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

So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

/pdf/novel-methods-improve-prediction-of-species-distributions-3cmioqeu6v.pdf

Novel methods improve prediction of species' distributions from occurrence data

Statistics for Spatial Data.

This book is the second of two volumes in a series on terrestrial and marine comparisons, focusing on the temporal complement of the earlier spatial analysis of patchiness and pattern (Levin et al. 1993). The issue of the relationships among pattern, scale, and patchiness has been framed forcefully in John Steele’s writings of two decades (e.g., Steele 1978). There is no pattern without an observational frame. In the words of Nietzsche, “There are no facts… only interpretations.”

https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.2307/1941447

The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur Award Lecture

The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt’s calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt’s outputs vary in response to different settings to highlight the need for making biologically motivated modeling decisions.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1600-0587.2013.07872.x

A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter

A spatial and mechanistic model is developed for the dynamics of transition oak—northern hardwoods forests in northeastern North America. The purpose of the model is to extrapolate from measurable fine—scale and short—term interactions among individual trees to large—scale and long—term dynamics of forest communities. Field methods, statistical estimators, and model structure were designed simultaneously to ensure that parameters could be estimated from data collected in the field. This paper documents eight aspects of a three—year study to calibrate, test, and analyze the model for the nine dominant and subdominant tree species in transition oak—northern hardwoods forests: 1) Design and structure of the model. The model makes population dynamic forecasts by predicting the fate of every individual tree throughout its life. Species—specific functions predict each tree's dispersal, establishment, growth, mortality, and fecundity. Trees occupy unique spatial positions, and individual performance is affected by the local availability of resources. Competition is mechanistic; resources available to each tree are reduced by neighbors. Although the model was developed to include light, water, and nitrogen, the version described here includes only competition for light (shading and light—dependent performance) because the field data provide little evidence of competition for nitrogen and water over the range of sites examined. 2) Estimates of the model's parameters for each species. The estimates reveal a variety of "strategic trade—offs" among the species. For example, species that grow quickly under high light tend to cast relatively little shade, have low survivorship under low light, and have high dispersal. In contrast, species that grow slowly under high light tend to cast relatively dark shade, and to have high survivorship under low light and low dispersal. These trade—offs define one of two dominant "axes" of strategic variation. 3) Community level predictions of the model. The model predicts succession from early dominance by species such as Quercus rubra and Prunus serotina, to late dominance by Fagus grandifolia and Tsuga canadensis, with Betula alleganiensis present as a gap phase species in old—growth stands. The model also predicts that old—growth communities will have intraspecifically clumped and interspecifically segregated spatial distributions. 4) An error analysis that identifies community level predictions that are robust given the level of sampling uncertainty in the study. This analysis translates the statistical uncertainty associated with each parameter estimate into statistical uncertainty in the model's predictions. The robust predictions include those mentioned in aspect (3) above. 5) Sensitivity of the model to changes in initial conditions and to changes in the three parameters not included in the error analysis. For example, the model predicts that initial abundances continue to affect community composition well into succession (> 300 yr for some species). 6) Tests of the system— and community—level predictions of the model against independent data gleaned from other studies. These tests support the predictions found to be robust in the error analysis, including those predictions mentioned in aspect (3) above. 7) Modeling experiments that determine which aspects of individual performance and inter—neighbor competition are responsible for each of the robust predictions identified in aspect (4) above and tested in aspect (6) above. This analysis reveals a wide variety of causal relationships, with most parameters contributing to at least one community level phenomenon. 8) An explanation of the diversity of individual level causes identified in aspect (7). The two "axes" describing most of the strategic variation among the species (see [2]), provide a simple explanation of community level pattern in terms of individual level processes.

