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Predicting the Effects of Climate Change on Migratory Birds and Bats:

Sector: Car parking • Location: United States of America

Source: Grants.gov

Project
Archived

There is an ongoing effort by a diverse group of academic, state/provincial, and federal collaborators to categorize the movement of migratory bats and birds across the Gulf of Maine as a function of weather, habitat characteristics, distance from the mainland, and relative location within the Gulf. The end result of this large project will be the development of a predictive model of animal movem

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The project “Predicting the Effects of Climate Change on Migratory Birds and Bats:” is an infrastructure initiative in the Car parking sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

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archived

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Description

Description

There is an ongoing effort by a diverse group of academic, state/provincial, and federal collaborators to categorize the movement of migratory bats and birds across the Gulf of Maine as a function of weather, habitat characteristics, distance from the mainland, and relative location within the Gulf. The end result of this large project will be the development of a predictive model of animal movement under a variety of climate change scenarios. Specific objectives of this opportunity are as follows. 1) To establish a station within Acadia National Park for the systematic monitoring of bird and bat migration within the Gulf of Maine using capture (mist-netting), transect surveys (diurnal), and acoustic recordings (nocturnal). 2) To design a long-term monitoring protocol that will allow Park Staff to assess the importance of the location and habitat characteristics of federal holdings within the Gulf of Maine for migratory species. 3) To implement a monitoring protocol that will allow Park Staff to track changes in far-shore versus near-shore versus mainland migration due to yearly variation and future climate change. This research is to be done collaboratively between the University cooperator and federal agency scientists and natural resource managers.

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High

Data quality score

100%

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