Large-scale disturbance, namely wildfire and bark beetle outbreaks, are central to forest ecosystem function. Human land use and climate change have altered the frequency and severity of disturbance in lower elevation forests, and forest management plans are now largely aimed at mitigating the unintended consequences of these alterations. Forest management plans must balance often conflicting demands for resource extraction, the safety of human lives and property, and legal mandates to maintain wildlife populations that depend on forested habitats.
Of these demands, wildlife population data are expensive to obtain and often demanded by public interests than economic or sociological data. This lack of wildlife population information is often the main reason for litigation, which is costly and hinders agency effectiveness towards meeting management goals. Tools are therefore needed to maximize translation of available data into usable information on forest wildlife populations.
Recent advances in computing power has accelerated development of statistical modeling tools for analyzing complex, often sparse, and minimally informative population data (e.g., occupancy models, species distribution models). Adaptation of these tools towards quantifying population responses to natural disturbance and forest management activities provides critical information for guiding agency decisions and planning, particularly if accompanied with rigorous evaluation of resulting inferences and predictions.
Several woodpecker species of conservation concern rely on disturbed forest habitats and are sparsely distributed, making population data difficult to obtain. Disturbance-associated woodpeckers are useful focal species for developing analytic tools for monitoring wildlife population responses to large-scale disturbance and related forest management activities.
Evaluate model-based predictions with independent data during various time periods relevant to management decisions (i.e., pre-disturbance, post-disturbance, and following proposed management activities).
Develop tools to translate available models into predictions relevant to specific projects.
Evaluate the validity of ecological inferences derived from analytic models.
Statistical modeling – Apply contemporary modeling techniques (e.g., Maxent, Mahalanobis D2, weighted logistic regression, occupancy models) to analyze and quantify environmental relationships with species location data (e.g., woodpecker nest location data, bird community survey data), allowing model-based, data-driven predictions of where woodpeckers and other bird species of conservation concern will most likely occur and breed successfully.
Model evaluation – Use independent data and computer simulations to verify the accuracy of model-based predictions and inferences. For example, the predictive value of a model developed in one area can be compared against data on breeding individuals in other areas outside where the model was originally developed. Additionally, hypothetical populations with specified responses to natural disturbance or proposed management can be simulated along with surveys of these populations. Subsequently, the simulated data can be analyzed and resulting inferences compared with known population responses upon which simulations were based.
Develop GIS-based tools and an accompanying manual to allow managers or resource specialists with minimal technical expertise to generate predictive habitat maps using published habitat suitability models for management plans aimed at specific project areas.
Analytic tools whose conclusions have been rigorously verified will provide critical information for forest management plans making them less prone to costly litigation over concerns about wildlife habitat conservation. Additionally, technology transfer tools will make model-based inferences and predictions generated from these tools readily available to agency personnel. Finally, by developing informative analytic methods, additional data collection will become more cost effective and attractive to funding agencies, leading to even better information for forest management planning.