Development and evaluation of habitat suitability models for nesting white-headed woodpecker (Dryobates albolarvatus) in burned forest

Abstract

Salvage logging in burned forests can negatively affect habitat for white-headed woodpeckers (Dryobates albolarvatus), a species of conservation concern, but also meets socioeconomic demands for timber and human safety. Habitat suitability index (HSI) models can inform forest management activities to help meet habitat conservation objectives. Informing post-fire forest management, however, involves model application at new locations as wildfires occur, requiring evaluation of predictive performance across locations. We developed HSI models for white-headed woodpeckers using nest sites from two burned-forest locations in Oregon, the Toolbox (2002) and Canyon Creek (2015) fires. We measured predictive performance by developing one model at each of the two locations and quantifying discrimination of nest from reference sites at two other wildfire locations where the model had not been developed (either Toolbox or Canyon Creek, and the Barry Point Fire [2011]). We developed and evaluated Maxent models based on remotely sensed environmental metrics to support habitat mapping, and weighted logistic regression (WLR) models that combined remotely sensed and field-collected metrics to inform management prescriptions. Both Maxent and WLR models developed either at Canyon Creek or Toolbox performed adequately to inform management when applied at the alternate Toolbox or Canyon Creek location, respectively (area under the receiver-operating-characteristic curve [AUC] range = 0.61-0.72) but poorly when applied at Barry Point (AUC = 0.53-0.57). The final HSI models fitted to Toolbox and Canyon Creek data quantified suitable nesting habitat as severely burned or open sites adjacent to lower severity and closed canopy sites, where foraging presumably occurs. We suggest these models are applicable at locations similar to development locations but not at locations resembling Barry Point, which were characterized by more (pre-fire) canopy openings, larger diameter trees, less ponderosa pine (Pinus ponderosa), and more juniper (Juniperus occidentalis). Considering our results, we recommend

Behavioral state resource selection in invasive wild pigs in the Southeastern United States

“…All GLM models were computed using the glm function in R version 3.6.1 41 , 49 . We assessed how well the second order model explained the data using area under the receiver-operating characteristic curve (AUC 50 – 52 ), which we computed using the pROC package in R version 3.6.1 41 , 53 . A value of 0.5 indicates the model provides predictions that are no better than random predictions, but values greater than 0.7 indicate a better model fit with more accurate predictions 51 .…”

Adapting Research, Management, and Governance to Confront Socioecological Uncertainties in Novel Ecosystems

“…These two programs (CFLRP and ASCC) represent a paradigm shift in National Forest policy and in developing successful adaptive learning approaches to help guide management in the future (Schultz et al, 2012;Urgenson et al, 2017). Combined with the modeling and targeted experimental research efforts discussed above they provide further opportunities to explore the co-production of long-term research in conjunction with the required monitoring (e.g., Latif et al, 2015Latif et al, , 2020Saab et al, 2019). These programs make significant moves toward including diverse participation in (1) identifying and setting the direction of information needs to support decision making as well as (2) involving stakeholders in the execution of the work and syntheses.…”

“…Salvage logging is often litigated over concerns regarding negative effects on aquatic and terrestrial ecosystems, and on wildlife associated with recent disturbance (e.g., Lindenmayer and Noss, 2006;Saab et al, 2009;Hutto et al, 2016 are strongly associated with recently burned or beetle-killed forests because forest openings and snags provide critical nesting, perching, and foraging resources. Habitat suitability index (HSI) models for these woodpeckers were developed from nest location and associated environmental variables (Saab et al, 2011(Saab et al, , 2019Latif et al, 2013Latif et al, , 2015Latif et al, , 2020 to map habitat for wildlife. A GIS decision-support tool (FIRE-BIRD) provides habitat suitability maps that inform treatment designs to maximize habitat suitability and minimize negative effects to woodpecker SCC for a single fire or proposed management activity, while accounting for overall habitat in a surrounding national forest (Latif et al, 2018).…”

Connecting the Dots: Behavioral State Resource Selection in Wild Pigs in the Southeast United States

“…All GLM models were computed using the lme4 package in R version 3.6.1 [36,41]. We assessed how well the second order model explained the data using area under the receiver-operating characteristic curve (AUC; [42][43][44]), which we computed using the pROC package in R version 3.6.1 [36,45]. A value of 0.5 indicates the model provides predictions that are no better than random predictions, but values greater than 0.7 indicate a better model t with more meaningful predictions [43].…”

“…We created a global model including all covariates for each sex in each behavioral state in each season (i.e., 2 sexes*3 behavioral states*2 seasons = 12 RSFs). As with our second-order analyses, we did not use a model selection technique so that we could more directly compare estimates across behavioral states, sexes, and seasons [40], and we used AUC to assess how well the model explained the data [42][43][44].…”