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

The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the “spatial” aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G*i statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify “outliers,” as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.1995.tb00338.x

Local Indicators of Spatial Association—LISA

The term ''urban stream syndrome'' describes the consistently observed ecological degra- dation of streams draining urban land. This paper reviews recent literature to describe symptoms of the syndrome, explores mechanisms driving the syndrome, and identifies appropriate goals and methods for ecological restoration of urban streams. Symptoms of the urban stream syndrome include a flashier hy- drograph, elevated concentrations of nutrients and contaminants, altered channel morphology, and reduced biotic richness, with increased dominance of tolerant species. More research is needed before generaliza- tions can be made about urban effects on stream ecosystem processes, but reduced nutrient uptake has been consistently reported. The mechanisms driving the syndrome are complex and interactive, but most impacts can be ascribed to a few major large-scale sources, primarily urban stormwater runoff delivered to streams by hydraulically efficient drainage systems. Other stressors, such as combined or sanitary sewer overflows, wastewater treatment plant effluents, and legacy pollutants (long-lived pollutants from earlier land uses) can obscure the effects of stormwater runoff. Most research on urban impacts to streams has concentrated on correlations between instream ecological metrics and total catchment imperviousness. Recent research shows that some of the variance in such relationships can be explained by the distance between the stream reach and urban land, or by the hydraulic efficiency of stormwater drainage. The mech- anisms behind such patterns require experimentation at the catchment scale to identify the best management approaches to conservation and restoration of streams in urban catchments. Remediation of stormwater impacts is most likely to be achieved through widespread application of innovative approaches to drainage design. Because humans dominate urban ecosystems, research on urban stream ecology will require a broadening of stream ecological research to integrate with social, behavioral, and economic research.

https://www.jstor.org/stable/10.1899/04-028.1

The urban stream syndrome: current knowledge and the search for a cure

Understanding the processes and patterns of gene flow and local adaptation requires a detailed knowledge of how landscape characteristics structure populations. This understanding is crucial, not only for improving ecological knowledge, but also for managing properly the genetic diversity of threatened and endangered populations. For nearly 80 years, population geneticists have investigated how physiognomy and other landscape features have influenced genetic variation within and between populations. They have relied on sampling populations that have been identified beforehand because most population genetics methods have required discrete populations. However, a new approach has emerged for analyzing spatial genetic data without requiring that discrete populations be identified in advance. This approach, landscape genetics, promises to facilitate our understanding of how geographical and environmental features structure genetic variation at both the population and individual levels, and has implications for ecology, evolution and conservation biology. It differs from other genetic approaches, such as phylogeography, in that it tends to focus on processes at finer spatial and temporal scales. Here, we discuss, from a population genetic perspective, the current tools available for conducting studies of landscape genetics.

/pdf/landscape-genetics-combining-landscape-ecology-and-qdkersxxkb.pdf

Landscape genetics: combining landscape ecology and population genetics

We present a new approach for defining groups of populations that are geographically homogeneous and maximally differentiated from each other. As a by-product, it also leads to the identification of genetic barriers between these groups. The method is based on a simulated annealing procedure that aims to maximize the proportion of total genetic variance due to differences between groups of populations (spatial analysis of molecular variance; samova). Monte Carlo simulations were used to study the performance of our approach and, for comparison, the behaviour of the Monmonier algorithm, a procedure commonly used to identify zones of sharp genetic changes in a geographical area. Simulations showed that the samova algorithm indeed finds maximally differentiated groups, which do not always correspond to the simulated group structure in the presence of isolation by distance, especially when data from a single locus are available. In this case, the Monmonier algorithm seems slightly better at finding predefined genetic barriers, but can often lead to the definition of groups of populations not differentiated genetically. The samova algorithm was then applied to a set of European roe deer populations examined for their mitochondrial DNA (mtDNA) HVRI diversity. The inferred genetic structure seemed to confirm the hypothesis that some Italian populations were recently reintroduced from a Balkanic stock, as well as the differentiation of groups of populations possibly due to the postglacial recolonization of Europe or the action of a specific barrier to gene flow.

/pdf/a-simulated-annealing-approach-to-define-the-genetic-4ofo4xvx9v.pdf

A simulated annealing approach to define the genetic structure of populations

Despite broad consensus on the power of experiments, correlational studies are still important in ecology, and may become more so as spatial studies proliferate. Conventional correlation analysis, however, (1) fundamentally conflicts with the basic ecological concept of limiting factors, and (2) ignores spatial structure in data, which can produce spuriously high correlations. Especially for field data, bivariate scattergrams often show factor—ceiling" distributions wherein data points are widely scattered beneath an upper limit, due to the action of other factors. Although most ecological information in such a graph resides in the upper limit, standard correlation/regression does not characterize such limits. If other factors have been measured, path analysis may be useful, but otherwise, direct description of ecological ceilings is desirable. Objective methods for doing so are barely known to ecologists; we review recent proposals for statistical testing and data display. For correcting correlations for spatial patchiness of the variables, another new technique has been proposed by Clifford, Richardson, and Hemon: by reducing the effective sample size to account for the autocorrelation it allows significance tests. We discuss these issues with reference to counts of glacier lily (Erythronium grandiflorum) seedlings, vegetative plants, and flowering plants in a square grid of 256 contiguous $2 \times 2$ m quadrats in subalpine meadow in western Colorado. We also measured soil moisture, pocket gopher activity, and soil rockiness. All six variables showed significant patchiness (spatial autocorrelation) at similar scales. The abundance of flowering plants was positively correlated with rockiness and negatively correlated with moisture and gopher activity. Although limited seed dispersal suggests that seedlings should be spatially associated with flowering plants, no such correlation existed: indeed, examination of the bivariate scatterplot suggests a negative association, in the particular and restricted sense that seedlings are abundant only in quadrats where flowering is low. We hypothesize that seed germination is higher in less rocky areas of deeper, moister soil than in the rocky areas where most seeds land, but that seedlings seldom reach maturity unless they are in a rocky refuge from predation. Results from path analysis are consistent with this hypothesis. Such an ecological situation should weaken natural selection on characters enhancing seed dispersal.

