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Case Study: Distribution and Abundance of Tree Species Along Climate Gradients. (Sometimes the PDF is more interesting than the actual model…). - PowerPoint PPT Presentation
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Case Study:
Distribution and Abundance of Tree Species Along Climate Gradients
(Sometimes the PDF is more interesting than the actual
model…)Canham, C. D. and R. Q. Thomas. 2010. Frequency, not relative abundance, of temperate tree species varies along climate gradients in eastern North America. Ecology 91:3433-3440
The Data
Relative abundance of the 24 most common tree species in the northeastern US- In ~ 20,000 US Forest Service Forest Inventory and Analysis
(FIA) plots from 19 northeastern states (Maine to Wisconsin, south to Kentucky and Virginia)
Climate (mean annual temperature and average annual precipitation) for each plot, averaged over the period since the previous census- Using 800-m resolution gridded PRISM climate data, with bi-
linear interpolation to true plot locations
The Basic Question
How does the abundance of tree species vary along climate gradients?
http://www.fs.fed.us/ne/delaware/atlas/s318.html
Basic Approach
Develop regression models that predict abundance of a given tree species in a plot as a function of climate at the location of the plot…
Initial decisions:1. What to use as a measure of
abundance?I chose relative abundance over absolute abundance
2. What sorts of functions could describe variation in relative abundance along climate gradients
Compare a Gaussian function with a null model that was flat
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Mean Annual Temperature (oC)
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But is a flat line an appropriate null hypothesis?
What about range limits?
And shouldn’t even the Gaussian model have truncated tails?
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So, what do the data look like?
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Acer rubrum (red maple) Quercus prinus ( chestnut oak)
It doesn’t look like there’s much hope for nice Gaussian niche curves…
A first regression model (in R)
# Square Gaussian - predicts mean of 0 when below lo or above hi
square.gauss.model <- function(a,m,b,lo,hi,X){ ifelse(X < lo,0,ifelse(X > hi,0,a*exp(-0.5*(((X-m)/b)^2)))) }
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5 Parameters:• “a” determines height of curve at mode• “m” temperature at peak of curve• “b” breadth of the curve• “lo” lower temperature limit• “hi” upper temperature limit
Note that a separate “null” model is not really necessary, because if “b” is large, the curve is flat
What might be an appropriate PDF?
ACRU
Relative abundance
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000The data are clearly not normally
distributed…
In fact, there are a whole lot of zeros….
Divide the analysis up into two parts
Predict probability of “presence” (i.e. non-zero abundance) –
Separately predict relative abundance when presentACRU
Relative abundance
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These two components of abundance have very different
ecological meanings….
A PDF for relative abundance when present:The Gamma Function
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Gamma PDF
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P(x
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mean = 10%mean = 20%mean = 50%mean = 80%
mean = 10%mean = 20%mean = 50%mean = 80%
mean = 10%mean = 20%mean = 50%mean = 80%
Gamma PDF with scale parameter = 22
One likely choice for plots where a species is present…
First ResultsRelative Abundance (when present)
Bottom line: for all 24 species, relative abundance is highly variable, but shows very little trend in the mean across the entire range of a species’ climate niche limits
Note: these results are for relative abundance, given that a species is present…
But what about all of those zeros?Creating a PDF that can model both
“presence” and “relative abundance”
“Zero-Inflated Distributions”
ACRU
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# ZERO INFLATED NORMAL PDFzinf_norm_PDF <- function(x,mean,sd,pz){ log(ifelse(x==0,pz + (1-pz)*dnorm(0,mean,sd,log=F), (1-pz)*dnorm(x,mean,sd,log=F))) }
# ZERO INFLATED GAMMA PDFzinf_gamma_PDF_climate <- function(x,mean,scale,px){ shape <- mean/scale loglh <- log(ifelse(x==0,pz + (1-pz)*dgamma(0,shape=shape,scale=scale,log=F), (1-pz)*dgamma(x,shape=shape,scale=scale,log=F))) return(loglh) }
But why stop there? Is presence constant across climate gradients, or
does it vary?
# ZERO INFLATED GAMMA PDF with pz a gaussian function of the# independent (climate) variable
zinf_gamma_PDF_climate <- function(x,mean,scale,pa,pm,pb,px){ shape <- mean/scale
pz <- 1 - pa*(exp(-0.5*(((px-pm)/pb)^2))) # px is temp or precip in the plot
loglh <- log(ifelse(x==0,pz + (1-pz)*dgamma(0,shape=shape,scale=scale,log=F), (1-pz)*dgamma(x,shape=shape,scale=scale,log=F))) return(loglh)}
One final complication…
How can I factor range limits into the PDF?- If probability of presence is modeled as a Gaussian function of
climate, but with truncated tails (climatic limits), what likelihood should be assigned to plots outside the estimated climatic limits? (my answer – 1 in a million…)
# CLIMATE DEPENDENT ZERO INFLATED GAMMA, WITH LIMITS PDF - # ARBITRARILY SET Prob(x>0|X=0) = 0.000001zinf_limits_gamma_PDF_climate <- function(x,mean,scale,pa,pm,pb,px){ shape <- mean/scale
pz <- 1 - pa*(exp(-0.5*(((px-pm)/pb)^2)))
loglh <- log(ifelse(mean == 0, ifelse(x == 0,0.999999,0.000001), ifelse(x==0,pz + (1-pz) * dgamma(0,shape=shape,scale=scale,log=F), (1-pz)*dgamma(x,shape=shape,scale=scale,log=F))))
return(loglh) }
