Local spatial modeling of white-tailed deer distribution

Complex spatial heterogeneity of ecological systems is difficult to capture and interpret using global models alone. For this reason, recent attention has been paid to local spatial modeling techniques. We used one local modeling approach, geographically weighted regression (GWR), to investigate the effects of local spatial heterogeneity on multivariate relationships of white-tailed deer distribution using land cover patch metrics and climate factors. The results of these analyses quantify differences in the contributions of model parameters to estimates of deer density over space. A GWR model with local kernel bandwidth was compared to a GWR model with global kernel bandwidth and an ordinary least-squares regression (OLS) model with the same parameters to evaluate their relative abilities in modeling deer distributions. The results indicated that the GWR models predicted deer density better than the traditional ordinary least-squares model and also provided useful information regarding local environmental processes affecting deer distribution. GWR model comparisons showed that the local kernel bandwidth GWR model was more realistic than the global kernel bandwidth GWR model, as the latter exaggerated local spatial variation. The parameter estimates and model statistics (e.g., model R²) of the GWR models were mapped using geographic information systems (GIS) to illustrate local spatial variation in the regression relationship and to identify causes of large-scale model misspecifications and low estimation efficiencies.     

Seeing the forest for the heterogeneous trees: stand-scale resource distributions emerge from tree-scale structure

Forest ecosystem processes depend on local interactions that are modified by the spatial pattern of trees and resources. Effects of resource supplies on processes such as regeneration are increasingly well understood, yet we have few tools to compare resource heterogeneity among forests that differ in structural complexity. We used a neighborhood approach to examine understory light and nutrient availability in a well-replicated and large-scale variable-retention harvesting experiment in a red pine forest in Minnesota, USA. The experiment included an unharvested control and three harvesting treatments with similar tree abundance but different patterns of retention (evenly dispersed as well as aggregated retention achieved by cutting 0.1- or 0.3-ha gaps). We measured light and soil nutrients across all treatments and mapped trees around each sample point to develop an index of neighborhood effects (NI). Field data and simulation modeling were used to test hypotheses that the mean and heterogeneity of resource availability would increase with patchiness because of greater variation in competitive environments. Our treatments dramatically altered the types and abundances of competitive neighborhoods (NI) in each stand and resulted in significantly nonlinear relationships of light, nitrogen and phosphorus availability to NI. Hence, the distribution of neighborhoods in each treatment had a significant impact on resource availability and heterogeneity. In dense control stands, neighborhood variation had little impact on resource availability, whereas in more open stands (retention treatments), it had large effects on light and modest effects on soil nutrients. Our results demonstrate that tree spatial pattern can affect resource availability and heterogeneity in explainable and predictable ways, and that neighborhood models provide a useful tool for scaling heterogeneity from the individual tree to the stand. These insights are needed to anticipate the outcomes of silvicultural manipulations and should become more holistically integrated into both basic ecological and management science.     

A spatial approach to combatting wildlife crime

Poaching can have devastating impacts on animal and plant numbers, and in many countries has reached crisis levels, with illegal hunters employing increasingly sophisticated techniques. We used data from an 8‐year study in Savé Valley Conservancy, Zimbabwe, to show how geographic profiling—a mathematical technique originally developed in criminology and recently applied to animal foraging and epidemiology—can be adapted for use in investigations of wildlife crime. The data set contained information on over 10,000 incidents of illegal hunting and the deaths of 6,454 wild animals. We used a subset of data for which the illegal hunters’ identities were known. Our model identified the illegal hunters’ home villages based on the spatial locations of the hunting incidences (e.g., snares). Identification of the villages was improved by manipulating the probability surface inside the conservancy to reflect the fact that although the illegal hunters mostly live outside the conservancy, the majority of hunting occurs inside the conservancy (in criminology terms, commuter crime). These results combined with rigorous simulations showed for the first time how geographic profiling can be combined with GIS data and applied to situations with more complex spatial patterns, for example, where landscape heterogeneity means some parts of the study area are less likely to be used (e.g., aquatic areas for terrestrial animals) or where landscape permeability differs (e.g., forest bats tend not to fly over open areas). More broadly, these results show how geographic profiling can be used to target antipoaching interventions more effectively and more efficiently and to develop management strategies and conservation plans in a range of conservation scenarios.     

