Projet de doctorat sur la réponse des forêts boréales aux changements environnementaux: approche paléoécologique des états de référence dans la pessière à mousses de l’Ouest du Québec

English follows

Contexte :

La forêt boréale constitue une ressource naturelle importante pour l’économie Québécoise. Au cours des dernières décennies, un écart s’est créé entre les paysages naturels et les paysages aménagés par l’homme. Par ailleurs les changements climatiques en cours modifient le régime des incendies et par voie de conséquence la composition et la structure de la végétation. Dans un contexte d’aménagement écosystémique de la forêt boréale il est important de connaître les états de référence pour les végétations et perturbations naturelles dans le passé afin d’estimer dans quelle mesure les végétations seront résilientes aux changements futurs. Nous recherchons un(e) étudiant(e) de doctorat qui travaillera sur la caractérisation de l’évolution de la composition forestière, en lien avec le feu et le climat, durant l’Holocène (11 000 ans) pour la pessière à mousses de l’Ouest du Québec. Une partie du projet de doctorat consistera à comparer la variabilité écosystémique future (définie par modélisation) avec celle du passé.

Le ou la candidat(e) aura à utiliser les outils de la paléoécologie (analyse de bio-indicateurs sédimentaires), de la cartographie historique, et réalisera des analyses spatiales afin de comparer les végétations et régimes d’incendies avec les enregistrements sédimentaires. Nous recherchons un candidat qui sera en mesure de réaliser des analyses statistiques avancées dans le but de calibrer les enregistrements paléoécologiques avec des données a
ctuelles. La calibration des enregistrements sédimentaires constitue une des originalités du projet, c’est une étape capitale qui permettra de mieux quantifier les changements et la variabilité écosystémique passée en des unités qui soient compatibles avec les divers scénarios pour le futur.

Le ou la candidat(e) choisi(e) sera sous la direction d’Olivier Blarquez (Département de Géographie, Université de Montréal) et de Pierre Grondin (Ministère des forêts, de la faune et des parcs (MFFP)). Le projet de doctorat s’inscrit dans un projet plus large visant à quantifier selon différentes approches la variabilité et les états de référence pour la pessière à mousse au Québec. A ce titre le ou la candidat(e) bénéficiera d’un réseau de collaboration enrichissant et devra réaliser une partie de ses travaux en collaboration avec des entreprises privées leaders de l’industrie forestière au Québec ainsi que le Ministère des forêts, de la faune et des parcs (MFFP) chargé de l’élaboration des états de référence.

Profil recherché :

Maitrise en géographie, biologie ou domaine connexe. La priorité sera donnée aux candidats ayant une expérience en analyse statistique et/ou SIG et/ou modélisation et/ou paléoécologie. Excellence académique, bonne capacité de rédaction et intérêt à publier les résultats de recherche.

Nous recherchons en priorité un candidat au doctorat, cependant les candidats ayant obtenu une thèse dans le domaine de la paléoécologie ou un domaine connexe, démontrant un très fort intérêt pour les approaches quantitatives et présentant un excellent dossier universitaire sont invités à postuler.

Postuler : Afin de postuler envoyer une lettre de motivation, un cv, un relevé de notes récent ainsi que le nom de trois référents à olivier.blarquez@umontreal.ca et Pierre.Grondin@mffp.gouv.qc.ca. Les candidatures seront examinées immédiatement et ce jusqu’à ce qu’un candidat soit choisi.

 

PhD project on the response of boreal forests to environmental change: a paleoecological approach to reference conditions in the spruce-moss domain in western Quebec. 

Context:

The boreal forest constitutes an important natural resource for the Quebec economy. In recent decades, a gap has emerged between natural and managed landscapes. Moreover, ongoing climate change is expected to alter fire regimes and consequently ecosystem composition and structure. In the context of sustainable management of the boreal forest it is essential that references conditions for vegetation and natural disturbances in the past be established, in order to estimate the resilience of vegetation in response to future changes. We are seeking a doctoral student to work on the characterization of changes in forest composition in response to fire and climate during the Holocene (11,000 years BP) for the spruce-moss domain in western Quebec. Part of the PhD project will focus on comparing future ecosystem variability (defined by modeling) with that of the past.

