Evaluating the ability of regional models to predict local avian abundance

Spatial modeling over broad scales can potentially direct conservation efforts to areas with high species-specific abundances. We examined the performance of regional models for predicting bird abundance at spatial scales typically addressed in conservation planning. Specifically, we used point count data on wood thrush (Hylocichla mustelina) and blue-winged warbler (Vermivora cyanoptera) from 2 time periods (1995–1998 and 2006–2007) to evaluate the ability of regional models derived via Bayesian hierarchical techniques to predict bird abundance. We developed models for each species within Bird Conservation Region (BCR) 23 in the upper midwestern United States at 800-ha, 8,000-ha, and approximately 80,000-ha scales. We obtained count data from the Breeding Bird Survey and land cover data from the National Land Cover Dataset (1992). We evaluated predictions from the best models, as defined by an information-theoretic criterion, using point count data collected within an ecological subregion of BCR 23 at 131 count stations in the 1990s and again in 2006–2007. Competing (Deviance Information Criteria <5) blue-winged warbler models accounted for 67% of the variability and suggested positive associations with forest edge and proportion of forest at the 8,000-ha scale, and negative associations with forest patch area (800 ha) and wetness (800 ha and 80,000 ha). The regional model performed best for blue-winged warbler predicted abundances from point counts conducted in Iowa during 1995–1996 (r_s = 0.57; P = 0.14), the survey period that most closely aligned with the time period of data used for regional model construction. Wood thrush models exhibited positive correlations with point count data for all survey areas and years combined (r_s = 0.58, P ≤ 0.001). In comparison, blue-winged warbler models performed worse as time increased between the point count surveys and vintage of the model building data (r_s = 0.03, P = 0.92 for Iowa and r_s = 0.13, P = 0.51 for all areas, 2006–2007), likely related to the ephemeral nature of their preferred early successional habitat. Species abundance and sensitivity to changing habitat conditions seems to be an important factor in determining the predictive ability of regional models. Hierarchical models can be a useful tool for concentrating efforts at the scale of management units and should be one of many tools used by land managers, but we caution that the utility of such models may decrease over time for species preferring relatively ephemeral habitats if model inputs are not updated accordingly. © 2012 The Wildlife Society.     

Plant cover estimation based on the beta distribution in grassland vegetation

Cover is the most frequently used measure of abundance in vegetation surveys of grasslands, and various qualitative and semi-quantitative methods have been developed for visual estimation of this metric. Field survey is usually made with a point-grid plate. The frequency distributions of cover derived from point-grid counts follow a beta distribution. Combining point-grid counts from a field survey and the beta distribution for a statistical analysis, we developed an effort-saving cover-measurement method. Cover is measured with a transparent plastic plate on which, for example, 10 × 10 = 100 points are arranged in a lattice with 1-cm grid spacing (thus, one point count represents 1 cm² of cover). N quadrats are set out at randomly dispersed sites in a grassland, and, in each, the plastic plate is used for making counts. The number of grid points located above a given species is counted in every quadrat until the number of counted points reaches a given value c, which is determined in advance. If the number of counted points reaches c in a quadrat, the count is stopped and the quadrat is classified in the category “>c”. In quadrats where c is not attained, full point counts above the species bodies are made. Let g be the number of observed quadrats whose cover is ≤c. Using these g cover measurements and the number of quadrats (N − g) with cover >c, we can quantitatively estimate cover for each species and the spatial pattern index value based on the maximum likelihood method. In trial counts using this method, the time savings varied between 5% and 41%, depending on the shape of the cover frequency distribution. The mean cover value estimates agreed well with conventional measures without a stopping point (i.e., based on full counts of all points in each quadrat).     

Assessing Ecoregional-Scale Habitat Suitability Index Models for Priority Landbirds

ABSTRACT Emerging methods in habitat and wildlife population modeling promise new horizons in conservation but only if these methods provide robust population-habitat linkages. We used Breeding Bird Survey (BBS) data to verify and validate newly developed habitat suitability index (HSI) models for 40 priority landbird species in the Central Hardwoods and West Gulf Coastal Plain/Ouachitas Bird Conservation Regions. We considered a species’ HSI model verified if there was a significant rank correlation between mean predicted HSI score and mean observed BBS abundance across the 88 ecological subsections within these Bird Conservation Regions. When we included all subsections, correlations verified 37 models. Models for 3 species were unverified. Rank correlations for an additional 5 species were not significant when analyses included only subsections with BBS abundance >0. To validate models, we developed generalized linear models with mean observed BBS abundance as the response variable and mean HSI score and Bird Conservation Region as predictor variables. We considered verified models validated if the overall model was an improvement over an intercept-only null model and the coefficient on the HSI variable in the model was >0. Validation provided a more rigorous assessment of model performance than verification, and models for 12 species that we verified failed validation. Species whose models failed validation were either poorly sampled by BBS protocols or associated with woodland and shrubland habitats embedded within predominantly open landscapes. We validated models for 25 species. Habitat specialists and species reaching their highest densities in predominantly forested landscapes were more likely to have validated models. In their current form, validated models are useful for conservation planning of priority landbirds and offer both insight into limiting factors at ecoregional scales and a framework for monitoring priority landbird populations from readily available national data sets.     