Use of Spatial Capture‐Recapture Modeling and DNA Data to Estimate Densities of Elusive Animals

Abstract: Assessment of abundance, survival, recruitment rates, and density (i.e., population assessment) is especially challenging for elusive species most in need of protection (e.g., rare carnivores). Individual identification methods, such as DNA sampling, provide ways of studying such species efficiently and noninvasively. Additionally, statistical methods that correct for undetected animals and account for locations where animals are captured are available to efficiently estimate density and other demographic parameters. We collected hair samples of European wildcat (Felis silvestris) from cheek‐rub lure sticks, extracted DNA from the samples, and identified each animals’ genotype. To estimate the density of wildcats, we used Bayesian inference in a spatial capture‐recapture model. We used WinBUGS to fit a model that accounted for differences in detection probability among individuals and seasons and between two lure arrays. We detected 21 individual wildcats (including possible hybrids) 47 times. Wildcat density was estimated at 0.29/km² (SE 0.06), and 95% of the activity of wildcats was estimated to occur within 1.83 km from their home‐range center. Lures located systematically were associated with a greater number of detections than lures placed in a cell on the basis of expert opinion. Detection probability of individual cats was greatest in late March. Our model is a generalized linear mixed model; hence, it can be easily extended, for instance, to incorporate trap‐ and individual‐level covariates. We believe that the combined use of noninvasive sampling techniques and spatial capture‐recapture models will improve population assessments, especially for rare and elusive animals.     

The scale and cause of spatial heterogeneity in strength of temporal density dependence

The importance of density dependence in natural communities continues to spark much debate because it is fundamental to population regulation. We used temporal manipulations of density to explore potentially stabilizing density dependence in early survivorship among six local populations of a tropical damselfish (Dascyllus flavicaudus). Specifically, we tested the premise that spatial heterogeneity in the strength of temporal density dependence would reflect variation in density of predators, the agent of mortality. Our field manipulations revealed that mortality among successive cohorts of young fishes was density dependent at each reef, but that its strength varied by approximately 1.5 orders of magnitude. This spatial heterogeneity was well predicted by variation among the six reefs in the density of predatory fishes that consume juvenile damselfishes. Because density dependence arose from competition for enemy-free space within a shelter coral, the mortality consequence of the competition depended on the neighborhood density of predators. Thus, the scale of heterogeneity in the density dependence largely reflected attributes of the environment that shaped the local abundance of predators. These results have important implications for how ecologists explore regulatory processes in nature. Failure to account for spatial variation could frequently yield misleading conclusions regarding density dependence as a stabilizing process, obscure underlying mechanisms influencing its strength, and provide no insight into the spatial scale of the heterogeneity. Further, models of population dynamics will be improved when experimental approaches better estimate the magnitude and causes of variation in strength of stabilizing density dependence.     

Spatiotemporal trends of illegal activities from ranger‐collected data in a Ugandan national park

