John G. Blake

Bio: John G. Blake is an academic researcher from University of Missouri–St. Louis. The author has contributed to research in topics: Species richness & Habitat. The author has an hindex of 26, co-authored 32 publications receiving 4046 citations. Previous affiliations of John G. Blake include University of Missouri & University of Wisconsin-Madison.

Temporal Variation in Birds and Fruits Along an Elevational Gradient in Costa Rica

Abstract: We studied temporal fluctuations in fruit production by plants and in pop- ulations of understory fruit-eating birds at three elevations (50, 500, and 1000 m) in Costa Rican wet forests over a 12-16 mo period to investigate effects of resource variation on bird movements and community structure We used mist nets to monitor changes in frugivore abundance, migration patterns, and breeding and molting cycles We sampled understory fruits of each forest concurrent with studies of frugivores Both frugivores and fruit exhibited considerable seasonal variation in abundance Peak frugivore capture rates occurred during peak periods of ripe fruit abundance Altitudinal migrants left lower mon- tane (1000 m) forest during periods of fruit scarcity and were present in lowland (50 m) and foothill (500 m) forest when ripe fruit was abundant Migrants, both altitudinal and temperate, accumulated fat before migration, and perhaps (for altitudinal migrants) in anticipation of breeding Some residents also put on fat before breeding Breeding was seasonal at all forests and occurred when ripe fruit abundance was low Results of this study indicate that birds may track changes in resource abundance Thus, variation in resource abundance influences dynamics of bird communities, both in terms of species composition and abundance Further, results illustrate the importance of viewing com- munities from different scales; dynamics at a local scale (eg, one elevation) can be influ- enced by changes in conditions (eg, fruit abundance) elsewhere That some species regularly move along elevational gradients implies that preservation of many species and of the biotic integrity of entire systems may require conservation of large, connected blocks of suitable habitat

Breeding birds of isolated woodlots: area and habitat relationships

Abstract: We investigated breeding bird communities of isolated woodlots (1.8-600 ha) in east-central Illinois during three summers (1979-1981) to compare the influence of area and habitat on community structure. Woodlots supported from 9 to 43 species and composition was relatively constant among years. Ecological generalists dominated small woodlots, while more specialized species increased in importance with area. Area accounted for most variation (86-98%) in total species number in each year and the species-area relationship did not change significantly among years. The amount of variance accounted for by area was greater than in previous studies. Neither habitat nor woodlot isolation explained significant additional variation in total species richness after area. Area accounted for most variation in number of species in different migratory and breeding habitat cate- gories, except for short-distance migrants, which correlated most strongly with habitat. Variation in habitat was not related to woodlot area and habitat accounted for additional variation in bird species numbers in most cases. Abundances of one-third to one-half of species examined correlated with woodlot area, but a greater proportion (66-72%) were influenced more strongly by habitat variables. Results from Illinois support previous con- clusions that species that breed in forest interior habitat and winter in the tropics are most likely to be adversely affected by a reduction in forest habitat. Results also show that bird communities in isolated tracts of forest are not random assemblages, but rather that species found in smaller woodlots are subsets of species found in larger forests.

Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes?

Abstract: Aim Species distribution models and geographical information system (GIS) technologies are becoming increasingly important tools in conservation planning and decision-making. Often the rich data bases of museums and herbaria serve as the primary data for predicting species distributions. Yet key assumptions about the primary data often are untested, and violation of such assumptions may have consequences for model predictions. For example, users of primary data assume that sampling has been random with respect to geography and environmental gradients. Here we evaluate the assumption that plant voucher specimens adequately sample the climatic gradient and test whether violation of this assumption influences model predictions. Location Bolivia and Ecuador. Methods Using 323,711 georeferenced herbarium collections and nine climatic variables, we predicted the distribution of 76 plant species using maximum entropy models (MAXENT) with training points that sampled the climate environments randomly and training points that reflected the climate bias in the herbarium collections. To estimate the distribution of species, MAXENT finds the distribution of maximum entropy (i.e. closest to uniform) subject to the constraint that the expected value for each environmental variable under the estimated distribution matches its empirical average. The experimental design included species that differed in geographical range and elevation; all species were modelled with 20 and 100 training points. We examined the influence of the number of training points and climate bias in training points, elevation and range size on model performance using analysis of variance models. Results We found that significant parts of the climatic gradient were poorly represented in herbarium collections for both countries. For the most part, existing climatic bias in collections did not greatly affect distribution predictions when compared with an unbiased data set. Although the effects of climate bias on prediction accuracy were found to be greater where geographical ranges were characterized by high spatial variation in the degree of climate bias (i.e. ranges where the bias of the various climates sampled by collections deviated considerably from the mean bias), the greatest influence on model performance was the number of presence points used to train the model. Main conclusions These results demonstrate that predictions of species distributions can be quite good despite existing climatic biases in primary data found in natural history collections, if a sufficiently large number of training points is available. Because of consistent overprediction of models, these results also confirm the importance of validating models with independent data or expert opinion. Failure to include independent model validation, especially in cases where training points are limited, may potentially lead to grave errors in conservation decision-making and planning.