Surrogate Habitats Demonstrate the Invasion Potential of the African Pugnacious Ant

Many ant species are highly invasive and are a significant component of disturbed ecosystems. They can have a major suppressive effect upon indigenous invertebrates, including other ants. Despite overwhelming circumstantial evidence for the ecological resourcefulness of many ants, there appears to be no experimental evidence illustrating the habitat breadth of a potentially invasive ant species. We demonstrate here that a particularly opportunistic and locally dominant ant Anoplolepis custodiens, which is a major indigenous African pest, overrides habitat structure to maintain its population level. We compared A. custodiens activity, morphology, foraging behaviour and ant species diversity in artificially established surrogate habitats (cover crops) in a vineyard containing an ample food resource in the form of the honeydew-producing mealybug Planococcus ficus. These cover crops were chosen so as to create highly altered habitats. The ant's ability to overcome these potentially suppressive habitat conditions hinged on its tight mutualism with the mealybug, and on its chasing away mealybug parasitoids. This ant species is predicted to be a latent invasive beyond Africa. It is unlikely to be impeded once it has established a foothold in a variety of novel habitats. It could locally invade to obtain food resources in a wide range of habitat types. Furthermore, in agricultural systems, cover crops are unlikely to control such an ant. Potential invasives such as this ant should be flagged as important quarantine suspects.     

Behavior of fish predators and their prey: habitat choice between open water and dense vegetation

Synopsis Behavior of largemouth bass, Micropterus salmoides, and northern pike, Esox lucius, foraging on fathead minnows, Pimephales promelas, or bluegills, Lepomis macrochirus, was quantified in pools with 50% cover (half the pool had artificial stems at a density of 1000 stems m⁻²). Both predators spent most of their time in the vegetation. Largemouth bass searched for bluegills and ambushed minnows, whereas the relatively immobile northern pike ambushed all prey. Minnows were closer to predators and were captured more frequently than bluegills. Even when minnows dispersed, they moved continually and eventually wandered within striking distance of a predator. Bluegills dispersed in the cover with predators. Bass captured the few bluegills that strayed into the open and pike captured those that approached too closely in the cover. The ability of predators to capture prey while residing in habitats containing patches of dense cover may explain their residence in areas often considered to be poor ones for foraging. The unit is sponsored jointly by the United States Fish and Wildlife Service, Ohio Department of NaturalResources, The Ohio State University, and the Wildlife Management Institute     

Soil management effects on entomopathogenic fungi during the transition to organic agriculture in a feed grain rotation

The growing demand for organic products creates opportunities for farmers. Information on the consequences of management practices can help farmers transition to organic and take advantage of these prospects. We examined the interaction between soil disturbance and initial cover crop on naturally occurring entomopathogenic fungi (EPF) during the 3-year transition to organic production in a feed grain rotation in central Pennsylvania. Our experiment included four systems comprised of a factorial combination of two levels of primary tillage (full vs. reduced) and two types of initial cover crop (timothy/clover vs. rye/vetch). The cropping sequence consisted of an initial cover crop, followed by soybean, and finally, maize. The entire experiment was replicated in time, with the initiation lagged by 1 year. We detected four species of EPF (Metarhizium anisopliae, Beauveria bassiana, Isaria fumosorosea, and Isaria farinosa) by bioassay of soil samples collected four times during each field season. The latter three species were detected infrequently; therefore, we focused statistical analysis on M. anisopliae. Detection of M. anisopliae varied across sampling date, year in crop sequence, and experimental start, with no consistent trend across the 3-year transition period. M. anisopliae was isolated more frequently in the systems initiated with timothy/clover cover crops and utilizing full tillage; however, we only observed a tillage effect in one temporal replicate. M. anisopliae detection was negatively associated with soil moisture, organic matter, and zinc, sulfur, and copper concentrations in the soil. This study helps to inform farmers about management effects on soil function, specifically conservation biological control.     

Do national parks protect natural landscapes?

Establishing national parks should result from a desire to protect natural or near natural landscapes with the lowest degree of anthropogenic transformation. This paper tries to ascertain whether national parks in Poland protect the most natural landscapes and how far they have been affected by humans. The level of anthropogenic transformation of natural landscapes was assessed based on an analysis of the percentage of natural, semi-natural and anthropogenic land cover. The vast majority of national parks in Poland (21 out of 23) protect landscapes which have been minimally transformed by humans (RAT_TNP ranges from 1.01 to 1.16). Only two kinds of natural landscapes, those that are the most transformed by humans, are not represented within the set of the Polish national parks. Hence, the distribution of national parks reflects the degree of anthropogenic landscape transformation. The proposed method could be applied to any type of spatial unit and thus be the basis for designating areas that should be protected.     