Within protected areas, biodiversity loss is often a consequence of illegal resource use. Understanding the patterns and extent of illegal activities is therefore essential for effective law enforcement and prevention of biodiversity declines. We used extensive data, commonly collected by ranger patrols in many protected areas, and Bayesian hierarchical models to identify drivers, trends, and distribution of multiple illegal activities within the Queen Elizabeth Conservation Area (QECA), Uganda. Encroachment (e.g., by pastoralists with cattle) and poaching of noncommercial animals (e.g., snaring bushmeat) were the most prevalent illegal activities within the QECA. Illegal activities occurred in different areas of the QECA. Poaching of noncommercial animals was most widely distributed within the national park. Overall, ecological covariates, although significant, were not useful predictors for occurrence of illegal activities. Instead, the location of illegal activities in previous years was more important. There were significant increases in encroachment and noncommercial plant harvesting (nontimber products) during the study period (1999–2012). We also found significant spatiotemporal variation in the occurrence of all activities. Our results show the need to explicitly model ranger patrol effort to reduce biases from existing uncorrected or capture per unit effort analyses. Prioritization of ranger patrol strategies is needed to target illegal activities; these strategies are determined by protected area managers, and therefore changes at a site‐level can be implemented quickly. These strategies should also be informed by the location of past occurrences of illegal activity: the most useful predictor of future events. However, because spatial and temporal changes in illegal activities occurred, regular patrols throughout the protected area, even in areas of low occurrence, are also required.     

A preliminary assessment of socio-ecological metabolism for three neighborhoods within a rust belt urban ecosystem

Rust belt cities of the northeastern United States are plagued by flat or declining economies and the accompanying social fallout from lack of employment. Advocates of green fuels, green infrastructure and green jobs have proposed these nature-based technologies as means to revitalize the economies of cities. Before making public and private investment a baseline analysis of the relative magnitude of existing energy production and energy respiration at the neighborhood scale is useful in order to understand what the potential for green infrastructure might be. Because the urban canopy and other green infrastructure can enhance urban socio-ecological metabolism, we measured the flows of natural energy produced (P) by the urban forest versus the industrial or fossil energy currently consumed or respired (R) in three economically and demographically distinct neighborhoods of a typical rust-belt city, Syracuse, NY. Our objectives were to (1) understand the potential for green energy to replace fossil fuels in general, (2) assess the degree to which different socio-demographic communities are receiving the ecosystem benefits of existing urban “green” infrastructure (i.e. forest primary production), and (3) identify where local (in-city) biotic energy resources could be enhanced or fossil fuel consumption altered to improve overall urban socio-ecological metabolism. We found that (1) the fossil energy consumed in all three neighborhoods was 200-700 times higher than the biotic “green” energy produced; (2) that to produce this much energy from willow biomass grown in the region would require at least between 0.3 and 0.7 ha of bio-energy production per person depending on affluence, density of living, transportation mix and home fuel mix; (3) that although the more affluent neighborhood used, per residence and per person, almost twice as much energy as that of the downtown more densely settled and poorer neighborhood, its R:P ratio was still the lowest due to the high primary productivity of its neighborhood tree canopy. As a first assessment our findings identify several opportunities for enhancement of the socio-ecological metabolism of these neighborhoods, and the city at large, through conversion of heating units in poorer neighborhoods away from expensive electricity, and toward tree planting, solar installations, and per capita energy use reductions.     

Quantifying site quality in a heterogeneous landscape: recruitment of a reef fish

Heterogeneity in site quality can play an important role in patterns of abundance and population dynamics. Yet, estimating site quality in natural systems can be problematic because site quality can (1) vary through ontogeny for a focal organism, leading to shifts in site quality with age, (2) be confounded with (or masked by) variation in traits of individuals populating the sites, and (3) be correlated with local density. For example, if high-quality sites attract more individuals but vital rates are density dependent, then observed vital rates will be relatively homogeneous in space despite strong heterogeneity in site quality. Here, we operationally define site quality for a reef fish as the mean survival time of juveniles transplanted to sites at a common density and size structure, with random assignment of individuals to sites to remove potential confounding effects of local variation in individual quality and density. Our assays using juvenile age classes of the six-bar wrasse (Thalassoma hardwicke) showed that site quality varied in space (i.e., among patch reefs) but was constant through time. Site quality increased with availability of the branching coral Pocillopora (which is used as a refuge), but decreased with density of a predator, the arc-eye hawkfish, Paracirrhites arcatus (which also uses Pocillopora). We experimentally added colonies of Pocillopora to reefs and (1) increased site quality, (2) enhanced natural settlement rates of six-bar wrasse, but (3) attracted more hawkfish predators, and (4) did not increase survival of juvenile fish under ambient densities. Our results suggest that Pocillopora increases site quality, but attracts greater densities of settlers and predators, resulting in increased density dependence and predation, which mask the underlying effects of Pocillopora on site quality (supporting the hypothesis of "cryptic density dependence"). Variation in site quality and the possible confounding effects of density and individual traits warrant more experimental study.     

