Using Multi-Scale Sampling and Spatial Cross-Correlation to Investigate Patterns of Plant Species Richness

Land managers need better techniques to assess exoticplant invasions. We used the cross-correlationstatistic, I _YZ, to test for the presence ofspatial cross-correlation between pair-wisecombinations of soil characteristics, topographicvariables, plant species richness, and cover ofvascular plants in a 754 ha study site in RockyMountain National Park, Colorado, U.S.A. Using 25 largeplots (1000 m²) in five vegetation types, 8 of 12variables showed significant spatial cross-correlationwith at least one other variable, while 6 of 12variables showed significant spatial auto-correlation. Elevation and slope showed significant spatialcross-correlation with all variables except percentcover of native and exotic species. Percent cover ofnative species had significant spatialcross-correlations with soil variables, but not withexotic species. This was probably because of thepatchy distributions of vegetation types in the studyarea. At a finer resolution, using data from ten1 m² subplots within each of the 1000 m² plots, allvariables showed significant spatial auto- andcross-correlation. Large-plot sampling was moreaffected by topographic factors than speciesdistribution patterns, while with finer resolutionsampling, the opposite was true. However, thestatistically and biologically significant spatialcorrelation of native and exotic species could only bedetected with finer resolution sampling. We foundexotic plant species invading areas with high nativeplant richness and cover, and in fertile soils high innitrogen, silt, and clay. Spatial auto- andcross-correlation statistics, along with theintegration of remotely sensed data and geographicinformation systems, are powerful new tools forevaluating the patterns and distribution of native andexotic plant species in relation to landscape structure.     

Attributes of reliable long-term landscape-scale studies: Malpractice insurance for landscape ecologists

Monitoring long-term change in forested landscapes is an intimidating challenge with considerable practical, methodological, and theoretical limitations. Current field approaches used to assess vegetation change at the plot-to-stand scales and nationwide forest monitoring programs may not be appropriate at landscape scales. We emphasize that few vegetation monitoring programs (and, thus, study design models) are designed to detect spatial and temporal trends at landscape scales. Based primarily on advice from many sources, and trial and error, we identify 14 attributes of a reliable long-term landscape monitoring program: malpractice insurance for landscape ecologists. The attributes are to: secure long-term funding and commitment; develop flexible goals; refine objectives; pay adequate attention to information management; take an experimental approach to sampling design; obtain peer-review and statistical review of research proposals and publications; avoid bias in selection of long-term plot locations; insure adequate spatial replication; insure adequate temporal replication; synthesize retrospective, experimental, and related studies; blend theoretical and empirical models with the means to validate both; obtain periodic research program evaluation; integrate and synthesize with larger and smaller scale research, inventory, and monitoring programs; and develop an extensive outreach program. Using these 14 attributes as a guide, we describe one approach to assess the potential effect of global change on the vegetation of the Front Range of the Colorado Rockies. This self-evaluation helps identify strengthes and weaknesses in our program, and may serve the same role for other landscape ecologists in other programs.     