Characterization of diverse plant communities in Aspen Parkland rangeland using LiDAR data

Question: How effective is high-resolution airborne LiDAR technology for quantifying biophysical characteristics of multiple community types within diverse rangeland environments? Location: Native Aspen Parkland vegetation in central Alberta, Canada. Methods: Vegetation within 117 reference plots stratified across eight types, including forest, shrubland, upland grassland and lowland meadow communities, were assessed in 2001 for the height, cover and density of vegetation within various strata (herb, shrub and tree layers). Actual ground data were subsequently compared against modelled values for each community type and strata derived from the analysis of airborne LiDAR data obtained in 2000. Results: LiDAR data were effective for quantifying vegetation height, cover and density of the overstory within closed- and open Populus forest communities. However, LiDAR measurements typically underestimated the height and cover of shrublands, as well as most of the herbaceous communities. Analysis of LiDAR intensity data indicated reflectance generally decreased as LiDAR sampling points moved upwards from the ground to the vegetation canopy. Conclusions: While LiDAR technology is useful for characterizing deciduous forest properties, the quantification of understory vegetation characteristics, as well as those of individual shrublands and grasslands, was more limiting. Further refinements in analysis methods are necessary to increase the reliability of characterizing these communities.     

OptimClass: Using species‐to‐cluster fidelity to determine the optimal partition in classification of ecological communities

Question: Community ecologists are often confronted with multiple possible partitions of a single set of records of species composition and/or abundances from several sites. Different methods of numerical classification produce different results, and the question is which of them, and how many clusters, should be selected for interpretation. We demonstrate a new method for identifying the optimal partition from a series of partitions of the same set of sites, based on number of species with high fidelity to clusters in a partition (faithful species). Methods: The new method, OptimClass, has two variants. OptimClass 1 searches the partition with the maximum number of faithful species across all clusters, while OptimClass 2 searches the partition with the maximum number of clusters that contain at least a preselected minimum number of faithful species. Faithful species are determined based on the P value of the Fisher's exact test, as a measure of fidelity. OptimClass was tested on three vegetation datasets that varied in species richness and internal heterogeneity, using several classification algorithms, resemblance measures and cover transformations. Results: Results from both variants of OptimClass depended on the preselected threshold P value for faithful species: higher P gave higher probability that a partition with more clusters was selected as optimal. Good partitions, in terms of OptimClass criteria, involved flexible beta clustering, and also ordinal clustering. Good partitions were also obtained with TWINSPAN when the required number of clusters was small, or UPGMA when the required number of clusters was large. Poor partitions usually resulted from classifications that used resemblance measures and cover transformations emphasizing differences in species cover; this is not unexpected because OptimClass uses a presence/absence‐based fidelity measure. Conclusions: If the aim of a classification is to obtain clusters rich in faithful species, which can be subsequently used as diagnostic species for identification of community types, OptimClass is a suitable method for simultaneous choice of the optimal classification algorithm and optimal number of clusters. It can be computed in the JUICE program.     

Effects of plot size on the ordination of vegetation samples

Questions: Do ordination patterns differ when based on vegetation samples recorded in plots of different size? If so, how large is the effect of plot size relative to the effects of data set heterogeneity and of using presence/absence or cover‐abundance data? Can we combine plots of different size in a single ordination? Methods: Two homogeneous and two heterogeneous data sets were sampled in Czech forests and grasslands. Cover‐abundances of plant species were recorded in series of five or six nested quadrats of increasing size (forest 49‐961 m2; grassland 1‐49 m²). Separate ordinations were computed for plots of each size for each data set, using either species presences/absences or cover‐abundances recorded on an ordinal scale. Ordination patterns were compared with Procrustean analysis. Also, ordinations of data sets jointly containing plots of different size were calculated; effects of plot size were evaluated using a Monte Carlo test in constrained ordination. Results: The results were consistent between forest and grassland data sets. In homogeneous data sets, the effect of presence/absence vs. cover‐abundance was similar to, or larger than, the effect of plot size; for presence/absence data the differences between ordinations of differently sized plots were smaller than for cover‐abundance data. In heterogeneous data sets, the effect of plot size was larger than the effect of presence‐absence vs. cover‐abundance. The plots of smaller size (= 100 m2 in forests, = 4 m² in grasslands) yielded the most deviating ordination patterns. Joint ordinations of differently sized plots mostly did not yield patterns that would be artifacts of different plot size, except for plots from the homogeneous data sets that differed in size by a factor of four or higher. Conclusions: Variation in plot size does influence ordination patterns. Smaller plots tend to produce less stable ordination patterns, especially in data sets with low ß‐diversity and species cover‐abundances. Data sets containing samples from plots of different sizes can be used for ordination if they represent vegetation with large ß‐diversity. However, if data sets are homogeneous, i.e. with low ß‐diversity, the differences in plot sizes should not be very large, in order to avoid the danger of plot size differences distorting the real vegetation differentiation in ordination patterns.