Updating coarse-scale species distribution models using small fine-scale samples

Large, fine-grained samples are ideal for predictive species distribution models used for management purposes, but such datasets are not available for most species and conducting such surveys is costly. We attempted to overcome this obstacle by updating previously available coarse-grained logistic regression models with small fine-grained samples using a recalibration approach. Recalibration involves re-estimation of the intercept or slope of the linear predictor and may improve calibration (level of agreement between predicted and actual probabilities). If reliable estimates of occurrence likelihood are required (e.g., for species selection in ecological restoration) calibration should be preferred to other model performance measures. This updating approach is not expected to improve discrimination (the ability of the model to rank sites according to species suitability), because the rank order of predictions is not altered. We tested different updating methods and sample sizes with tree distribution data from Spain. Updated models were compared to models fitted using only fine-grained data (refitted models). Updated models performed reasonably well at fine scales and outperformed refitted models with small samples (10-100 occurrences). If a coarse-grained model is available (or could be easily developed) and fine-grained predictions are to be generated from a limited sample size, updating previous models may be a more accurate option than fitting a new model. Our results encourage further studies on model updating in other situations where species distribution models are used under different conditions from their training (e.g., different time periods, different regions).     

Pressure cookers or pressure valves: Do roads lead to deforestation in China?

The effect of roads on forests is ambiguous. Many studies conclude that building and upgrading roads increases pressure on forests but some find that new and better roads may reduce the rate of deforestation. In this paper we use satellite remote sensing images of forest cover in Jiangxi Province, China, to test whether the existence and the size of roads (ranging from expressways to tertiary roads) in 1995 affected the level of forest cover in 2000 or the rate of change between 1995 and 2000. To account for road access for each of our 1 km² (“pixel”) units of forest cover we measure whether or not and what type of roads penetrate the “watershed” in which the pixel lies. These watersheds allow more plausible measures of accessibility than do traditional “crowfly” distance measures that ignore topography. To account for possible confounding we also use 12 additional covariates: geographic and climatic variables (e.g., elevation, slope, rainfall, temperature, soil properties); demographic and economic variables (e.g., local population and GDP per square kilometer); and distance variables (e.g., distance to the nearest provincial capital). Although simple univariate OLS regressions show that forest levels are lower and deforestation rates higher either when there is a road, or when there is a higher quality road, these results are not robust. Controlling for all of the covariates and also using recently developed covariate matching techniques to estimate treatment effects, we find that roads in China’s Jiangxi Province can most safely be described as having no impact on the level of forests and no impact on the rate of deforestation.     

Combining multistate capture-recapture data with tag recoveries to estimate demographic parameters

Matrix population models that allow an animal to occupy more than one state over time are important tools for population and evolutionary ecologists. Definition of state can vary, including location for metapopulation models and breeding state for life history models. For populations whose members can be marked and subsequently reencountered, multistate mark-recapture models are available to estimate the survival and transition probabilities needed to construct population models. Multistate models have proved extremely useful in this context, but they often require a substantial amount of data and restrict estimation of transition probabilities to those areas or states subjected to formal sampling effort. At the same time, for many species, there are considerable tag recovery data provided by the public that could be modeled in order to increase precision and to extend inference to a greater number of areas or states. Here we present a statistical model for combining multistate capture-recapture data (e.g., from a breeding ground study) with multistate tag recovery data (e.g., from wintering grounds). We use this method to analyze data from a study of Canada Geese (Branta canadensis) in the Atlantic Flyway of North America. Our analysis produced marginal improvement in precision, due to relatively few recoveries, but we demonstrate how precision could be further improved with increases in the probability that a retrieved tag is reported.     