The candidate will perform paleoecological analysis and interpretation (analysis of sedimentary bio-proxies), historical mapping, and spatial analysis to compare vegetation and fire regimes with recent sedimentary records. We seek a PhD student to apply advanced statistical analysis to calibrate sedimentary records using survey data. The calibration of sedimentary records is an original characteristic of this project and represents a critical step that will make it possible to quantify past ecosystem variability more precisely in units compatible with the various scenarios for the future.

The successful candidate will be supervised by Dr. Olivier Blarquez (Department of Geography, University of Montreal) and Dr. Pierre Grondin (Ministry of Forestry, Wildlife and Parks – Ministère des forêts, de la faune et des parcs (MFFP)). The PhD project is part of a larger project that aims at quantifying the ecosystem variability of the spruce-moss domain in Quebec according to different approaches. For this reason, the student will be part of a research network and will conduct some of his/her work in collaboration with private companies that are leaders in the Quebec forestry industry, and the Ministry of Forestry, Wildlife and Parks (MFFP).

Training and skills:

MSc in geography, biology or a related field. Applicants with experience in statistical analysis and / or GIS and / or modeling and / or paleoecology will be given priority. Academic excellence, good writing skills and interest in publishing research results.

While priority will be given to a PhD candidate, applicants with a PhD in paleoecology or a related field who have demonstrated a strong interest in quantitative approaches and have an excellent academic record will also be considered.

Application:

To apply, send a cover letter, CV, recent transcript and the names of 3 referees to olivier.blarquez@umontreal.ca and Pierre.Grondin@mffp.gouv.qc.ca. Applications will be considered immediately and until the position is filled.

paleofire 1.1.5

A new version of the R package paleofire is available on CRAN at http://cran.r-project.org/web/packages/paleofire/index.html

This release includes a new vignette on the basic usage of paleofire for reconstructing regional charcoal composite curves and a new function to easily export GCD sites to Google Earth .kml file.

Screen Shot 2014-12-01 at 3.28.06 PM

The australasian sites above have been exported using:

library(paleofire)
x=pfSiteSel(id_region=="AUST")
require(sp)
pfToKml(x, file='/Users/Olivier/Desktop/sites.kml')

 

 

New paper: paleofire: an R package to analyse sedimentary charcoal records from the Global Charcoal Database to reconstruct past biomass burning

I am  pleased to announce the publication of a new study in Computers and Geosciences with Boris Vannière, Jennifer Marlon, Anne-Laure Daniau, Mitch Power, Simon Brewer and Patrick Bartlein. The paper is freely available online here until October 19 and soon as a package vignette for paleofire.

 

Paper highlights:
• We present the open source paleofire R package for analysis of sedimentary charcoal series.
• The package is used to analyse charcoal records from the Global Charcoal Database.
• The functions eases the steps for interrogating data contained within the GCD.
• Analyses included charcoal series transformation (homogenization) and synthesis.
• We describe paleofire by producing a regional synthesis of biomass burning in NE America.

paleofire package highlighted in the PAGES Magazine

The paleofire package has been highlighted in the April issue of the PAGES Magazine :

PAGES_April2014Vannière B., O. Blarquez, J. Marlon, A.-L. Daniau and M. Power. 2014. Multi-Scale Analyses of fire-climate- Vegetation Interactions on Millennial Scales. PAGES Magazine, Volume 22, Page 40.

The article presents the workshop of the Global Paleofire Working Group held in Frasne (France) on 2-6 October 2013 that was supported by PAGES. The pdf of the article can be downloaded here or directly from the PAGES website.