Forest models defined by field measurements : Estimation, error analysis and dynamics

To take advantage of the high spectral resolution of Landsat TM images and the high spatial resolution of SPOT panchromatic images (SPOT PAN), we present a wavelet transform method to merge the two data types. In a pyramidal fashion, each TM reflective band or SPOT PAN image was decomposed into an orthogonal wavelet representation at a given coarser resolution, which consisted of a low frequency approximation image and a set of high frequency, spatially-oriented detail images. Band-by-band, the merged images were derived by performing an inverse wavelet transform using the approximation image from each TM band and detail images from SPOT PAN. The spectral and spatial features of the merged results of the wavelet methods were compared quantitatively with those of intensity-hue-saturation (IHS), principal component analysis (PCA), and the Brovey transform. It was found that multisensor data merging is a trade-off between the spectral information from a low spatial-high spectral resolution sensor an...

A wavelet transform method to merge Landsat TM and SPOT panchromatic data

With a view toward understanding species-specific differences in juvenile tree mortality and the community-level implications of these differences, we characterized juvenile survivorship of 10 dominant tree species of oak transition-northern hardwood forests using species-specific mathematical models. The mortality models predict a sapling's probability of dying as a function of its recent growth history. These models and species-specific growth functions (published elsewhere), characterize a species' shade tolerance. Combined growth and mortality models express a sapling's probability of mortality as a function of light availability. We describe the statistical bases and the field methods used to calibrate the mortality models. We examined inter- and intraspecific variation in juvenile mortality across three sites: Great Mountain Forest (low pH, nutrient poor soils) in northwestern Connecticut, a calcareous bedrock region (neutral pH, nutrient rich soils) also in northwestern Connecticut, and a site in central-western Michigan (low pH, nutrient poor soils). Interspecific differences in juvenile mortality have profound effects on community dynamics and composition; the importance of these effects is demonstrated through a spatially explicit simulator of forest dynamics (SORTIE). The 10 species we examined occupy a continuum of survivorship levels at 1% of full sun. There was surprisingly little intraspecific variation in mortality functions for sugar maple, American beech, eastern hemlock, and white ash between the Great Mountain and Michigan sites. However, there was a striking increase in survivorship for sugar maple in the calcareous site. Differences in survivorship among the sites are correlated with soil pH and presumably nutrient availability. Growth rates in high-light and low-light survivorship are inversely correlated across species; as level of shade tolerance increases, a species grows more slowly in high light and exhibits increased survivorship under low light. Our results indicate that interspecific differences in sapling mortality are critical components of forest community dynamics.

https://www.jstor.org/stable/info/1942040

Juvenile Tree Survivorship as a Component of Shade Tolerance

Recruitment, the addition of new individuals into a community, is an im- portant factor that can substantially affect community composition and dynamics. We present a method for calibrating spatial models of plant recruitment that does not require identifying the specific parent of each recruit. This method calibrates seedling recruitment functions by comparing tree seedling distributions with adult distributions via a maximum likelihood analysis. The models obtained from this method can then be used to predict the spatial distributions of seedlings from adult distributions. We calibrated recruitment functions for 10 tree species characteristic of transition oak- northern hardwood forests. Significant differences were found in recruit abundances and spatial distributions. Predicted seedling recruitment limitation for test stands varied sub- stantially between species, with little recruitment limitation for some species and strong recruitment limitation for others. Recruitment was limited due to low overall recruit production or to restricted recruit dispersion. When these seedling recruitment parameters were incorporated into a spatial, individual-based model of forest dynamics, called SOR- TIE, alterations of recruitment parameters produced substantial changes in species abun- dance, providing additional support for the potential importance of seedling recruitment processes in community structure and dynamics.

/pdf/seedling-recruitment-in-forests-calibrating-models-to-158rauq9u6.pdf

John A. Silander

Papers

A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter

Forest models defined by field measurements : Estimation, error analysis and dynamics

A wavelet transform method to merge Landsat TM and SPOT panchromatic data

Juvenile Tree Survivorship as a Component of Shade Tolerance

Seedling recruitment in forests: calibrating models to predict patterns of tree seedling dispersion'