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

The Theory of Island Biogeography

KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.

/pdf/random-forests-for-classification-in-ecology-4nfz47gm9v.pdf

Random Forests for Classification in Ecology

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.

/pdf/fast-tree-computing-large-minimum-evolution-trees-with-4grq0gwfpy.pdf

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

Conventional meta-analytic techniques rely on the assumption that effect size estimates from different studies are independent and have sampling distributions with known conditional variances. The independence assumption is violated when studies produce several estimates based on the same individuals or there are clusters of studies that are not independent (such as those carried out by the same investigator or laboratory). This paper provides an estimator of the covariance matrix of meta-regression coefficients that are applicable when there are clusters of internally correlated estimates. It makes no assumptions about the specific form of the sampling distributions of the effect sizes, nor does it require knowledge of the covariance structure of the dependent estimates. Moreover, this paper demonstrates that the meta-regression coefficients are consistent and asymptotically normally distributed and that the robust variance estimator is valid even when the covariates are random. The theory is asymptotic in the number of studies, but simulations suggest that the theory may yield accurate results with as few as 20-40 studies. Copyright © 2010 John Wiley & Sons, Ltd.

Robust variance estimation in meta-regression with dependent effect size estimates.

Plants can change soil biology, chemistry and structure in ways that alter subsequent plant growth. This process, referred to as plant-soil feedback (PSF), has been suggested to provide mechanisms for plant diversity, succession and invasion. Here we use three meta-analytical models: a mixed model and two Bayes models, one correcting for sampling dependence and one correcting for sampling and hierarchical dependence (delta-splitting model) to test these hypotheses. All three models showed that PSFs have medium to large negative effects on plant growth, and especially grass growth, the life form for which we had the most data. This supports the hypothesis that PSFs, through negative frequency dependence, maintain plant diversity, especially in grasslands. PSFs were also large and negative for annuals and natives, but the delta-splitting model indicated that more studies are needed for these results to be conclusive. Our results support the hypotheses that PSFs encourage successional replacements and plant invasions. Most studies were performed using monocultures of grassland species in greenhouse conditions. Future research should examine PSFs in plant communities, non-grassland systems and field conditions.

/pdf/plant-soil-feedbacks-a-meta-analytical-review-3u50kgvayh.pdf

Plant-Soil Feedbacks: A Meta-Analytical Review

Using corridors for conservation is increasing despite a lack of consensus on their efficacy. Specifically, whether corridors increase movement of plants and animals between habitat fragments has been addressed on a case-by-case basis with mixed results. Because of the growing number of well-designed experiments that have addressed this question, we conducted a meta-analysis to determine whether corridors increase movement; whether corridor effectiveness differs among taxa; how recent changes in experimental design have influenced findings; and whether corridor effectiveness differs between manipulative and natural experiments. To conduct our meta-analysis, we analyzed 78 experiments from 35 studies using a conservative hierarchical Bayesian model that accounts for hierarchical and sampling dependence. We found a highly significant result that corridors increase movement between habitat patches by approximately 50% compared to patches that are not connected with corridors. We found that corridors were more important for the movement of invertebrates, nonavian vertebrates, and plants than they were for birds. Recent methodological advances in corridor experiments, such as controlling for the area added by corridors, did not influence whether corridors increased movement, whereas controlling for the distance between source and connected or unconnected recipient patches decreased movement through corridors. After controlling for taxa differences and whether studies controlled for distance in experimental design, we found that natural corridors (those existing in landscapes prior to the study) showed more movement than manipulated corridors (those created and maintained for the study). Our results suggest that existing corridors increase species movement in fragmented landscapes and that efforts spent on maintaining and creating corridors are worthwhile.