New paper: Disentangling the trajectories of alpha, beta and gamma plant diversity of North American boreal ecoregions since 15,500 years

I am  pleased to announce the publication of a new study in Frontiers in Ecology and Evolution with Christopher Carcaillet, Thibaut Frejaville and Yves Bergeron. The paper is freely available online here.

Assessment of biodiversity in a changing world is a key issue and studies on the processes and factors influencing its history at relevant time scales are needed. In this study, we analysed temporal trends of plant diversity using fossil pollen records from the North American boreal forest-taiga biome (NABT). We selected 205 pollen records spanning the last 15,500 years. Diversity was decomposed into α and γ richness, and β diversity, using Shannon entropy indices. We investigated temporal and spatial patterns of β diversity by decomposing it into independent turnover (variation in taxonomic composition due to species replacements) and nestedness (variation due to species loss) components. The palynological diversity of the NABT biome experienced major rearrangements during the Lateglacial and early Holocene in response to major climatic shifts. The β nestedness likely reflected plant immigration processes and generally peaked before the β turnover value, which mirrors spatial and temporal community sorting related to environmental conditions and specific habitat constraints. Palynological diversity was generally maximal during the Lateglacial and the early Holocene and decreased progressively during the Holocene. These results are discussed according to macro-ecological processes, such as immigration, disturbances and environmental fluctuations, with climate most notably as the main ecological driver at millennial scales.

Introducing the paleofire package

The new R package, paleofire, has been released on CRAN. The package is dedicated to the analysis and synthesis of charcoal series contained in the Global Charcoal Database (GCD) to reconstruct past biomass burning.

paleofire is an initiative of the Global Paleofire Working Group core team, whose aim is to encourage the use of sedimentary charcoal series to develop regional to global synthesis of paleofire activity, and to ease access to the GCD data through a common research framework that will be useful to the paleofire research community. Currently, paleofire features are organized into three different parts related to (i) site selection and charcoal series extraction from GCD data; (ii) charcoal data transformation and homogenization; and (iii) charcoal series compositing and syntheses.

Installation:

paleofire is available from both CRAN website and GitHub

To install the official release (v1.0, 01/2014) from CRAN you just need to type this line at the R prompt:

 install.packages("paleofire")

To install the development version of paleofire from the GitHub repository the devtools package is required: on Windows platform the Rtools.exe program is required in order to install the devtools package. Rtools.exe can be downloaded for a specific R version on http://cran.r-project.org/bin/windows/Rtools/

Once devtools is installed type the following lines at R prompt to install paleofire:

library(devtools) 
install_github("paleofire","paleofire") 
library(paleofire)

To test everything is working you can plot a map of all charcoal records included in the Global Charcoal Database v03:

plot(pfSiteSel()) 

For details and examples about paleofire please refer to the included manual.

More examples of the capabilities of paleofire to come in the near future…

Calculate pixel values at increasing radius

ndvi_classes.m function could be used to calculate the number and percentage of pixels at increasing radius from a lake following certain criterion’s. The function was originally developed to count the number of pixels falling in specific NDVI classes around lakes but may be useful for different proxies and situations. The function use georeferenced tiff images as input. For details please refer to Aleman et al. (2013) or to the included help. The image used in the example can be downloaded here.

The function:

function [classe,classe_per]=ndvi_classes(ImageName,critere1,critere2)

% INPUT:  ImageName is the name of the geotiff image for imput
%         critere1 (and 2) criterion for classes evaluation
% 
% OUTPUT: classe: vector of NDVI classes at increasing radius from
%                 the lake center (number of pixel falling in class)
%         classe_per: Same as classe but in percentage of pixels
% EXAMPLE: Using the given Image 'classif_GBL_94.tif'
%{
        [classe classe_per]=ndvi_classes('classif_GBL_94.tif','>2','<4');
        %Plot the classe as a function of distance in km 
        %(1pixel=0.0301km, input 200 pixels when asked)
        plot((1:200)*0.0301,classe_per,'k-')
        ylabel('% pixels >2 and <4')
        xlabel('Distance from lake center (km)')
%}         
% Blarquez Olivier 2012
% blarquez@gmail.com

