База резюме и ссылок по экологии бентоса

Видовое разнообразие.

Видовое разнообразие.

Volume 25 Issue 5 Page 541-550 - October 2002

Geographic range, turnover rate and the scaling of species diversity

Hector T. Arita and Pilar Rodriguez

The study of the relative roles of local and regional processes in determining the scaling of species diversity is a very active field in current ecology. The importance of species turnover and the species-range-size frequency distributions in determining how local and regional species diversity are linked has been recognised by recent approaches. Here we present a model, based on a system of fully nested sampling quadrats, to analyse species diversity at several scales. Using a recursive procedure that incorporates increasingly smaller scales and a multiplicative formula for relating local and regional diversity, the model allows the simultaneous depiction of alpha, beta and gamma diversity in a single "species-scale plot". Species diversity is defined as the number of ranges that are intersected by sampling quadrats of various sizes. The size, shape and location of individual species ranges determine diversity at any scale, but the average point diversity, measured at hypothetical zero-area localities, is determined solely by the size of individual ranges, regardless of their shape and location. The model predicts that if the species-area relationship is a power function, then beta diversity must be scale invariant if measured at constant scale increments. Applying the model to the mammal fauna of four Mexican regions with contrasting environmental conditions, we found that: 1) the species-range-size frequency distribution at the scale of the Mexican regions differs from the log-normal pattern reported for the national and continental scales. 2) Beta diversity is not scale-invariant within each region, implying that the species-area relationship (SAR) does not follow a power function. 3) There is geographic variation in beta diversity. 4) The scaling of diversity is directly linked to patterns of species turnover rate, and ultimately determined by patterns in the geographic distribution of species. The model shows that regional species diversity and the average distribution range of species are the two basic data necessary to predict patterns in the scaling of species diversity.
СОВРЕМЕННЫЕ ПРОБЛЕМЫ ИЗУЧЕНИЯ МОРСКОГО БИОЛОГИЧЕСКОГО РАЗНООБРАЗИЯ

Авторы Андрей Владимирович Адрианов

Журнал Биология моря

Год выпуска 2004 Дата январь 21

Том 30 Номер 1

Страницы 3-19 Статус

Обсуждаются терминология и шесть предложенных уровней биологического разнообразия. С использованием современных данных и расчетов проведено сравнение видового богатства и таксономического разнообразия наземных, пресноводных и морских организмов. К настоящему времени описаны около 1.5 млн. видов наземных и 320 тыс. видов водных организмов. Несмотря на длительную историю изучения, описаны лишь около 280 тыс. морских видов, из которых 180 тыс. видов представлены беспозвоночными животными. Из 33 типов многоклеточных животных 31 тип найден в море, из них 13 представлены исключительно морскими формами. Представители 17 типов многоклеточных животных найдены в пресных водах, и лишь 11 типов включают наземных обитателей. Представители двух типов - пресноводные Micrognathozoa и наземные Onychophora - не найдены в морских биотопах. Обсуждаются расчеты морского биологического разнообразия коралловых рифов, прибрежных экосистем, морского макробентоса и мейофауны. Для каждого типа многоклеточных приведены современные данные по количеству описанных видов; обсуждаются прогнозы о количестве ожидаемых видов. Разнообразие глубоководного макробентоса оценено на уровне 25 млн. видов; в мейофауне предполагаются около 20-30 млн. видов, причем более 10 млн. придется на морских нематод. Рассматриваются гипотезы, объясняющие высокое видовое разнообразие морского глубоководного макробентоса и мейофауны.

Методы пространственного анализа и типы размещений.

Фрактальное распределение видов.

Volume 97 Issue 3 Page 378-386 - June 2002

Fractal species distributions do not produce power-law species-area relationships

Jack J. Lennon, William E. Kunin and Stephen Hartley

We derive the species-area relationship (SAR) expected from an assemblage of fractally distributed species. If species have truly fractal spatial distributions with different fractal dimensions, we show that the expected SAR is not the classical power-law function, as suggested recently in the literature. This analytically derived SAR has a distinctive shape that is not commonly observed in nature: upward-accelerating richness with increasing area (when plotted on log-log axes). This suggests that, in reality, most species depart from true fractal spatial structure. We demonstrate the fitting of a fractal SAR using two plant assemblages (Alaskan trees and British grasses). We show that in both cases, when modelled as fractal patterns, the modelled SAR departs from the observed SAR in the same way, in accord with the theory developed here. The challenge is to identify how species depart from fractality, either individually or within assemblages, and more importantly to suggest reasons why species distributions are not self-similar and what, if anything, this can tell us about the spatial processes involved in their generation.

Эволюция перемещений и вагильности.