/pdf/a-meta-analytic-review-of-corridor-effectiveness-40ctzujm9a.pdf

A Meta-Analytic Review of Corridor Effectiveness

MicroRNAs (miRNAs) have been implicated in colorectal cancer (CRC) development and associated with prognostic indicators such as disease stage and survival. Prognostic associations are often based on few individuals and imprecise. In this study, we utilize population-based data from 1,141 CRC cases to replicate previously reported associations between 121 miRNAs and disease stage and survival. The Agilent Human miRNA Microarray V19.0 was used to generate miRNA data following a stringent quality control protocol. Assessment of survival was done using Cox Proportional Hazard models adjusting for age, disease stage and tumor molecular phenotype. Five miRNAs were associated with more advanced disease stage; hsa-miR-145-5p and hsa-miR-31-5p showed increased expression with more advanced tumor stage, while hsa-miR-200b-3p, hsa-miR-215 and hsa-miR-451a had decreased expression with more advanced tumors. Thirteen miRNAs were associated with CRC mortality among individuals diagnosed with colon cancer while 14 were associated with CRC mortality after a diagnosis with rectal cancer. Strongest associations were observed for those miRNAs that were expressed in a small subset of tumors. Most notable associations were for hsa-miR-145-3p [hazard ratio (HR) 2.94, 95% confidence interval (CI) 1.54, 5.61], and hsa-miR-9-3p (HR 10.28, 95% CI 1.31, 80.84) with colon cancer and hsa-miR-335-5p (HR 0.17, 95% CI 0.05, 0.54) for rectal cancer. hsa-miR-374a-5p, hsa-miR-570-3p and hsa-miR-18a-5p significantly reduced the hazard of dying for all cases, regardless of tumor site. Our findings illustrate the need for a large sample to evaluate the association of miRNAs with survival and disease stage in order to determine associations by tumor site.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ijc.29384

An evaluation and replication of miRNAs with disease stage and colorectal cancer-specific mortality

MiRNAs are small, non-protein-coding RNA molecules that regulate gene expression either by post-transcriptionally suppressing mRNA translation or by mRNA degradation. We examine differentially expressed miRNAs in colorectal carcinomas, adenomas and normal colonic mucosa. Data come from population-based studies of colorectal cancer conducted in Utah and the Kaiser Permanente Medical Care Program. A total of 1893 carcinoma/normal-paired samples and 290 adenoma tissue samples were run on the Agilent Human miRNA Microarray V19.0 which contained 2006 miRNAs. We tested for significant differences in miRNA expression between paired carcinoma/adenoma/normal colonic tissue samples. Fewer than 600 miRNAs were expressed in >80% of people for colonic tissue; of these 86.5% were statistically differentially expressed between carcinoma and normal colonic mucosa using a false discovery rate of 0.05. Roughly half of these differentially expressed miRNAs showed a progression in levels of expression from normal to adenoma to carcinoma tissue. Other miRNAs appeared to be altered at the normal to adenoma stage, while others were only altered at the adenoma to carcinoma stage or only at the normal to carcinoma stage. Evaluation of the Agilent platform showed a high degree of repeatability (r = 0.98) and reasonable agreement with the NanoString platform. Our data suggest that miRNAs are highly dysregulated in colorectal tissue among individuals with colorectal cancer; the pattern of disruption varies by miRNA as tissue progresses from normal to adenoma to carcinoma.

/pdf/microrna-profiles-in-colorectal-carcinomas-adenomas-and-28hw9pscaw.pdf

MicroRNA profiles in colorectal carcinomas, adenomas and normal colonic mucosa: variations in miRNA expression and disease progression

Expression Profiles of miRNA Subsets Distinguish Human Colorectal Carcinoma and Normal Colonic Mucosa

/pdf/expression-profiles-of-mirna-subsets-distinguish-human-1wjnzr8fgo.pdf

John R. Stevens

Papers

Plant-Soil Feedbacks: A Meta-Analytical Review

A Meta-Analytic Review of Corridor Effectiveness

An evaluation and replication of miRNAs with disease stage and colorectal cancer-specific mortality

MicroRNA profiles in colorectal carcinomas, adenomas and normal colonic mucosa: variations in miRNA expression and disease progression

Expression Profiles of miRNA Subsets Distinguish Human Colorectal Carcinoma and Normal Colonic Mucosa