[Imge,dat] = geotiffread(ImageName);

imagesc(double(Imge))

select= input('Select point interactively? Y/N: ', 's');
if select=='Y'
title('Please double clic on lake center:')
p = impoint(gca,[]);
p = wait(p);
p=round(p);
elseif select=='N' 
valstring = input('Enter latitude and longitude: ', 's');
valparts = regexp(valstring, '[ ,]', 'split');
coords = str2double(valparts);
[~,p(2)]=min(abs(linspace(dat.Latlim(1),dat.Latlim(2),dat.RasterSize(1))-...
    coords(1)));
[~,p(1)]=min(abs(linspace(dat.Lonlim(1),dat.Lonlim(2),dat.RasterSize(2))-...
    coords(2)));
end

radius = input('Radius in pixels to be evaluated: ');
Imge(:,((p(1)+(radius)):end))=[];
Imge(:, 1:(p(1)-(radius)))=[];
Imge(((p(2)+(radius)):end),:)=[];
Imge(1:(p(2)-(radius)),:)=[];

n=length(Imge);

classe=zeros(radius,1);
classe_per=zeros(radius,1);
for r=1:radius
    warning('off','all')
imshow(Imge)
%imagesc(double(Imge))

hEllipse = imellipse(gca,[(n/2-(r)) n/2-(r) r*2 r*2]);
maskImage = hEllipse.createMask();
imshow(maskImage);

maskedImge = Imge .* cast(maskImage,class(Imge));
%imshow(maskedImge);
maskedImge(maskedImge==0)=NaN;
pixList=reshape(maskedImge,[],1);
pixList(isnan(pixList),:)=[];
 
classe(r,1)=length(pixList(eval(['pixList',critere1]) & ...
     eval(['pixList',critere2]),:));
classe_per(r,1)=classe(r,1)./length(pixList(:,1));
end
close all
    warning('on','all')
end

Paleofire reconstruction based on an ensemble-member strategy

Matlab codes and data associated with the manuscript: Blarquez O., M. P. Girardin, B. Leys, A. A. Ali, J. C. Aleman, Y. Bergeron and C. Carcaillet. 2013. Paleofire reconstruction based on an ensemble-member strategy applied to sedimentary charcoal. Geophysical Research Letters 40: 2667–2672, are available here.

Preprocessed data for Lac à la Pessière and Lago perso used in the paper also available:
PESSIERE.mat.zip
PERSO.mat.zip

Replicated rarefied richness

This example is intended to present the methods develloped for the paper Blarquez et al. (2013). We will use the macroremain record of the Lac du Loup a small subalpine lake with continuous macoremain counts for the last 11 750 years. More details on this site can be found in Blarquez et al. (2010).

First load the macroremain data:

dataMacro=read.csv("http://blarquez.com/public/code/dataMacro.csv")
# Macroremain counts
paramMacro=read.csv("http://blarquez.com/public/code/paramMacro.csv")
# Associated depths, ages and sample volumes
dataMacro[is.na(dataMacro)]=0
# Set missing values to zero

We now caluculates the median resolution of the record and the interpolated ages at which the influx will be calculated:

resMed=median(diff(paramMacro[,3]))
Ages=seq(-50, 11750, resMed)

We reconstruct the influx matrix using the pretreatment function which is the R implementation of the CharAnalysis CharPretreatment.m function originally develloped by P. Higuera and available at https://sites.google.com/site/charanalysis
Requires a matrix as input with the following columns:
CmTop CmBot AgeTop AgeBot Volume (params)
A serie from which to calculate accumulation rates (serie)

source("http://blarquez.com/public/code/pretreatment.R")
infMacro=matrix(ncol=length(dataMacro[1,]),nrow=length(Ages))
for (i in 1:length(dataMacro[1,])){
 + infMacro[,i]=c(pretreatment(params=paramMacro,serie=dataMacro[,i],
 + yrInterp=resMed,first=-50,last=11750,
 + plotit=F,Int=F)$accI)
 + }

