A critical challenge for the Earth sciences is to trace the transport and flux of matter within and among aquatic, terrestrial, and atmospheric systems. Robust descriptions of isotopic patterns across space and time, called “isoscapes,” form the basis of a rapidly growing and wide‐ranging body of research aimed at quantifying connectivity within and among Earth's systems. However, isoscapes of rivers have been limited by conventional Euclidean approaches in geostatistics and the lack of a quantitative framework to apportion the influence of processes driven by landscape features versus in‐stream phenomena. Here we demonstrate how dendritic network models substantially improve the accuracy of isoscapes of strontium isotopes and partition the influence of hydrologic transport versus local geologic features on strontium isotope ratios in a large Alaska river. This work illustrates the analytical power of dendritic network models for the field of isotope biogeochemistry, particularly for provenance studies of modern and ancient animals.

We evaluated habitat suitability and nest survival of breeding white‐headed woodpeckers (Picoides albolarvatus) in unburned forests of central Oregon, USA. Daily nest‐survival rate was positively related to maximum daily temperature during the nest interval and to density of large‐diameter trees surrounding the nest tree. We developed a niche‐based habitat suitability model (partitioned Mahalanobis distance) for nesting white‐headed woodpeckers using remotely sensed data. Along with low elevation, high density of large trees, and low slope, our habitat suitability model suggested that interspersion–juxtaposition of low‐ and high‐canopy cover ponderosa pine (Pinus ponderosa) patches was important for nest‐site suitability. Cross‐validation suggested the model performed adequately for management planning at a scale >1 ha. Evaluation of mapped habitat suitability index (HSI) suggested that the maximum predictive gain (HSI = 0.36), where the number of nest locations are maximized in the smallest proportion of the modeled landscape, provided an objective initial threshold for identification of suitable habitat. However, managers can choose the threshold HSI most appropriate for their purposes (e.g., locating regions of low–moderate suitability that have potential for habitat restoration). Consequently, our habitat suitability model may be useful for managing dry coniferous forests for white‐headed woodpeckers in central Oregon; however, model validation is necessary before our model could be applied to other locations. © 2011 The Wildlife Society.

To conserve habitat for disturbance specialist species, ecologists must identify where individuals will likely settle in newly disturbed areas. Habitat suitability models can predict which sites at new disturbances will most likely attract specialists. Without validation data from newly disturbed areas, however, the best approach for maximizing predictive accuracy can be unclear (Northwestern U.S.A.). We predicted habitat suitability for nesting Black-backed Woodpeckers (Picoides arcticus; a burned-forest specialist) at 20 recently (≤6 years postwildfire) burned locations in Montana using models calibrated with data from three locations in Washington, Oregon, and Idaho. We developed 8 models using three techniques (weighted logistic regression, Maxent, and Mahalanobis D2 models) and various combinations of four environmental variables describing burn severity, the north–south orientation of topographic slope, and prefire canopy cover. After translating model predictions into binary classifications (0 = low suitability to unsuitable, 1 = high to moderate suitability), we compiled “ensemble predictions,” consisting of the number of models (0–8) predicting any given site as highly suitable. The suitability status for 40% of the area burned by eastside Montana wildfires was consistent across models and therefore robust to uncertainty in the relative accuracy of particular models and in alternative ecological hypotheses they described. Ensemble predictions exhibited two desirable properties: (1) a positive relationship with apparent rates of nest occurrence at calibration locations and (2) declining model agreement outside surveyed environments consistent with our reduced confidence in novel (i.e., “no-analogue”) environments. Areas of disagreement among models suggested where future surveys could help validate and refine models for an improved understanding of Black-backed Woodpecker nesting habitat relationships. Ensemble predictions presented here can help guide managers attempting to balance salvage logging with habitat conservation in burned-forest landscapes where black-backed woodpecker nest location data are not immediately available. Ensemble modeling represents a promising tool for guiding conservation of large-scale disturbance specialists.

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