Volume 97 Issue 2 Page 229-236 - May 2002

The evolution of dispersal distance in spatially-structured populations

David J. Murrell, Justin M. J. Travis and Calvin Dytham

Most evolutionary models of dispersal have concentrated on dispersal rate, with emigration being either global or restricted to nearest neighbours. Yet most organisms fall into an intermediate region where most dispersal is local but there is a wide range of dispersal distances. We use an individual-based model with 2500 patches each with identical local dynamics and show that the dispersal distance is under selection pressure. The dispersal distance that evolves is critically dependent on the ecological dynamics. When the cost of dispersal increases linearly with distance, selection is for short-distance dispersal under stable and damped local dynamics but longer distance dispersal is favoured as local dynamics become more complex. For the cases of stable, damped and periodic patch dynamics global patch synchrony occurs even with very short-distance dispersal. Increasing the scale of dispersal for chaotic local dynamics increases the scale of synchrony but global synchrony does not neccesarily occur. We discuss these results in the light of other possible causes of dispersal and argue for the importance of incorporating non-equilibrium population dynamics into evolutionary models of dispersal distance.

Масштабы эффектов хищничества и нарушений.

Lancaster J.

Institution

Inst. Ecol. Resour. Management, Univ. Edinburgh, Darwin Build., Mayfield

Rd., Edinburgh EH9 3JU, UK.

Title

Source

The effects of hydraulic disturbances on the impact of two predatory

benthic invertebrates on their prey were examined in a stream at two

distinct spatial scales. At the scale of small habitat patches (0.0625

m-2), hydraulic patch type was an important determinant of the

microdistribution of prey and predators. Prey abundances were similar

across all patch types at baseflow, but local densities were higher in

patches identified as low-flow refugia after periods of high and

fluctuating flow. The microdistribution pattern of predatory larvae of a

caddis-fly, Plectrocnemia conspersa, was similar to that of its prey,

whereas predatory larvae of an alderfly, Sialis fuliginosa, did not shift

their microdistribution significantly with discharge and were always most

abundant in low-flow refugia. There was little evidence of an aggregative

response of predators with prey, even though both predators and prey are

mobile. Both predator species showed similar patch-specific patterns of

per capita consumption rates: uniform consumption rates across hydraulic

patch types at low and moderate flows, but highest in flow refugia during

high flows. Species-specific patterns, however, were apparent in the

magnitude and direction of differences between consumption rates during

disturbance events, and in comparable patches at base flow: At high flow,

consumption rates for P. conspersa were exaggerated (3.9 times higher) in

flow refugia but "at par" in other patches; for S. fuliginosa they were

"at par" in flow refugia but reduced in other patches (up to 3.3. times

lower). These differences may be related to species-specific foraging

behaviours (search vs ambush predators) and the influence of prey

movements on feeding success. Using the patch-scale results only, it is

difficult to predict the effects of physical disturbance on predation

intensity at the larger scales of whole habitats, populations or

communities. At the large scale ( gt 200 m-2), net predator impacts were

estimated over the stream reach, using a spatially explicit model that

accounts, in an additive way, for habitat heterogeneity and patch-specific

responses of predators and prey. The relationship between predator impact

over the whole reach and hydraulic disturbance differed for the two

predators. The predator impact of S. fuliginosa decreased with increasing

hydraulic disturbance, as predicted by the harsh-benign hypothesis. There

was no directional trend for P. conspersa, however, and maximum predator

impact may occur at intermediate disturbance levels. For the prey

community in this stream, predation pressure from S. fuliginosa appears to

fluctuate directly with the discharge hydrograph, whereas predation from

P. conspersa may be more persistent. Flow refugia may play a dual role in

the structure of stream communities by preventing catastrophic mortality

of animals (predators and prey) from physical forces during disturbances,

and by maintaining (or perhaps increasing) predation pressure. Summing the

effects of species interactions in small habitat patches to the larger

scale of a whole stream reach indicates that the scale of approach

influences the observed patterns and their implied underlying process.

Масштаб экол.взаимодействий, бентос литорали озер.

Johnson R.K., Goedkoop W.

Littoral macroinvertebrate communities: spatial scale and ecological

relationships // Freshwater Biology. 2002. V.47: 1840-1854.

См файл Johnson.pdf

Масштаб неоднородности речных улиток.

Kawata M., Hiroko A.

Perceptual scales of spatial heterogeneity of periphyton for freshwater snails // Ecology Letters. 1999. 2: 210-214. См файл kawata.pdf

Масштаб в анализе размещений.

