Dear list members,
There are also other approaches to estimate species richness using
capture-recapture models and occupancy models which can take into
account detection probability.
See softwares SPECRICH, SPECRICH2 and PRESENCE at the Patuxet web site:
http://www.mbr-pwrc.usgs.gov/software.html
Best,
Manuel Spínola
--
Manuel Spínola, Ph.D.
Instituto Internacional en Conservación y Manejo de Vida Silvestre
Universidad Nacional
Apartado 1350-3000
Heredia
COSTA RICA
mspin...@una.ac.cr
mspinol...@gmail.com
Teléfono: (506) 2277-3598
Fax: (506) 2237-7036
Peter Solymos wrote:
Dear List,
Thierry's suggestion, to use Binomial(p, N) for modelling species
richness, assumes that the probability of finding a new species (p)
depends e.g. on covaiates (logit(p)=X%*%beta), while different species
share the same probability to be encountered (N independent? trials --
as Alain noted). Because ecological communities rarely have uniform
species-abundance distribution, and species specific probabilities
will probably differ among sites due to different responses to
environmental factors, the Binomial approximation has limited
applicability. And this can be true even for the Poisson. So it turns
out that modeling marginal statistics (total abundance/richness) of
the community matrix requires modeling the communities first...
By the way, Nathan wrote me off list, that he used log transformed
richness, which is the traditional species-area way of handling
richness. He was more interested in variance components, but this
diverged conversation also brought up some interesting views.
Cheers,
Peter
On Mon, Feb 8, 2010 at 6:12 AM, ONKELINX, Thierry
<thierry.onkel...@inbo.be> wrote:
Dear Gavin,
For many taxonomical groups to total number of species is rather low. Ecologist
can either use a fixed total number of species or use some expert knowledge to
get the total number of species, taking only large scale effect into account.
E.g. freshwater fish not entering seawater, plant species not occuring above a
given altitude, ...
The number of absent species is then the total number minus the number of
present species.
As long as the number of present species is much smaller than the total number
of species, a Poisson distribution seems a reasonable simplification. But what
if you are studying a small taxonomical group? Let's assume a group with 10
species and frequently 8 or 9 of them are present. Can assume that the species
richness follows a Poisson distribution in that case?
Best regards,
Thierry
----------------------------------------------------------------------------
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium
Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
thierry.onkel...@inbo.be
www.inbo.be
To call in the statistician after the experiment is done may be no more than
asking him to perform a post-mortem examination: he may be able to say what the
experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not ensure
that a reasonable answer can be extracted from a given body of data.
~ John Tukey
-----Oorspronkelijk bericht-----
Van: Gavin Simpson [mailto:gavin.simp...@ucl.ac.uk]
Verzonden: maandag 8 februari 2010 11:14
Aan: ONKELINX, Thierry
CC: Peter Solymos; Nathan Lemoine
Onderwerp: Re: [R-sig-eco] multiple regression
On Mon, 2010-02-08 at 11:02 +0100, ONKELINX, Thierry wrote:
Peter,
I would think that the species richness is binomial distributed. Since
there is a maximum number of species that can be present. Therefore I
would model it like
glm(cbind(number.present, number.absent) ~ covariates, family =
binomial)
Hi Thierry,
How would one estimate number.absent? To my mind, that sounds some what
Rumsfeldian...
G
HTH,
Thierry
----------------------------------------------------------------------
------
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek team Biometrie & Kwaliteitszorg
Gaverstraat 4 9500 Geraardsbergen Belgium
Research Institute for Nature and Forest team Biometrics & Quality
Assurance Gaverstraat 4 9500 Geraardsbergen Belgium
tel. + 32 54/436 185
thierry.onkel...@inbo.be
www.inbo.be
To call in the statistician after the experiment is done may be no more than
asking him to perform a post-mortem examination: he may be able to say what the
experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not ensure
that a reasonable answer can be extracted from a given body of data.
~ John Tukey
-----Oorspronkelijk bericht-----
Van: r-sig-ecology-boun...@r-project.org
[mailto:r-sig-ecology-boun...@r-project.org] Namens Peter Solymos
Verzonden: zaterdag 6 februari 2010 20:53
Aan: Nathan Lemoine
CC: r-sig-ecology@r-project.org
Onderwerp: Re: [R-sig-eco] multiple regression
Nathan,
Species richness is categorical, so if your richness values are usually low (say
< 20), you should consider the use of Poisson GLM, or log-transform your
response (and log is the canonical link function for Poisson GLM). This usually
improves the model fit. And this might apply to abundance as well.
If you use lm(), you can inspect the residual variance of the models
after excluding one of the covariates. The increase in residual
variance compared to the full model will tell which variance component
is higher (explains more of your data). Or you may as well inspect the
anova() table of the fitted model (both for lm or glm).
Best,
Peter
Péter Sólymos
Alberta Biodiversity Monitoring Institute Department of Biological
Sciences CW 405, Biological Sciences Bldg University of Alberta
Edmonton, Alberta, T6G 2E9, Canada
Phone: 780.492.8534
Fax: 780.492.7635
On Sat, Feb 6, 2010 at 9:17 AM, Nathan Lemoine <lemoine.nat...@gmail.com> wrote:
Hi everyone,
I'm trying to fit a multiple regression model and have run into some
questions regarding the appropriate procedure to use. I am trying to
compare fish assemblages (species richness, total abundance, etc.)
to metrics of habitat quality. I swam transects are recorded all
fish observed, then I measured the structural complexity and live coral cover
over each transect.
I am interested in weighting which of these two metrics has the
largest influence on structuring fish assemblages.
My strategy was to use a multiple linear regression. Since the data
were in two different measurement units, I scaled the variables to a
mean of 0 and std. dev. of 1. This should allow me to compare the
sizes of the beta coefficients to determine the relative (but not
absolute) importance of each habitat variable on the fish assemblage, correct?
My model was lm(Species Richness~Complexity+Coral Cover). I had run
a full model and found no evidence of interactions, so I ran it
without the interaction present.
It turns out coral cover was not significant in any regression. I
have been told that the test I used was incorrect and that the
appropriate procedure is a stepwise regression, which would,
undoubtedly, provide me with Complexity as a significant variable and remove
Coral Cover.
This seems to me to be the exact same interpretation as the above
model. So, since I'm very new to all of this, I am wondering how to
tell whether one model is 'incorrect' or 'inappropriate' given that
they yield almost identical results? What are the advantages of a
stepwise regression over a standard multiple regression like I have run?
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Druk dit bericht a.u.b. niet onnodig af.
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--
Manuel Spínola, Ph.D.
Instituto Internacional en Conservación y Manejo de Vida Silvestre
Universidad Nacional
Apartado 1350-3000
Heredia
COSTA RICA
mspin...@una.ac.cr
mspinol...@gmail.com
Teléfono: (506) 2277-3598
Fax: (506) 2237-7036
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