Modeling marbled murrelet (Brachyramphus marmoratus) habitat using LiDAR‐derived canopy data

Abstract

LiDAR (Light Detection And Ranging) is an emerging remote-sensing tool that can provide fine-scale data describing vertical complexity of vegetation relevant to species that are responsive to forest structure. We used LiDAR data to estimate occupancy probability for the federally threatened marbled murrelet (Brachyramphus marmoratus) in the Oregon Coast Range of the United States. Our goal was to address the need identified in the Recovery Plan for a more accurate estimate of the availability of nesting habitat by developing occupancy maps based on refined measures of nest-strand structure. We used murrelet occupancy data collected by the Bureau of Land Management Coos Bay District, and canopy metrics calculated from discrete return airborne LiDAR data, to fit a logistic regression model predicting the probability of occupancy. Our final model for stand-level occupancy included distance to coast, and 5 LiDAR-derived variables describing canopy structure. With an area under the curve value (AUC) of 0.74, this model had acceptable discrimination and fair agreement (Cohen's k ¼ 0.24), especially considering that all sites in our sample were regarded by managers as potential habitat. The LiDAR model provided better discrimination between occupied and unoccupied sites than did a model using variables derived from Gradient Nearest Neighbor maps that were previously reported as important predictors of murrelet occupancy (AUC ¼ 0.64, k ¼ 0.12). We also evaluated LiDAR metrics at 11 known murrelet nest sites. Two LiDAR-derived variables accurately discriminated nest sites from random sites (average AUC ¼ 0.91). LiDAR provided a means of quantifying 3-dimensional canopy structure with variables that are ecologically relevant to murrelet nesting habitat, and have not been as accurately quantified by other mensuration methods. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.KEY WORDS Brachyramphus marmoratus, forest structure, habitat model, LiDAR, marbled murrelet, remote sensing.

The Reliability and Application of Methods Used to Predict Suitable Nesting Habitat for Marbled Murrelets

“…Future versions of the BC Model might therefore become more reliable as VRI is updated. Both VRI and the BC Model might be improved further with the application of new technologies, such as Light Detection and Ranging (LiDAR), which is currently being tested for identifying and mapping Marbled Murrelet forest habitat (Clyde 2017, Hagar et al 2014.…”

High‐resolution habitat modeling with airborne LiDAR for red tree voles

“…Predictors of basal area or dbh can be estimated by LiDAR, but we did not test any because of their allometric derivation from canopy heights. Habitat models derived from LiDAR provide significant advantages over existing models constructed from ground data or imputation techniques (Hagar et al 2014, Ackers et al 2015. Because LiDAR measures are continuous across the areas of acquisition, the LiDAR-derived habitat model can be used directly to estimate suitability throughout the entire area.…”

“…Furthermore, multi‐scale analyses important to understanding habitat requirements are rarely performed with field data because data collection is laborious (Wiens , Scott et al , Seavy et al ). Although modeling with remotely sensed data can facilitate multi‐scale analyses and straightforward mapping of habitat suitability, the resolution and types of habitat information are sometimes too coarse for biologically meaningful analyses or local‐level planning (Ohmann and Gregory , Hagar et al , Ackers et al ).…”

Modeling Marbled Murrelet nesting habitat: a quantitative approach using airborne laser scanning data in British Columbia, Canada

“…Airborne laser scanning captures a range of structural features important to the ecophysiological needs of birds, enabling ecologically relevant HSM for species such as the Northern Spotted Owl (Strix occidentalis caurina; Ackers et al 2015, Hagar et al 2020. Hagar et al (2014) demonstrated the feasibility of ALS informed HSM for murrelets, with canopy cover, tree height, structural complexity, and distance to the coast used as predictors in a binomial regression model that successfully discriminated murrelet occupancy among forest stands in Oregon. However, this model cannot be replicated in BC due to the lack of reliable absence data and latitudinal differences in forest composition and structure.…”