Spatial ecological processes and local factors predict the distribution and abundance of spawning by steelhead (Oncorhynchus mykiss) across a complex riverscape.

Processes that influence habitat selection in landscapes involve the interaction of habitat composition and configuration and are particularly important for species with complex life cycles. We assessed the relative influence of landscape spatial processes and local habitat characteristics on patterns in the distribution and abundance of spawning steelhead (Oncorhynchus mykiss), a threatened salmonid fish, across ∼15,000 stream km in the John Day River basin, Oregon, USA. We used hurdle regression and a multi-model information theoretic approach to identify the relative importance of covariates representing key aspects of the steelhead life cycle (e.g., site access, spawning habitat quality, juvenile survival) at two spatial scales: within 2-km long survey reaches (local sites) and ecological neighborhoods (5 km) surrounding the local sites. Based on Akaike's Information Criterion, models that included covariates describing ecological neighborhoods provided the best description of the distribution and abundance of steelhead spawning given the data. Among these covariates, our representation of offspring survival (growing-season-degree-days, °C) had the strongest effect size (7x) relative to other predictors. Predictive performances of model-averaged composite and neighborhood-only models were better than a site-only model based on both occurrence (percentage of sites correctly classified = 0.80±0.03 SD, 0.78±0.02 vs. 0.62±0.05, respectively) and counts (root mean square error = 3.37, 3.93 vs. 5.57, respectively). The importance of both temperature and stream flow for steelhead spawning suggest this species may be highly sensitive to impacts of land and water uses, and to projected climate impacts in the region and that landscape context, complementation, and connectivity will drive how this species responds to future environments.

Modelling dendritic ecological networks in space: an integrated network perspective.

Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2-D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within the context of stream ecology. Within this context, we summarise the key innovations of a new family of spatial statistical models that describe spatial relationships in DENs. Finally, we discuss how different network analyses may be combined to address more complex and novel research questions. While our main focus is streams, the taxonomy of network analyses is also relevant anywhere spatial patterns in both network and 2-D space can be used to explore the influence of multi-scale processes on biota and their habitat (e.g. plant morphology and pest infestation, or preferential migration along stream or road corridors).

A tale of four "carp": invasion potential and ecological niche modeling.

BACKGROUND: Invasive species are a serious problem in ecosystems, but are difficult to eradicate once established. Predictive methods can be key in determining which areas are of concern regarding invasion by such species to prevent establishment [1]. We assessed the geographic potential of four Eurasian cyprinid fishes (common carp, tench, grass carp, black carp) as invaders in North America via ecological niche modeling (ENM). These "carp" represent four stages of invasion of the continent (a long-established invader with a wide distribution, a long-established invader with a limited distribution, a spreading invader whose distribution is expanding, and a newly introduced potential invader that is not yet established), and as such illustrate the progressive reduction of distributional disequilibrium over the history of species' invasions. METHODOLOGY/PRINCIPAL FINDINGS: We used ENM to estimate the potential distributional area for each species in North America using models based on native range distribution data. Environmental data layers for native and introduced ranges were imported from state, national, and international climate and environmental databases. Models were evaluated using independent validation data on native and invaded areas. We calculated omission error for the independent validation data for each species: all native range tests were highly successful (all omission values <7%); invaded-range predictions were predictive for common and grass carp (omission values 8.8 and 19.8%, respectively). Model omission was high for introduced tench populations (54.7%), but the model correctly identified some areas where the species has been successful; distributional predictions for black carp show that large portions of eastern North America are at risk. CONCLUSIONS/SIGNIFICANCE: ENMs predicted potential ranges of carp species accurately even in regions where the species have not been present until recently. ENM can forecast species' potential geographic ranges with reasonable precision and within the short screening time required by proposed U.S. invasive species legislation.

Modeling the geographic distribution of Bacillus anthracis, the causative agent of anthrax disease, for the contiguous United States using predictive ecological [corrected] niche modeling.

The ecology and distribution of Bacillus anthracis is poorly understood despite continued anthrax outbreaks in wildlife and livestock throughout the United States. Little work is available to define the potential environments that may lead to prolonged spore survival and subsequent outbreaks. This study used the genetic algorithm for rule-set prediction modeling system to model the ecological niche for B. anthracis in the contiguous United States using wildlife and livestock outbreaks and several environmental variables. The modeled niche is defined by a narrow range of normalized difference vegetation index, precipitation, and elevation, with the geographic distribution heavily concentrated in a narrow corridor from southwest Texas northward into the Dakotas and Minnesota. Because disease control programs rely on vaccination and carcass disposal, and vaccination in wildlife remains untenable, understanding the distribution of B. anthracis plays an important role in efforts to prevent/eradicate the disease. Likewise, these results potentially aid in differentiating endemic/natural outbreaks from industrial-contamination related outbreaks or bioterrorist attacks.

Niche differentiation and fine-scale projections for Argentine ants based on remotely sensed data.

Modeling ecological niches of species is a promising approach for predicting the geographic potential of invasive species in new environments. Argentine ants (Linepithema humile) rank among the most successful invasive species: native to South America, they have invaded broad areas worldwide. Despite their widespread success, little is known about what makes an area susceptible--or not--to invasion. Here, we use a genetic algorithm approach to ecological niche modeling based on high-resolution remote-sensing data to examine the roles of niche similarity and difference in predicting invasions by this species. Our comparisons support a picture of general conservatism of the species' ecological characteristics, in spite of distinct geographic and community contexts.