Spatial heterogeneity influences native and nonnative plant species richness

Spatial heterogeneity may have differential effects on the distribution of native and nonnative plant species richness. We examined the effects of spatial heterogeneity on native and nonnative plant species richness distributions in the central part of Rocky Mountain National Park, Colorado, USA. Spatial heterogeneity around vegetation plots was characterized using landscape metrics, environmental/topographic variables (slope, aspect, elevation, and distance from stream or river), and soil variables (nitrogen, clay, and sand). The landscape metrics represented five components of landscape heterogeneity and were measured at four spatial extents (within varying radii of 120, 240, 480, and 960 m) using the FRAGSTATS landscape pattern analysis program. Akaike's Information Criterion adjusted for small sample size (AICc) was used to select the best models from a set of multiple linear regression models developed for native and nonnative plant species richness at four spatial extents and three levels of ecological hierarchy (i.e., landscape, land cover, and community). Both native and nonnative plant species richness were positively correlated with edge density, Simpson's diversity index and interspersion/juxtaposition index, and were negatively correlated with mean patch size. The amount of variation explained at four spatial extents and three hierarchical levels ranged from 30% to 70%. At the landscape level, the best models explained 43% of the variation in native plant species richness and 70% of the variation in nonnative plant species richness (240-m extent). In general, the amount of variation explained was always higher for nonnative plant species richness, and the inclusion of landscape metrics always significantly improved the models. The best models explained 66% of the variation in nonnative plant species richness for both the conifer land cover type and lodgepole pine community. The relative influence of the components of spatial heterogeneity differed for native and nonnative plant species richness and varied with the spatial extent of analysis and levels of ecological hierarchy. The study offers an approach to quantify spatial heterogeneity to improve models of plant biodiversity. The results demonstrate that ecologists must recognize the importance of spatial heterogeneity in managing native and nonnative plant species.     

Rapid assessment of postfire plant invasions in coniferous forests of the western United States.

Fire is a natural part of most forest ecosystems in the western United States, but its effects on nonnative plant invasion have only recently been studied. Also, forest managers are engaging in fuel reduction projects to lessen fire severity, often without considering potential negative ecological consequences such as nonnative plant species introductions. Increased availability of light, nutrients, and bare ground have all been associated with high-severity fires and fuel treatments and are known to aid in the establishment of nonnative plant species. We use vegetation and environmental data collected after wildfires at seven sites in coniferous forests in the western United States to study responses of nonnative plants to wildfire. We compared burned vs. unburned plots and plots treated with mechanical thinning and/or prescribed burning vs. untreated plots for nonnative plant species richness and cover and used correlation analyses to infer the effect of abiotic site conditions on invasibility. Wildfire was responsible for significant increases in nonnative species richness and cover, and a significant decrease in native cover. Mechanical thinning and prescribed fire fuel treatments were associated with significant changes in plant species composition at some sites. Treatment effects across sites were minimal and inconclusive due to significant site and site x treatment interaction effects caused by variation between sites including differences in treatment and fire severities and initial conditions (e.g., nonnative species sources). We used canonical correspondence analysis (CCA) to determine what combinations of environmental variables best explained patterns of nonnative plant species richness and cover. Variables related to fire severity, soil nutrients, and elevation explained most of the variation in species composition. Nonnative species were generally associated with sites with higher fire severity, elevation, percentage of bare ground, and lower soil nutrient levels and lower canopy cover. Early assessments of postfire stand conditions can guide rapid responses to nonnative plant invasions.     

The invasion paradox: reconciling pattern and process in species invasions

The invasion paradox describes the co-occurrence of independent lines of support for both a negative and a positive relationship between native biodiversity and the invasions of exotic species. The paradox leaves the implications of native-exotic species richness relationships open to debate: Are rich native communities more or less susceptible to invasion by exotic species? We reviewed the considerable observational, experimental, and theoretical evidence describing the paradox and sought generalizations concerning where and why the paradox occurs, its implications for community ecology and assembly processes, and its relevance for restoration, management, and policy associated with species invasions. The crux of the paradox concerns positive associations between native and exotic species richness at broad spatial scales, and negative associations at fine scales, especially in experiments in which diversity was directly manipulated. We identified eight processes that can generate either negative or positive native-exotic richness relationships, but none can generate both. As all eight processes have been shown to be important in some systems, a simple general theory of the paradox, and thus of the relationship between diversity and invasibility, is probably unrealistic. Nonetheless, we outline several key issues that help resolve the paradox, discuss the difficult juxtaposition of experimental and observational data (which often ask subtly different questions), and identify important themes for additional study. We conclude that natively rich ecosystems are likely to be hotspots for exotic species, but that reduction of local species richness can further accelerate the invasion of these and other vulnerable habitats.     

Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado. Our objectives were to compare model results and accuracy between those developed locally and extrapolated regionally, those developed regionally and extrapolated locally, and to evaluate extending species distribution modeling from predicting the probability of presence to predicting abundance. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We compared model predicted mean and range abundance values to observed values between local and regional. We also evaluated model prediction performance based on Pearson's correlation coefficient. We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.     

Assessing vulnerability to invasion by nonnative plant species at multiple spatial scales

Basic information on where nonnative plant species have successfully invaded is lacking. We assessed the vulnerability of 22 vegetation types (25 sets of four plots in nine study areas) to nonnative plant invasions in the north–central United States. In general, habitats with high native species richness were more heavily invaded than species-poor habitats, low-elevation areas were more invaded than high-elevation areas, and riparian zones were more invaded than nearby upland sites. For the 100 1000-m² plots (across all vegetation types), 50% of the variation in nonnative species richness was explained by longitude, latitude, native plant species richness, soil total percentage nitrogen, and mean maximum July temperature (n = 100 plots; P < 0.001). At the vegetation-type scale (n = 25 sets of four 1000-m² plots/type), 64% of the variation in nonnative species richness was explained by native plant species richness, elevation, and October to June precipitation (P < 0.001). The foliar cover of nonnative species (log) was strongly positively correlated with the nonnative species richness at the plot scale (r = 0.77, P < 0.001) and vegetation-type scale (r = 0.83, P < 0.001). We concluded that, at the vegetation-type and regional scales in the north–central United States, (1) vegetation types rich in native species are often highly vulnerable to invasion by nonnative plant species; (2) where several nonnative species become established, nonnative species cover can substantially increase; (3) the attributes that maintain high native plant species richness (high light, water, nitrogen, and temperatures) also help maintain nonnative plant species richness; and (4) more care must be taken to preserve native species diversity in highly vulnerable habitats.     

Effects of herbage removal on productivity of selected high-sierra meadow community types

We investigated the effects of herbage removal on three subalpine meadow plant communities in the Rock Creek drainage of Sequoia National Park, California, USA. In the xericCarex exserta Mkze. (short-hair sedge) type, annual aboveground productivity averaged 19 g/m² in control plots (clipped once after plant senescence in late September) over a five-year period. Annual aboveground productivity was enhanced about 30%–35% when plots in this community type were clipped more frequently (i.e., additional herbage removal in the early, mid, and late seasons) during each of four treatment years but was reduced by 13%–19% during a fifth (recovery) year in which all but late September clipping was suspended. In a moderately mesicEleocharis pauciflora (Lightf.) Link. (few-flowered spike rush)-Calamagrostis breweri Thurb. (short-hair grass) type, control plot productivity averaged 115 g/m²/yr and was reduced by 20–30% by the additional herbage removal. A more mesicDeschampsia caespitosa (L.) Beauv. (tufted hairgrass)-Carex rostrata Stokes, (beaked sedge) type had the greatest mean above-ground productivity (169 g/m²/yr) but also showed damage (i.e., decrease in productivity by 15%–20%) caused by the additional herbage removal. These data suggest that longterm, intensive herbage removal may be more detrimental to moderately mesic and mesic subalpine meadow community types than to xeric types.     

Establishing backcountry use quotas: An example from Mineral King,California

Increasing levels of visitor use and consequent resource damage have necessitated that backcountry use restrictions be established in the Mineral King area of Sequoia National Park, California. In this paper we review the steps taken in developing a trailhead quota system. The availability of acceptable campsites, based on a detailed inventory of site distribution and impact, was used to quantitatively derive use capacities for each camp area. Wilderness permit data on visitor dispersal patterns from the major trailheads, including length of stay at each camp area, were then used to translate the area capacities into daily trailhead quotas that would assure that these capacities were not surpassed. The general approach is applicable to any backcountry area, although large complex areas may require the use of available computer simulation models.