Calculate samples macroremain sums and the minimun influx sum:

S=rowSums(infMacro)
nMin=min(na.omit(S[1:length(S)-1]))

We exclude the last sample (not calculated because of unknown accumulation rate) and samples with NA (because of vol==0):

del=which(S==0 | is.na(S))
infMacro=infMacro[-del,]
Ages=Ages[-del]
S=S[-del]

We calculate a proportion matrix which is used to replicate the rarefaction procedure:

S_=matrix(ncol=ncol(infMacro),nrow=nrow(infMacro))
for (i in 1:length(infMacro[1,])){
 + S_[,i]=c(S)
 + }
propMat=infMacro/S_

Replicate rarefaction with min influx n=1,2,…,500.
We will use the rarefy function from the package vegan:

library(vegan)
rare_=matrix(nrow=nrow(infMacro),ncol=500)
for (n in 1:500){
 + infN=ceiling(S_*n/nMin*propMat)
 + rare_[,n]=c(rarefy(infN,sample=n))
 + }

And finnally calculates the 90 percent confidence intervals and plot the rarefied richness using a 500 years locally weighted scatter plot smoother:

CI_=t(apply(rare_, 1, quantile, probs = c(0.05, 0.5, .95), na.rm = TRUE))
span=500/resMed/length(Ages)
plot(Ages,lowess(CI_[,2], f =span)$y,type="l",ylab="E[Tn]",ylim=c(min(CI_),
 + max(CI_)),xlab="Age",main="Rarefied Richness",xlim=c(12000,-100))
lines(Ages,lowess(CI_[,1], f =span)$y,type="l",lty=2)
lines(Ages,lowess(CI_[,3], f =span)$y,type="l",lty=2)

Screen Shot 2014-02-09 at 12.41.53 PM
References:
Blarquez O., Finsinger W. and C. Carcaillet. 2013. Assessing paleo-biodiversity using low proxy influx. PLoS ONE 8(4): e65852
Blarquez O., Carcaillet C., Mourier B., Bremond L. and O. Radakovitch. 2010. Trees in the subalpine belt since 11 700 cal. BP : origin, expansion and alteration of the modern forest. The Holocene 20 : 139-146. DOI : 10.1177/0959683609348857

Download pdf vignette

Superposed Epoch Analysis (SEA)

SEA.m function for Superposed Epoch Analysis (SEA) with confidence intervals estimated using block bootstrap procedure, the method is freely inspired from Adams et al. (2003), for an example of using SEA in palaeoecology see Blarquez and Carcaillet (2010). For details please refer to included help.

Example:

Create a random time serie with noise, define some events and plot them along with the time series:

t=1:500;
d=2.5*sin(2*pi*t/100)+1.5*sin(2*pi*t/41)+1*sin(2*pi*t/21);
T=(d+2*randn(size(d))).';
figure()
plot(t,T,'g')
ev=[50,93,131,175,214,257,297,337,381,428,470].';
hold on
plot(ev,repmat(4,length(ev),1),'v')

Screen Shot 2014-02-09 at 11.49.50 AM

Then we perform a SEA using a time window of 20 years before and 20 years after each event. ‘nbboot’ argument is used to define the number of circular block bootsrtap replicates with an automatically calculated block length (‘b’ equal 0) following Adams et al. (2003) procedure.


[SEA_means,SEA_prctiles]=SEA(T,ev,20,20,'prctilesIN',[5,95],'nbboot',9999,'b',0)

Screen Shot 2014-02-09 at 11.54.30 AM

Proxy response to the randomly generated events appears significant at the 95% confidence levels during events occurrence (at a lag close to zero).