Paper 43

Conner, E.F. and E.D. McCoy. 1979. The statistics and biology of the species-area relationship. American Naturalist 113:791-833.

Blog by Rebbeca Kiat

Paper Authors: Edward F. Connor, Earl D. McCoy

(Commentary by Brian A. Maurer)

Brian A. Maurer

·      Ph.D. from  University of Arizona

·      Associate Professor at Michigan State University

“Ecology is understood to be the study of phenomena that occur on a wide range of spatial and temporal scales. My interest focuses on the largest spatial and longest temporal scales studied by ecologists.  To understand the importance of processes at geographical scales, it is important to understand how they connect to local scale processes.  Thus, I am interested in modeling population and community dynamics in a geographical context.  I am working with a variety of vertebrate organisms to model how population dynamics and abundance vary from one place to the next within their geographical ranges.  These focal questions, however, give rise to a number of different ancillary questions regarding such things as the ecological and evolutionary importance of body size, geographic patterns of species diversity, and resource use behavior.  Modeling ecological systems in space and time is a major tool that I use to answer questions about geographic scale ecological processes.  To this end, my lab uses a variety of quantitative and computational technologies. We intensively use geographical information systems (GIS) to analyze and model spatial processes.  Spatial statistics and related techniques from geostatistics provide the analytical framework for many of our statistical analyses.  Finally, use of nonlinear and spatially explicit mathematical modeling techniques allows us to develop theoretical approaches to large scale ecological systems.”

Edward F. Connor

·      Ph.D. from  Florida State University

·      Professor at San Francisco State University

“I am broadly interested in population and community ecology, statistical ecology, biogeography, insect-plant interactions, and sampling and monitoring design in ecology and conservation. My field research employs insects communities and insect-host plant systems to answer basic questions about the population dynamics of pest insects and the structure of insect communities. My interests in statistical ecology and biogeography lie in developing probabilitistic statistical procedures for the analysis of biogeographic data and in developing a rigorous framework for hypothesis testing and staitsical inference using both experimental and non-experimental  evidence in ecology. My work on sampling design focuses on questions concerning the optimal design and allocation of effort in monitoring plant, bird, and bee populations and communities.

My recent research has focused on trying to determine the mechanism by which insects induce pall galls (tumors) and its evolution, developing hierarchical models for abundance data, and automating avian population monitoring.”

Earl D. McCoy

·      Ph.D. from  Florida State University

·      Professor and Associate Chair at University of South Florida

“My students and I study a broad range of ecological and biogeographical problems. Many of our projects relate in some way to conservation biology, either in theory or in practice. Most of our current research deals with conservation and restoration of severely threatened upland habitats, particularly sandhill and scrub, in Florida. Within this framework, my students have focused their projects on a variety of topics: structure of gopher tortoise populations, demography and autecology of sand skinks, restoration of Florida mouse populations on lands mined for phosphate, and comparative biology of common and rare frogs, for example. Other students have focused their projects on topics such as methods of ecological analysis and the composition of species' assemblages. My own research encompasses additional topics in the areas of disturbance ecology, particularly fire ecology; biogeographical theory; and the philosophical basis of ecology.

Virtually all of the research being conducted by my students is aimed at solving particular problems and, therefore, probably would be labeled "applied research" by many persons.”

Paper - Summary/Main points:

·      Concerned with use and interpretation of species-area curves.

·      Power function model vs. alternative models – is it the best model? Theoretically unique in explaining observations?

·      Biological explanations for these models?

(1) “Does the equilibrium model provide a unique theoretical basis for the species-area relationship?”

·      Habitat diversity hypothesis - with increased area, more variety of habitats?

·      Area-per se hypothesis - equilibrium theory of island biogeography?

·      Sampling hypothesis – passive sampling; increased sampling for larger areas?

(2) “Is the power function model (log/log), derived from equilibrium theory, the best model of the species-area relationship?”

·      Is there a model that statistically fits the data the best?

·      Danger in applying a model without comparison to other models/ignoring important assumption made when applying a model.

·      Analysis of 100 data sets of species-area curves from literature and re-analyzing using various models – found no best-fit model.

·      Note: did not look at curvilinear models?

·      The power function and the untransformed models provide good fits most frequently.

(3) “Can the parameters of the power function or other species-area models be interpreted biologically?”

·      Closer look at parameter values for models (e.g. z , k, log k)

·      Slope parameters – predictions and limitations and what this means

·      Published predictions and interpretations at the time were not supported by available evidence – needed more data/due to the nature of the claim could not be tested

Questions:

·      In this paper, the authors also discuss Arrhenius’s 1920 & 1921 papers and the equations from them (note previous reading). Were there limitations to these equations or were there underlying assumptions?

·      Do we have new evidence that might better explain some of these models now, biologically?

·      Why did the authors not look not look at curvilinear models? Is this relevant?