Untangling Multiple Factors in Spatial Distributions: Lilies, Gophers, and Rocks

Dispersion du pollen d'E. grandifolium par le bourdon etudie a l'aide du polymorphisme de la couleur du pollen, contribution a la variation du succes reproducteur mâle et au flux genique

Dispersal of erythronium grandiflorum pollen by bumblebees: implications for gene flow and reproductive success.

We review the recently developed local spatial autocorrelation statistics Zi, Ci, Gi, and G,?. We discuss two alternative randomization assumptions, total and conditional, and then newly derive expectations and variances under conditional randomization for Zi and ci, as well as under total randomization for ci. The four statistics are tested by a biological simulation model from population genetics in which a population lives on a 21 x 21 lattice of stepping stones (sixtyyour individuals per stone) and reproduces and disperses over a number of generations. Some designs model global spatial autocorrelation, others spatially random sugaces. We find that spatially random designs give reliable test results by permutational methods of testing signif cance. Globally autocorrelated designs do not fit expectations by any of the three tests we employed. Asymptotic methods of testing signif came failed consistently, regardless of design. Because most biological data sets are autocorrelated, significance testing for local spatial autocorrelation is problematic. However, the statistics are informative when employed in an exploratory manner. We found that hotspots (positive local autocorrelation) and coldspots (negative local autocorrelation) are successfully distinguished in spatially autocorrelated, biologically plausible data sets. Work on spatial autocorrelation (SA) has been frequently featured in the pages of this journal. Pioneered in the 1950s, this topic was comprehensively treated by Cliff and Ord (1973, 1981). It was introduced to biology by Jumars, Thistle, and Jones (1977) and by Sokal and Oden (1978a, b). It has been extensively applied by ecologists and population biologists to a variety of organisms including humans, not only to test for departures from spatial randomness, but also to make inferences about the processes underlying the observed patterns. Representative studies are Sokal and Wartenberg (1981), Sokal and Jacquez (1991), Epperson (1993), Barbujani (1987), Sokal, Oden, and Barker (1987), and Sokal and Thomson (1998). Contribution no. 1016 in Ecology and Evolution from SUNY-Stony Brook. This research was supported by grant DEB9220538 from the National Science Foundation to Robert R. Sokal. The extensive computations were made possibIe by a grant of supercomputer time from the Cornell Theory Center.

Local Spatial Autocorrelation in a Biological Model

Spatial autocorrelation (SA) methods have recently been extended to include the detection of local spatial autocorrelation at individual sampling stations. We review the formulas for these statistics and report on the results of an extensive population-genetic simulation study we have published elsewhere to test the applicability of these methods in spatially distributed biological data. We find that most biological variables exhibit global SA, and that in such cases the methods proposed for testing the significance of local SA coefficients reject the null hypothesis excessively. When global SA is absent, permutational methods for testing significance yield reliable results. Although standard errors have been published for the local SA coefficients, their employment using an asymptotically normal approach leads to unreliable results; permutational methods are preferred. In addition to significance tests of suspected non-stationary localities, we can use these methods in an exploratory manner to find and identify hotspots (places with positive local SA) and coldspots (negative local SA) in a dataset. We illustrate the application of these methods in three biological examples from plant population biology, ecology and population genetics. The examples range from the study of single variables to the joint analysis of several variables and can lead to successful demographic and evolutionary inferences about the populations studied.

Local spatial autocorrelation in biological variables

Trapline foraging—repealed sequential visits to a series of feeding locations—presents interesting problems seldom treated in foraging models. Work on traplining is hampered by the lack of statistical, operational approaches for detecting its existence and measuring its strength. We propose several statistical procedures, illustrating them with records of interplant flight sequences by bumble bees visiting penstemon flowers. An asymmetry test detects deviations from binomial expectation in the directionality of visits between pairs of plants. Several tests compare data from one bee to another frequencies of visits to plants and frequencies of departures to particular destinations are compared using contingency tables; similarities of repeated sequences within bees are compared to those between bees by means of sequence alignment and Mantel tests. We also compared observed movement patterns to those generated by null models designed to represent realistic foraging by non-traplining bees, examining: temporal patterns of the bee's spatial displacement from its starting point using spectral analysis; the variance of return times to particular plants; and the sequence alignment of repeated cycles within sequences. We discuss the different indications and the relative strengths of these approaches. Krj words: asymmetry test, Bombus, foraging, Mantel test, null model, Ptnsttwum, sequence, trapline. [Bthav Ecol 8:199-210 (1997)]

Barbara A. Thomson

Papers

Untangling Multiple Factors in Spatial Distributions: Lilies, Gophers, and Rocks

Dispersal of erythronium grandiflorum pollen by bumblebees: implications for gene flow and reproductive success.

Local Spatial Autocorrelation in a Biological Model

Local spatial autocorrelation in biological variables

Trapline foraging by bumble bees: II. Definition and detection from sequence data