Modelling the geographical distribution of two bark ... - Jean-Pierre Rossi
May 11, 2012 - These factors are advantageously used to model species distribution ... Generalized Linear Model with logit link function (logistic regression).
Modelling the geographical distribution of two bark beetles Tomicus destruens and Tomicus piniperda in Europe and the Mediterranean region Jean-Pierre Rossi?, Agnès Horn, Francois Lieutier & Carole Kerdelhué? ?
CBGP - INRA & Univ. Orléans, France
7-03-14 IUFRO Group Symposium
“Entomological Research in Mediterranean Forest Ecosystems” May 11, 2012
Introduction
Climate and resource availability are major determinants of geographical distribution of species Climate strongly impacts survival, development and growth rate of insects These factors are advantageously used to model species distribution () Species Distribution Modelling, SDM) Invited conference by G. Gader (expert-GIZ, Tunisia) about vulnerability of forest ecosystems to climate change
Introduction
2
What is Species Distribution Modelling ? Quantitative predictive models of species-environment relationships I
Potential species distribution
Introduction
3
Tomicus destruens & T. piniperda Subject species Bark beetles of the genus Tomicus are considered among the more damaging insects of Eurasian pine forests Tomicus destruens has long been mistaken for T. piniperda due to high morphological similarity Molecular studies on mitochondrial and nuclear markers have shown they are distinct species (Kerdelhué et al., 2002 among others) Morphological analysis provided diagnostic morphological characters (Faccoli, 2006)
Species
4
Tomicus destruens & T. piniperda Questions Can we identify climate descriptors explaining species pattern ? Can we draw detailed maps of both species present distribution ?
Available information A set of valid species occurrences (molecular analyses) in Europe and the Mediterranean region (sampling + data from literature) Variables describing climate within the study area
Species
5
Tomicus destruens & T. piniperda Questions Can we identify climate descriptors explaining species pattern ? Can we draw detailed maps of both species present distribution ?
Available information A set of valid species occurrences (molecular analyses) in Europe and the Mediterranean region (sampling + data from literature) Variables describing climate within the study area
Species
5
Sampling design : Tomicus destruens
⌅ Presence (species sampled) ⇤ Absence (the species has been searched for and was absent)
Material and methods
6
Sampling design : Tomicus piniperda
⌅ Presence (species sampled) ⇤ Absence (the species has been searched for and was absent)
Material and methods
7
Climate data source : the worldclim data base Current conditions (interpolations of observed data, representative of 1950-2000) Spatial resolution of about 1 square kilometer Monthly mean, min & max temperatures and precipitations Example : Annual Mean Temperature (˚C)
Material and methods
8
Data modelling Model linking species occurrences to climate data Generalized Linear Model with logit link function (logistic regression) Links presence/absence data and quantitative data (climate) Model ! probabilities of presence of each species across geographical range Predicted presence-absences are derived from probabilities using a threshold
Predicting future geographical distributions The fitted model can be used with data depicting future climate We used the IPCC 4 climate scenario from CIAT (climate model CCCM + emission scenario a2a)
Data modelling
9
Results Significant predictors T. destruens Min Temperature of Coldest Month (BIO6) Mean Temperature of Warmest Quarter (BIO10) Mean Temperature of Coldest Quarter (BIO11) T. piniperda Min Temperature of Coldest Month (BIO6) Mean Temperature of Warmest Quarter (BIO10) Mean Temperature of Coldest Quarter (BIO11) Annual Mean Temperature (BIO1) Mean Temperature of Wettest Quarter (BIO8) Horn et al., 2012 Results : present distributions
10
Example of climate-species relationships
Results : present distributions
11
Predicted geographic range of T. destruens
(Horn et al., 2012)
⌅ Presence (species sampled) ⇤ Absence (the species has been searched for and was absent)
Results : present distributions
12
Predicted geographic range of T. piniperda
(Horn et al., 2012)
⌅ Presence (species sampled) ⇤ Absence (the species has been searched for and was absent)
Results : present distributions
13
Predicted zone of sympatry
(Horn et al., 2012)
⌅ T. piniperda sampled ⇤ T. destruens sampled
Results : present distributions
14
T. destruens - today
Results : future distributions
15
T. destruens - 2020
Results : future distributions
16
T. destruens - 2050
Results : future distributions
17
T. destruens - 2080
Results : future distributions
18
T. piniperda - today
Results : future distributions
19
T. piniperda - 2020
Results : future distributions
20
T. piniperda - 2050
Results : future distributions
21
T. piniperda - 2080
Results : future distributions
22
Sympatry - today
Results : future distributions
23
Sympatry - 2020
Results : future distributions
24
Sympatry - 2050
Results : future distributions
25
Sympatry - 2080
Results : future distributions
26
Conclusions 1/3 T. destruens and T. piniperda have contrasted ecological requirements Both species are linked to similar environmental factors (mostly temperature) but
T. destruens is associated to higher temperatures Reproduction and larval development occur in automn and winter
T. piniperda is associated to lower temperatures Overwintering under bark at base of tree during cold season Requires low temperatures in winter that trigger the obligate period of maturation
Conclusions
27
Conclusions 2/3 Distribution maps Maps are useful pictures of potential distributions Design additional samplings to build more accurate models Search for species where it is predicted while never been observed e.g . mountaineous regions of North Africa seem suitable for T. piniperda
Conclusions
28
Conclusions 3/3
Implications for pest management Preventive measures/monitoring can be adapted according to predicted maps ! earlier sampling in region with T. destruens because host attacks are expected months earlier Distribution forecasts give insight of possible future expansion/regression of both species ! helpful targeting of early monitoring and management practices
Conclusions
29
Conclusions 3/3 Implications for pest management Preventive measures/monitoring can be adapted according to predicted maps ! earlier in region with T. destruens because host attacks should be expected months earlier Distribution forecasts give insight of possible future expansion/regression of both species ! helpful targeting of early monitoring and management practices
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