The immediate costs and long-term benefits of assisted gene flow in large populations

With the genetic health of many plant and animal populations deteriorating due to climate change outpacing adaptation, interventions, such as assisted gene flow (AGF), may provide genetic variation necessary for populations to adapt to climate change. We ran genetic simulations to mimic different AGF scenarios in large populations and measured their outcomes on population-level fitness to determine circumstances in which it is worthwhile to perform AGF. In the absence of inbreeding depression, AGF was beneficial within a few generations only when introduced genotypes had much higher fitness than local individuals and traits affecting fitness were controlled by a few genes of large effect. AGF was harmful over short periods (e.g., first ∼10–20 generations) if there was strong outbreeding depression or introduced deleterious genetic variation. When the adaptive trait was controlled by many loci of small effect, the benefits of AGF took over 10 generations to realize—potentially too long for most climate-related management scenarios. The genomic integrity of the recipient population typically remained intact following AGF; the amount of genetic material from the donor population usually constituted no more of the recipient population's genome than the fraction of the population introduced. Significant genomic turnover (e.g., >50% replacement) only occurred when the selective advantage of the adaptive trait and translocation fraction were extremely high. Our results will be useful when adaptive management is used to maintain the genetic health and productivity of large populations under climate change.     

The effect of pollution on crime: Evidence from data on particulate matter and ozone

We estimate the effect of short-term air pollution exposure (PM_2.5 and ozone) on several categories of crime, with a particular emphasis on aggressive behavior. To identify this relationship, we combine detailed daily data on crime, air pollution, and weather for an eight-year period across the United States. Our primary identification strategy employs extremely high dimensional fixed effects and we perform a series of robustness checks to address confounding variation between temperature and air pollution. We find a robust positive effect of increased air pollution on violent crimes, and specifically assaults, but no relationship between increases in air pollution and property crimes. The effects are present in and out of the home, at levels well below Ambient Air Pollution Standards, and PM_2.5 effects are strongest at lower temperatures. The results suggest that a 10% reduction in daily PM_2.5 and ozone could save $1.4 billion in crime costs per year, a previously overlooked cost associated with pollution.     

There will be blood: Crime rates in shale-rich U.S. counties

Over the past decade the production of tight oil and shale gas significantly increased in the United States. This paper examines how this energy boom has affected regional crime rates throughout the country. We find positive effects on rates of various property and violent crimes in shale-rich counties. In 2013, the cost of the additional crimes in the average treatment county was roughly $2 million. These results are not easily explained by shifts in observed demographics like gender and age. There is however evidence that people with criminal records (registered sex offenders) moved disproportionally to shale-boom towns in North Dakota. We also document a rise in income inequality (a postulated determinant of criminal activity) that coincides with the timing of the energy boom. Policy makers in boom towns should anticipate these crime effects and invest in public infrastructure accordingly.     

Is the effect of forest structure on bird diversity modified by forest productivity?

Currently, the most common strategy when managing forests for biodiversity at the landscape scale is to maintain structural complexity within stands and provide a variety of seral stages across landscapes. Advances in ecological theory reveal that biodiversity at continental scales is strongly influenced by available energy (i.e., climate factors relating to heat and light and primary productivity). This paper explores how available energy and forest structural complexity may interact to drive biodiversity at a regional scale. We hypothesized that bird species richness exhibits a hump-shaped relationship with energy at the regional scale of the northwestern United States. As a result, we hypothesized that the relationship between energy and richness within a landscape is positive in energy-limited landscapes and flat or decreasing in energy-rich landscapes. Additionally, we hypothesized that structural complexity explains less of the variation in species richness in energy-limited environments and more in energy-rich environments and that the slope of the relationship between structural complexity and richness is greatest in energy-rich environments. We sampled bird communities and vegetation across seral stages and biophysical settings at each of five landscapes arrayed across a productivity gradient from the Pacific Coast to the Rocky Mountains within the five northwestern states of the contiguous United States. We analyzed the response of richness to structural complexity and energy covariates at each landscape. We found that (1) richness had a hump-shaped relationship with available energy across the northwestern United States, (2) the landscape-scale relationships between energy and richness were positive or hump shaped in energy-limited locations and were flat or negative in energy-rich locations, (3) forest structural complexity explained more of the variation in bird species richness in energy-rich landscapes, and (4) the slope of the relationship between forest structural complexity and richness was steepest in energy-limited locations. In energy-rich locations, forest managers will likely increase landscape-scale bird diversity by providing a range of forest structural complexity across all seral stages. In low-energy environments, bird diversity will likely be maximized by managing local high-energy hotspots judiciously and adjusting harvest intensities in other locations to compensate for slower regeneration rates.     

