William J. McShea

Bio: William J. McShea is an academic researcher from Smithsonian Conservation Biology Institute. The author has contributed to research in topics: Population & Habitat. The author has an hindex of 49, co-authored 196 publications receiving 7839 citations. Previous affiliations of William J. McShea include Binghamton University & National Museum of Natural History.

CTFS-ForestGEO: A worldwide network monitoring forests in an era of global change

Abstract: Global change is impacting forests worldwide, threatening biodiversity and ecosystem services including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long-term forest dynamics research sites (CTFS-ForestGEO) useful for characterizing forest responses to global change. Within very large plots (median size 25ha), all stems 1cm diameter are identified to species, mapped, and regularly recensused according to standardized protocols. CTFS-ForestGEO spans 25 degrees S-61 degrees N latitude, is generally representative of the range of bioclimatic, edaphic, and topographic conditions experienced by forests worldwide, and is the only forest monitoring network that applies a standardized protocol to each of the world's major forest biomes. Supplementary standardized measurements at subsets of the sites provide additional information on plants, animals, and ecosystem and environmental variables. CTFS-ForestGEO sites are experiencing multifaceted anthropogenic global change pressures including warming (average 0.61 degrees C), changes in precipitation (up to +/- 30% change), atmospheric deposition of nitrogen and sulfur compounds (up to 3.8g Nm(-2)yr(-1) and 3.1g Sm(-2)yr(-1)), and forest fragmentation in the surrounding landscape (up to 88% reduced tree cover within 5km). The broad suite of measurements made at CTFS-ForestGEO sites makes it possible to investigate the complex ways in which global change is impacting forest dynamics. Ongoing research across the CTFS-ForestGEO network is yielding insights into how and why the forests are changing, and continued monitoring will provide vital contributions to understanding worldwide forest diversity and dynamics in an era of global change.

Global importance of large‐diameter trees

Abstract: Aim: To examine the contribution of large-diameter trees to biomass, stand structure, and species richness across forest biomes. Location: Global. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: We examined the contribution of large trees to forest density, richness and biomass using a global network of 48 large (from 2 to 60 ha) forest plots representing 5,601,473 stems across 9,298 species and 210 plant families. This contribution was assessed using three metrics: the largest 1% of trees >= 1 cm diameter at breast height (DBH), all trees >= 60 cm DBH, and those rank-ordered largest trees that cumulatively comprise 50% of forest biomass. Results: Averaged across these 48 forest plots, the largest 1% of trees >= 1 cm DBH comprised 50% of aboveground live biomass, with hectare-scale standard deviation of 26%. Trees >= 60 cm DBH comprised 41% of aboveground live tree biomass. The size of the largest trees correlated with total forest biomass (r(2) 5.62, p < .001). Large-diameter trees in high biomass forests represented far fewer species relative to overall forest richness (r(2) = 5.45, p < .001). Forests with more diverse large-diameter tree communities were comprised of smaller trees (r(2) = 5.33, p < .001). Lower large-diameter richness was associated with large-diameter trees being individuals of more common species (r(2) =5.17, p=5.002). The concentration of biomass in the largest 1% of trees declined with increasing absolute latitude (r(2) = 5.46, p < .001), as did forest density (r(2) = 5.31, p < .001). Forest structural complexity increased with increasing absolute latitude (r(2) = 5.26, p < .001). Main conclusions: Because large-diameter trees constitute roughly half of the mature forest biomass worldwide, their dynamics and sensitivities to environmental change represent potentially large controls on global forest carbon cycling. We recommend managing forests for conservation of existing large-diameter trees or those that can soon reach large diameters as a simple way to conserve and potentially enhance ecosystem services.

The influence of acorn crops on annual variation in rodent and bird populations

Abstract: I recorded mast production by oaks (Quercus sp.) at 12 forested sites in western Virginia for 6–12 yr and measured its impact on the abundance of small mammals, understory vegetation, and artificial-nest predation. White-tailed deer (Odocoileus virginianus) were excluded from half the 4-ha sites after at least one season of data collection. My hypothesis was that annual variation in acorn crops affected multiple species and that the strength of those interactions is mediated by white-tailed deer. The acorn crop was variable across sites and year, with some of the between-site variability explained by differences in elevation. All sites experienced at least one mast failure, and mast failure years were generally consistent across sites. White-footed mouse (Peromyscus leucopus), eastern chipmunk (Tamias striatus), and gray squirrel (Sciurus carolinensis) populations were significantly correlated with annual fluctuations in the acorn crop. The exclusion of deer had a significant impact on P. leucopus and T. striatus populations by increasing the number of animals captured following low acorn mast years. Annual fluctuations in the acorn crop, but not in rodent densities, were significantly correlated with the rates of predation on artificial nests the next summer. There was no significant interaction between predation rates and the exclusion of deer. An index from the Breeding Bird Survey (BBS) for Virginia was used to measure regional numbers for 11 common species captured at the sites. The index for two understory species was significantly negatively correlated with the mean acorn crop measured 2 yr previously. The effect of white-tailed deer on the forest community was not consistent across all conditions, as sites with large acorn crops were not strongly influenced by deer. These data are consistent with the hypothesis that mast crops from oaks serve as important determinants of community function within Appalachian forests.