Bird assemblages in second-growth and old-growth forests, costa rica: perspectives from mist nets and point counts

Abstract: Second growth has replaced lowland forest in many parts of the Neotropics, providing valuable habitat for many resident and migrant bird species. Given the prevalence of such habitats and the potential benefit for conservation of biodiversity, it is important to understand patterns of diversity in second growth and old growth. Descriptions of species-distribution patterns may depend, however, on method(s) used to sample birds. We used data from mist nets and point counts to (1) describe species diversity and community composition in second-growth (young and old) and old-growth forests at La Selva Biological Station, Costa Rica; and (2) to evaluate perspectives on community composition provided by the two methods. We recorded 249 species from 39 families, including 196 species captured in mist nets (10,019 captures) and 215 recorded during point counts (15,577 observations), which represents ∼78% of the terrestrial avifauna known from La Selva (excluding accidentals and birds characteristic of aqu...

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

Effects of Habitat Fragmentation on Biodiversity

Abstract: ■ Abstract The literature on effects of habitat fragmentation on biodiversity is huge. It is also very diverse, with different authors measuring fragmentation in different ways and, as a consequence, drawing different conclusions regarding both the magnitude and direction of its effects. Habitat fragmentation is usually defined as a landscape-scale process involving both habitat loss and the breaking apart of habitat. Results of empirical studies of habitat fragmentation are often difficult to interpret because (a) many researchers measure fragmentation at the patch scale, not the landscape scale and (b) most researchers measure fragmentation in ways that do not distinguish between habitat loss and habitat fragmentation per se, i.e., the breaking apart of habitat after controlling for habitat loss. Empirical studies to date suggest that habitat loss has large, consistently negative effects on biodiversity. Habitat fragmentation per se has much weaker effects on biodiversity that are at least as likely to be positive as negative. Therefore, to correctly interpret the influence of habitat fragmentation on biodiversity, the effects of these two components of fragmentation must be measured independently. More studies of the independent effects of habitat loss and fragmentation per se are needed to determine the factors that lead to positive versus negative effects of fragmentation per se. I suggest that the term “fragmentation” should be reserved for the breaking apart of habitat, independent of habitat loss.

Estimating Terrestrial Biodiversity through Extrapolation

Abstract: Both the magnitude and the urgency of the task of assessing global biodiversity require that we make the most of what we know through the use of estimation and extrapolation. Likewise, future biodiversity inventories need to be designed around the use of effective sampling and estimation procedures, especially for 'hyperdiverse' groups of terrestrial organisms, such as arthropods, nematodes, fungi, and microorganisms. The challenge of estimating patterns of species richness from samples can be separated into (i) the problem of estimating local species richness, and (ii) the problem of estimating the distinctness, or complementarity, of species assemblages. These concepts apply on a wide range of spatial, temporal, and functional scales. Local richness can be estimated by extrapolating species accumulation curves, fitting parametric distributions of relative abundance, or using non-parametric techniques based on the distribution of individuals among species or of species among samples. We present several of these methods and examine their effectiveness for an example data set. We present a simple measure of complementarity, with some biogeographic examples, and outline the difficult problem of estimating complementarity from samples. Finally, we discuss the importance of using 'reference' sites (or sub-sites) to assess the true richness and composition of species assemblages, to measure ecologically significant ratios between unrelated taxa, to measure taxon/sub-taxon (hierarchical) ratios, and to 'calibrate' standardized sampling methods. This information can then be applied to the rapid, approximate assessment of species richness and faunal or floral composition at 'comparative' sites.

A distributed, architecture-centric approach to computing accurate recommendations from very large and sparse datasets

Abstract: The use of recommender systems is an emerging trend today, when user behavior information is abundant. There are many large datasets available for analysis because many businesses are interested in future user opinions. Sophisticated algorithms that predict such opinions can simplify decision-making, improve customer satisfaction, and increase sales. However, modern datasets contain millions of records, which represent only a small fraction of all possible data. Furthermore, much of the information in such sparse datasets may be considered irrelevant for making individual recommendations. As a result, there is a demand for a way to make personalized suggestions from large amounts of noisy data. Current recommender systems are usually all-in-one applications that provide one type of recommendation. Their inflexible architectures prevent detailed examination of recommendation accuracy and its causes. We introduce a novel architecture model that supports scalable, distributed suggestions from multiple independent nodes. Our model consists of two components, the input matrix generation algorithm and multiple platform-independent combination algorithms. A dedicated input generation component provides the necessary data for combination algorithms, reduces their size, and eliminates redundant data processing. Likewise, simple combination algorithms can produce recommendations from the same input, so we can more easily distinguish between the benefits of a particular combination algorithm and the quality of the data it receives. Such flexible architecture is more conducive for a comprehensive examination of our system. We believe that a user's future opinion may be inferred from a small amount of data, provided that this data is most relevant. We propose a novel algorithm that generates a more optimal recommender input. Unlike existing approaches, our method sorts the relevant data twice. Doing this is slower, but the quality of the resulting input is considerably better. Furthermore, the modular nature of our approach may improve its performance, especially in the cloud computing context. We implement and validate our proposed model via mathematical modeling, by appealing to statistical theories, and through extensive experiments, data analysis, and empirical studies. Our empirical study examines the effectiveness of accuracy improvement techniques for collaborative filtering recommender systems. We evaluate our proposed architecture model on the Netflix dataset, a popular (over 130,000 solutions), large (over 100,000,000 records), and extremely sparse (1.1%) collection of movie ratings. The results show that combination algorithm tuning has little effect on recommendation accuracy. However, all algorithms produce better results when supplied with a more relevant input. Our input generation algorithm is the reason for a considerable accuracy improvement.