Nitrogen and phosphorus pool sizes, distribution, and cycling rates were described and compared for six different ecosystem types occurring along a single toposequence in northern Alaska. The toposequence was located on a series of old floodplains of the Sagavanirktok River, in the northern foothills of the Brooks Range. From tussock tundra in the uplands, the toposequence passed through a relatively dry hilltop heath zone, a hillslope shrub/lupine/Cassiope zone, a footslope Equisetum zone, a wet sedge tundra, and a riparian shrub zone. A late—melting snowbank covered the hillslope site in early June of each year, and the sites consistently varied in soil temperature, soil moisture, thaw depth, and the seasonal pattern of soil thaw. The standing stocks of N, P, and C in soils of these six ecosystem types varied dramatically but not monotonically along the toposequence, as did the turnover rates of these elements. Several measures were used in comparisons of N and P availability, including soil solution concentrations, in situ accumulation on ion—exchange resins, and levels of KCI—extractable N and P. Annual rates of net N mineralization were assayed using a buried bag method, and ecosystem respiration was measured by trapping CO2 in soda lime [NaOH + Ca (OH)2]. Soil P pools were characterized by sequential extraction methods into four major pools, including loosely bound P, Al— and Fe—bound P, primary mineral P, and organic P. Both N and P availability were low in all six ecosystems when compared with temperate forests or wetlands. Among ecosystems, however, there was considerable variation in the relative availability of N vs. P, and in the apparent relative importance of nitrate as a nitrogen source.

Abstract. The last decade has seen a dramatic increase in the use of species distribution models (SDMs) to characterize patterns of species' occurrence and abundance. Efforts to parameterize SDMs often create a tension between the quality and quantity of data available to fit models. Estimation methods that integrate both standardized and non-standardized data types offer a potential solution to the tradeoff between data quality and quantity. Recently several authors have developed approaches for jointly modeling two sources of data (one of high quality and one of lesser quality). We extend their work by allowing for explicit spatial autocorrelation in occurrence and detection error using a Multivariate Conditional Autoregressive (MVCAR) model and develop three models that share information in a less direct manner resulting in more robust performance when the auxiliary data is of lesser quality. We describe these three new approaches ("Shared," "Correlation," "Covariates") for combining data sources and show their use in a case study of the Brown-headed Nuthatch in the Southeastern U.S. and through simulations. All three of the approaches which used the second data source improved out-of-sample predictions relative to a single data source ("Single"). When information in the second data source is of high quality, the Shared model performs the best, but the Correlation and Covariates model also perform well. When the information quality in the second data source is of lesser quality, the Correlation and Covariates model performed better suggesting they are robust alternatives when little is known about auxiliary data collected opportunistically or through citizen scientists. Methods that allow for both data types to be used will maximize the useful information available for estimating species distributions.

The future health of ecosystems is arguably as dependent on urban sprawl as it is on human-caused climatic warming. Urban sprawl strongly impacts the urban ecosystems it creates and the natural and agro-ecosystems that it displaces and fragments. Here, we project urban sprawl changes for the next 50 years for the fast-growing Southeast U.S. Previous studies have focused on modeling population density, but the urban extent is arguably as important as population density per se in terms of its ecological and conservation impacts. We develop simulations using the SLEUTH urban growth model that complement population-driven models but focus on spatial pattern and extent. To better capture the reach of low-density suburban development, we extend the capabilities of SLEUTH by incorporating street-network information. Our simulations point to a future in which the extent of urbanization in the Southeast is projected to increase by 101% to 192%. Our results highlight areas where ecosystem fragmentation is likely, and serve as a benchmark to explore the challenging tradeoffs between ecosystem health, economic growth and cultural desires.