3 years ago

Landscape capability models as a tool to predict fine-scale forest bird occupancy and abundance

Brian W. Rolek, Petra B. Wood, Zachary G. Loman, Cynthia S. Loftin, William V. Deluca, Daniel J. Harrison



Species-specific models of landscape capability (LC) can inform landscape conservation design. Landscape capability is “the ability of the landscape to provide the environment […] and the local resources […] needed for survival and reproduction […] in sufficient quantity, quality and accessibility to meet the life history requirements of individuals and local populations.” Landscape capability incorporates species’ life histories, ecologies, and distributions to model habitat for current and future landscapes and climates as a proactive strategy for conservation planning.


We tested the ability of a set of LC models to explain variation in point occupancy and abundance for seven bird species representative of spruce-fir, mixed conifer-hardwood, and riparian and wooded wetland macrohabitats.


We compiled point count data sets used for biological inventory, species monitoring, and field studies across the northeastern United States to create an independent validation data set. Our validation explicitly accounted for underestimation in validation data using joint distance and time removal sampling.


Blackpoll warbler (Setophaga striata), wood thrush (Hylocichla mustelina), and Louisiana (Parkesia motacilla) and northern waterthrush (P. noveboracensis) models were validated as predicting variation in abundance, although this varied from not biologically meaningful (1%) to strongly meaningful (59%). We verified all seven species models [including ovenbird (Seiurus aurocapilla), blackburnian (Setophaga fusca) and cerulean warbler (Setophaga cerulea)], as all were positively related to occupancy data.


LC models represent a useful tool for conservation planning owing to their predictive ability over a regional extent. As improved remote-sensed data become available, LC layers are updated, which will improve predictions.

Publisher URL: https://link.springer.com/article/10.1007/s10980-017-0582-z

DOI: 10.1007/s10980-017-0582-z

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