Tree species diversity and its relationship to stand parameters and geomorphology features in the eastern Black Sea region forests of Turkey

We studied the effects of stand parameters (crown closure, basal area, stand volume, age, mean stand diameter number of trees, and heterogeneity index) and geomorphology features (elevation, aspect and slope) on tree species diversity in an example of untreated natural mixed forest stands in the eastern Black Sea region of Turkey. Tree species diversity and basal area heterogeneity in forest ecosystems are quantified using the Shannon-Weaver and Simpson indices. The relationship between tree species diversity basal area heterogeneity stand parameters and geomorphology features are examined using regression analysis. Our work revealed that the relationship between tree species diversity and stand parameters is loose with a correlation coefficient between 0.02 and 0.70. The correlation of basal area heterogeneity with stand parameters fluctuated between 0.004 and 0.77 (R2). According to our results, stands with higher tree species diversity are characterised by higher mean stand diameter number of diameter classes, basal area and lower homogeneity index value. Considering the effect of geomorphology features on tree species or basal area heterogeneity we found that all investigated relationships are loose with R < or = 0.24. A significant correlation was detected only between tree species diversity and aspect. Future work is required to verify the detected trends in behaviour of tree species diversity if it is to estimate from the usual forest stand parameters and topography characteristics.     

An integrated approach for predicting fates of reintroductions with demographic data from multiple populations

We devised a novel approach to model reintroduced populations whereby demographic data collected from multiple sites are integrated into a Bayesian hierarchical model. Integrating data from multiple reintroductions allows more precise population-growth projections to be made, especially for populations for which data are sparse, and allows projections that account for random site-to-site variation to be made before new reintroductions are attempted. We used data from reintroductions of the North Island Robin (Petroica longipes), an endemic New Zealand passerine, to 10 sites where non-native mammalian predators are controlled. A comparison of candidate models that we based on deviance information criterion showed that rat-tracking rate (an index of rat density) was a useful predictor of robin fecundity and adult female survival, that landscape connectivity and a binary measure of whether sites were on a peninsula were useful predictors of apparent juvenile survival (probably due to differential dispersal away from reintroduction sites), and that there was unexplained random variation among sites in all demographic rates. We used the two best supported models to estimate the finite rate of increase (λ) for populations at each of the 10 sites, and for a proposed reintroduction site, under different levels of rat control. Only three of the reintroduction sites had λ distributions completely >1 for either model. At two sites, λ was expected to be >1 if rat-tracking rates were <5%. At the other five reintroduction sites, λ was predicted to be close to 1, and it was unclear whether growth was expected. Predictions of λ for the proposed reintroduction site were less precise than for other sites because distributions incorporated the full range of site-to-site random variation in vital rates. Our methods can be applied to any species for which postrelease data on demographic rates are available and potentially can be extended to model multiple species simultaneously.     

