report there are no competing interests to declare. Our manuscript was subjected to review by the U.S. Environmental Protection Agency National Health and Environmental Effects Research Laboratory’s Pacific Ecological Systems Division and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.

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Auteurs

Jeremy Deeds (J)

The Maine Department of Environmental Protection, Augusta, Maine 04333.

Aria Amirbahman (A)

Department of Civil, Environmental and Sustainable Engineering, Santa Clara University, Santa Clara, California 95053.

Kirsten Hugger (K)

The New Hampshire Department of Environmental Services, Concord, NH 03302.

Philip R Kaufmann (PR)

US Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, Pacific Ecological Systems Division, Corvallis, OR, and Department of Fisheries, Wildlife and Conservation Sciences, Oregon State University, Corvallis, Oregon 97331, USA.

Leslie J Matthews (LJ)

The Vermont Department of Environmental Conservation, Montpelier, VT 05620.

Kellie Merrell (K)

The Vermont Department of Environmental Conservation, Montpelier, VT 05620.

Stephen A Norton (SA)

School of Earth and Climate Sciences, University of Maine, Orono, Maine 04469.

Classifications MeSH