Scale-dependent relationships between tree species richness and ecosystem function in forests

Abstract: 1. The relationship between species richness and ecosystem function, as measured by productivity or biomass, is of long-standing theoretical and practical interest in ecology. This is especially true for forests, which represent a majority of global biomass, productivity and biodiversity.

The Theory of Island Biogeography

Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

A review of methods for the assessment of prediction errors in conservation presence/absence models

Abstract: Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false negatives. Many of the prediction errors can be traced to ecological processes such as unsaturated habitat and species interactions. Consequently, if prediction errors are not placed in an ecological context the results of the model may be misleading. The simplest, and most widely used, measure of prediction accuracy is the number of correctly classified cases. There are other measures of prediction success that may be more appropriate. Strategies for assessing the causes and costs of these errors are discussed. A range of techniques for measuring error in presence/absence models, including some that are seldom used by ecologists (e.g. ROC plots and cost matrices), are described. A new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed. Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models.

Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar

Abstract: Aim: Techniques that predict species potential distributions by combining observed occurrence records with environmental variables show much potential for application across a range of biogeographical analyses. Some of the most promising applications relate to species for which occurrence records are scarce, due to cryptic habits, locally restricted distributions or low sampling effort. However, the minimum sample sizes required to yield useful predictions remain difficult to determine. Here we developed and tested a novel jackknife validation approach to assess the ability to predict species occurrence when fewer than 25 occurrence records are available. Location: Madagascar. Methods: Models were developed and evaluated for 13 species of secretive leaf-tailed geckos (Uroplatus spp.) that are endemic to Madagascar, for which available sample sizes range from 4 to 23 occurrence localities (at 1 km2 grid resolution). Predictions were based on 20 environmental data layers and were generated using two modelling approaches: a method based on the principle of maximum entropy (Maxent) and a genetic algorithm (GARP). Results: We found high success rates and statistical significance in jackknife tests with sample sizes as low as five when the Maxent model was applied. Results for GARP at very low sample sizes (less than c. 10) were less good. When sample sizes were experimentally reduced for those species with the most records, variability among predictions using different combinations of localities demonstrated that models were greatly influenced by exactly which observations were included. Main conclusions: We emphasize that models developed using this approach with small sample sizes should be interpreted as identifying regions that have similar environmental conditions to where the species is known to occur, and not as predicting actual limits to the range of a species. The jackknife validation approach proposed here enables assessment of the predictive ability of models built using very small sample sizes, although use of this test with larger sample sizes may lead to overoptimistic estimates of predictive power. Our analyses demonstrate that geographical predictions developed from small numbers of occurrence records may be of great value, for example in targeting field surveys to accelerate the discovery of unknown populations and species. © 2007 The Authors.

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

Abstract: Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

Biogeochemical Hot Spots and Hot Moments at the Interface of Terrestrial and Aquatic Ecosystems

Abstract: Rates and reactions of biogeochemical processes vary in space and time to produce both hot spots and hot moments of elemental cycling. We define biogeochemical hot spots as patches that show disproportionately high reaction rates relative to the surrounding matrix, whereas hot moments are defined as short periods of time that exhibit disproportionately high reaction rates relative to longer intervening time periods. As has been appreciated by ecologists for decades, hot spot and hot moment activity is often enhanced at terrestrial-aquatic interfaces. Using examples from the carbon (C) and nitrogen (N) cycles, we show that hot spots occur where hydrological flowpaths converge with substrates or other flowpaths containing complementary or missing reactants. Hot moments occur when episodic hydrological flowpaths reactivate and/or mobilize accumulated reactants. By focusing on the delivery of specific missing reactants via hydrologic flowpaths, we can forge a better mechanistic understanding of the factors that create hot spots and hot moments. Such a mechanistic understanding is necessary so that biogeochemical hot spots can be identified at broader spatiotemporal scales and factored into quantitative models. We specifically recommend that resource managers incorporate both natural and artificially created biogeochemical hot spots into their plans for water quality management. Finally, we emphasize the needs for further research to assess the potential importance of hot spot and hot moment phenomena in the cycling of different bioactive elements, improve our ability to predict their occurrence, assess their importance in landscape biogeochemistry, and evaluate their utility as tools for resource management.