Regression analysis of forest inventory data with time and space dependencies

In this paper the data of a forest health inventory are analyzed. Since 1983 the degree of defoliation, together with various explanatory variables (covariates) concerning stand, site, soil and weather, are recorded by the second of the two authors, in the forest district of Rothenbuch (Spessart, Bavaria). The focus is on the space and time dependencies of the data. The mutual relationship of space-time functions and the set of covariates is evaluated. For this we use generalized linear models (GLMs) for ordinal response variables and semiparametric estimation approaches. By using goodness-of-fit measures it turns out that (i) the contribution of space-time functions is quantitatively comparable with that of the set of covariates, (ii) the contribution of space-time functions is small compared with the contribution of a set of variables describing the last-year and neighboring response values. By applying appropriate residual methods a detailed analysis of the individual sites in the area can be carried out. This analysis reveals where the predictive power of the covariates fail to explain the observed defoliation.     

Effects of Land-Use and Tsetse Fly Control on Bird Species Richness in Southwestern Ethiopia

Successful control of tsetse (Glossina spp.)-transmitted trypanosomiasis in the Ghibe Valley, Ethiopia, appears to have accelerated conversion of wooded grassland into cropland. Land conversion, in turn, may have fragmented wildlife habitat. Our objective was to assess the influence of the expansion of agricultural land-use, brought about by tsetse control, on ecological properties by using bird species richness and composition as indicators of environmental impacts. We sampled bird species richness and composition (using Timed-Species counts) and habitat structure (using field sampling and remote sensing) in four land cover/land-use types in areas subjected to tsetse fly control and adjacent areas without control. At the height of the growing season bird species numbers and vegetative complexity were greater in the small-holder, oxen-plowed fields and riparian woodlands than in wooded grasslands or in large-holder, tractor-plowed fields. Species composition was highly dissimilar (40–70% dissimilarity) comparing among land-use types, with many species found only in a single type. This implies that trypanosomiasis control that results in land conversion from wooded grasslands to small-holder farming in this region may have no adverse impacts on bird species numbers but will alter composition. These results also suggest that moderate land-use by humans (e.g., small-holder field mosaics) increases habitat heterogeneity and bird species richness relative to high levels of use (e.g., tractor-plowed fields). Tsetse control may be indirectly maintaining species richness in the valley by encouraging the differential spread of these small-scale, heterogeneous farms in place of large-scale, homogeneous farms. Nevertheless, if the extent of small-holder farms significantly exceeds that of present levels, negative impacts on bird species richness and large shifts in species composition may occur.     

Aquatic plant community invasibility and scale-dependent patterns in native and invasive species richness

Invasive species richness often is negatively correlated with native species richness at the small spatial scale of sampling plots, but positively correlated in larger areas. The pattern at small scales has been interpreted as evidence that native plants can competitively exclude invasive species. Large-scale patterns have been understood to result from environmental heterogeneity, among other causes. We investigated species richness patterns among submerged and floating-leaved aquatic plants (87 native species and eight invasives) in 103 temperate lakes in Connecticut (northeastern USA) and found neither a consistently negative relationship at small (3-m2) scales, nor a positive relationship at large scales. Native species richness at sampling locations was uncorrelated with invasive species richness in 37 of the 60 lakes where invasive plants occurred; richness was negatively correlated in 16 lakes and positively correlated in seven. No correlation between native and invasive species richness was found at larger spatial scales (whole lakes and counties). Increases in richness with area were uncorrelated with abiotic heterogeneity. Logistic regression showed that the probability of occurrence of five invasive species increased in sampling locations (3 m2, n = 2980 samples) where native plants occurred, indicating that native plant species richness provided no resistance against invasion. However, the probability of three invasive species' occurrence declined as native plant density increased, indicating that density, if not species richness, provided some resistance with these species. Density had no effect on occurrence of three other invasive species. Based on these results, native species may resist invasion at small spatial scales only in communities where density is high (i.e., in communities where competition among individuals contributes to community structure). Most hydrophyte communities, however, appear to be maintained in a nonequilibrial condition by stress and/or disturbance. Therefore, most aquatic plant communities in temperate lakes are likely to be vulnerable to invasion.