Ecography

Volume 25 Issue 5 Page 626 - October 2002

A balanced view of scale in spatial statistical analysis

J. L. Dungan, J. N. Perry, M. R. T. Dale, P. Legendre, S. Citron-Pousty, M.-J. Fortin, A. Jakomulska, M. Miriti and M. S. Rosenberg

Concepts of spatial scale, such as extent, grain, resolution, range, footprint, support and cartographic ratio are not interchangeable. Because of the potential confusion among the definitions of these terms, we suggest that authors avoid the term "scale" and instead refer to specific concepts. In particular, we are careful to discriminate between observation scales, scales of ecological phenomena and scales used in spatial statistical analysis. When scales of observation or analysis change, that is, when the unit size, shape, spacing or extent are altered, statistical results are expected to change. The kinds of results that may change include estimates of the population mean and variance, the strength and character of spatial autocorrelation and spatial anisotropy, patch and gap sizes and multivariate relationships. The first three of these results (precision of the mean, variance and spatial autocorrelation) can sometimes be estimated using geostatistical support-effect models. We present four case studies of organism abundance and cover illustrating some of these changes and how conclusions about ecological phenomena (process and structure) may be affected. We identify the influence of observational scale on statistical results as a subset of what geographers call the Modifiable Area Unit Problem (MAUP). The way to avoid the MAUP is by careful construction of sampling design and analysis. We recommend a set of considerations for sampling design to allow useful tests for specific scales of a phenomenon under study. We further recommend that ecological studies completely report all components of observation and analysis scales to increase the possibility of cross-study comparisons.

Роль размещений в анализе полевых данных.

Ecography

Volume 25 Issue 5 Page 601 - October 2002

The consequences of spatial structure for the design and analysis of ecological field surveys

Pierre Legendre, Mark R. T. Dale, Marie-Josee Fortin, Jessica Gurevitch, Michael Hohn and Donald Myers

In ecological field surveys, observations are gathered at different spatial locations. The purpose may be to relate biological response variables (e.g., species abundances) to explanatory environmental variables (e.g., soil characteristics). In the absence of prior knowledge, ecologists have been taught to rely on systematic or random sampling designs. If there is prior knowledge about the spatial patterning of the explanatory variables, obtained from either previous surveys or a pilot study, can we use this information to optimize the sampling design in order to maximize our ability to detect the relationships between the response and explanatory variables?

The specific questions addressed in this paper are: a) What is the effect (type I error) of spatial autocorrelation on the statistical tests commonly used by ecologists to analyse field survey data? b) Can we eliminate, or at least minimize, the effect of spatial autocorrelation by the design of the survey? Are there designs that provide greater power for surveys, at least under certain circumstances? c) Can we eliminate or control for the effect of spatial autocorrelation during the analysis? To answer the last question, we compared regular regression analysis to a modified t-test developed by Dutilleul for correlation coefficients in the presence of spatial autocorrelation.

Replicated surfaces (typically, 1000 of them) were simulated using different spatial parameters, and these surfaces were subjected to different sampling designs and methods of statistical analysis. The simulated surfaces may represent, for example, vegetation response to underlying environmental variation. This allowed us 1) to measure the frequency of type I error (the failure to reject the null hypothesis when in fact there is no effect of the environment on the response variable) and 2) to estimate the power of the different combinations of sampling designs and methods of statistical analysis (power is measured by the rate of rejection of the null hypothesis when an effect of the environment on the response variable has been created).

Our results indicate that: 1) Spatial autocorrelation in both the response and environmental variables affects the classical tests of significance of correlation or regression coefficients. Spatial autocorrelation in only one of the two variables does not affect the test of significance. 2) A broad-scale spatial structure present in data has the same effect on the tests as spatial autocorrelation. When such a structure is present in one of the variables and autocorrelation is found in the other, or in both, the tests of significance have inflated rates of type I error. 3) Dutilleul's modified t-test for the correlation coefficient, corrected for spatial autocorrelation, effectively corrects for spatial autocorrelation in the data. It also effectively corrects for the presence of deterministic structures, with or without spatial autocorrelation.

The presence of a broad-scale deterministic structure may, in some cases, reduce the power of the modified t-test.

Выбор стат.методов анализа размещений.

Ecography

Volume 25 Issue 5 Page 578-600 - October 2002

Illustrations and guidelines for selecting statistical methods

for quantifying spatial pattern in ecological data

J. N. Perry, A. M. Liebhold, M. S. Rosenberg, J. Dungan, M. Miriti, A. Jakomulska and S. Citron-Pousty

This paper aims to provide guidance to ecologists with limited experience in spatial analysis to help in their choice of techniques. It uses examples to compare methods of spatial analysis for ecological field data. A taxonomy of different data types is presented, including point- and area-referenced data, with and without attributes. Spatially and non-spatially explicit data are distinguished. The effects of sampling and other transformations that convert one data type to another are discussed; the possible loss of spatial information is considered.

