A risk-based approach to evaluating wildlife demographics for management in a changing climate: a case study of the Lewis's Woodpecker.

Given the projected threat that climate change poses to biodiversity, the need for proactive response efforts is clear. However, integrating uncertain climate change information into conservation planning is challenging, and more explicit guidance is needed. To this end, this article provides a specific example of how a risk-based approach can be used to incorporate a species' response to climate into conservation decisions. This is shown by taking advantage of species' response (i.e., impact) models that have been developed for a well-studied bird species of conservation concern. Specifically, we examine the current and potential impact of climate on nest survival of the Lewis's Woodpecker (Melanerpes lewis) in two different habitats. To address climate uncertainty, climate scenarios are developed by manipulating historical weather observations to create ensembles (i.e., multiple sequences of daily weather) that reflect historical variability and potential climate change. These ensembles allow for a probabilistic evaluation of the risk posed to Lewis's Woodpecker nest survival and are used in two demographic analyses. First, the relative value of each habitat is compared in terms of nest survival, and second, the likelihood of exceeding a critical population threshold is examined. By embedding the analyses in a risk framework, we show how management choices can be made to be commensurate with a defined level of acceptable risk. The results can be used to inform habitat prioritization and are discussed in the context of an economic framework for evaluating trade-offs between management alternatives.

Simulating ensembles of source water quality using a K-nearest neighbor resampling approach.

Climatological, geological, and water management factors can cause significant variability in surface water quality. As drinking water quality standards become more stringent, the ability to quantify the variability of source water quality becomes more important for decision-making and planning in water treatment for regulatory compliance. However, paucity of long-term water quality data makes it challenging to apply traditional simulation techniques. To overcome this limitation, we have developed and applied a robust nonparametric K-nearest neighbor (K-nn) bootstrap approach utilizing the United States Environmental Protection Agency's Information Collection Rule (ICR) data. In this technique, first an appropriate "feature vector" is formed from the best available explanatory variables. The nearest neighbors to the feature vector are identified from the ICR data and are resampled using a weight function. Repetition of this results in water quality ensembles, and consequently the distribution and the quantification of the variability. The main strengths of the approach are its flexibility, simplicity, and the ability to use a large amount of spatial data with limited temporal extent to provide water quality ensembles for any given location. We demonstrate this approach by applying it to simulate monthly ensembles of total organic carbon for two utilities in the U.S. with very different watersheds and to alkalinity and bromide at two other U.S. utilities.