Techniques for analyzing spatial pattern, developed in plant ecology, animal ecology, landscape ecology, geostatistics and applied statistics are reviewed briefly and their overlap in methodology and philosophy noted. The techniques are categorized according to their output and the inferences that may be drawn from them, in a discursive style without formulae. Methods are compared for four case studies with field data covering a range of types. These are: 1) percentage cover of three shrubs along a line transect; 2) locations and volume of a desert plant in a 1 ha area; 3) a remotely-sensed spectral index and elevation from 105 km2 of a mountainous region; and 4) land cover from three rangeland types within 800 km2 of a coastal region. Initial approaches utilize mapping, frequency distributions and variance-mean indices. Analysis techniques we compare include: local quadrat variance, block quadrat variance, correlograms, variograms, angular correlation, directional variograms, wavelets, SADIE, nearest neighbour methods, Ripley's (t), and various landscape ecology metrics.

Our advice to ecologists is to use simple visualization techniques for initial analysis, and subsequently to select methods that are appropriate for the data type and that answer their specific questions of interest. It is usually prudent to employ several different techniques.

Статистика в анализе размещений.

Ecography

Volume 25 Issue 5 Page 553-557 - October 2002

Integrating the statistical analysis of spatial data in ecology

A. M. Liebhold and J. Gurevitch

In many areas of ecology there is an increasing emphasis on spatial relationships. Often ecologists are interested in new ways of analyzing data with the objective of quantifying spatial patterns, and in designing surveys and experiments in light of the recognition that there may be underlying spatial pattern in biotic responses. In doing so, ecologists have adopted a number of widely different techniques and approaches derived from different schools of thought, and from other scientific disciplines. While the adaptation of a diverse array of statistical approaches and methodologies for the analysis of spatial data has yielded considerable insight into various ecological problems, this diversity of approaches has sometimes impeded communication and retarded more rapid progress in this emergent area. Many of these different statistical methods provide similar information about spatial characteristics, but the differences among these methods make it difficult to compare the results of studies that employ contrasting approaches. The papers in this mini-series explore possible areas of agreement and synthesis between a diversity of approaches to spatial analysis in ecology.

Методы пространственного анализа и типы размещений.

Ecography Volume 25 Issue 5 Page 558-577 - October 2002

Conceptual and mathematical relationships among methods for spatial analysis

Mark R. T. Dale, Philip Dixon, Marie-Josee Fortin, Pierre Legendre,

Donald E. Myers and Michael S. Rosenberg

Полный текст см. oikos3.pdf

A large number of methods for the analysis of the spatial structure of natural phenomena (for example, the clumping or overdispersion of tree stems, the positions of veins of ore in a rock formation, the arrangement of habitat patches in a landscape, and so on) have been developed in a wide range of scientific fields. This paper reviews many of the methods and describes the relationships among them, both mathematically, using the cross-product as a unifying principle, and conceptually, based on the form of a moving window or template used in calculation. The relationships among these methods suggest that while no single method can reveal all the important characteristics of spatial data, the results of different analyses are not expected to be completely independent of each other.

Межвидовые взаимодействия.

Речные рыбы: конкурентное вытеснение.

Authors: Douglas-ME Marsh-PC Minckley-WL

Indigenous Fishes of Western North-America and the Hypothesis of Competitive Displacement - Meda Fulgida (Cyprinidae) as a Case-Study

COPEIA 1994, Iss 1, pp 9-19

Recent invasion of low-elevation streams in the Colorado

River basin of western North America by the nonnative red shiner

(Cyprinella lutrensis) seems linked with the dramatic decline of

a threatened cyprinid (spikedace, Meda fulgida) native to the

Gila River subbasin. The mechanism by which red shiner impacts

spikedace is unknown. Two hypotheses have been offered:

displacement of the native through competitive interaction with

invader, and replacement of native by nonnative as a result of

environmental perturbation. To ascertain whether spikedace was

being actively displaced by red shiner, we compared niche

requirements of each in syntopy, allotopy, and disjunct

allopatry. Fishes were collected by seining one to three sites

in each of six different stream reaches; and current velocity,

substrate particle size, and water depth were measured at each

site. Red shiner occupied similar microhabitat whether

allopatric, allotopic, or syntopic with spikedace. Spikedace

occupied the same microhabitat when allopatric or allotopic to

red shiner. However, spikedace syntopic with red shiner

displayed a niche shift into currents significantly swifter than

those selected when in isolation. Displacement of spikedace by

red shiner suggests negative interspecific interactions

potentially detrimental to the indigenous species.

жүктеу/скачать 1.21 Mb.

Достарыңызбен бөлісу: