Presentation: Predicting the combined impacts of climate change and selective logging in production forests of Central Africa

Climate changeand selective

logging in CentralAfrica

Florian CLAEYS

ContextCentral Africa and climatechange

Forestry and adaptation

MethodologyModel

Datasets

ResultsSpecies clustering

Stationary states

Simulations for 2100

Conclusion

Predicting the combined impacts of climate change andselective logging in production forests of Central Africa

Florian CLAEYS1,2,3,4,? , Frédéric MORTIER2, Dakis-Yaoba OUÉDRAOGO5,Louis FRANÇOIS6, Bruno HÉRAULT7, Romain GASPARD7, Adeline

FAYOLLE5, Nicolas PICARD8,9,10, Mahlet G. TADESSE11, SylvieGOURLET-FLEURY2

Our Common Future under Climate Change (CFCC)UNESCO, Paris, July 8th, 2015

1 ENGREF, AgroParisTech, Paris, France ; 2 BSEF, CIRAD, Montpellier, France ; 3 LEF, AgroParisTech,Nancy, France ; 4 UMR 356 Forest Economics, INRA, Nancy, France ; 5 BIOSE, University of Liège, Gem-bloux, Belgium ; 6 UMCCB, University of Liège, Liège, Belgium ; 7 ECOFOG, Kourou, France ; 8 BSEF,CIRAD, Yaounde, Cameroon ; 9 COMIFAC, Yaounde, Cameroon ; 10 Silva Mediterranea, FAO, Rome, Italy ;11 DMS, Georgetown University, Washington DC, USA.? Corresponding author : [email protected].

Climate change and selective logging in Central Africa 0 / 11

mailto:[email protected]



Florian CLAEYS



MethodologyModel

Datasets


Stationary states


Conclusion

Central Africa, a climate change hotspotI Extreme seasonal temperature and precipitation (Diffenbaugh

and Giorgi 2012)

The occurrence of the 1986-2005 maximum JJA seasonal temperature in the 2016-2035, 2046-2065 and2080-2099 periods of RCP4.5 (left) and RCP8.5 (right). The panels show the absolute occurrences as thepercent of years in each 20-year period. The frequency of occurrence of the 1986-2005 maximum JJAseasonal temperature value is, by definition, 5 % at each grid point during the 20-year 1986-2005 period.




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MethodologyModel

Datasets


Stationary states


Conclusion

Forestry and adaptation in Central AfricaI Great emphasis on mitigation, with REDD+ (Bele et al. 2011,

Somorin et al. 2012)

I Climate change adaptation, a challenging stake partiallyconsidered :

I Hydrology and energyI Wetter wet seasons, drier dry seasons (Beyene et al. 2014,

Faramarzi et al. 2013) :I Agriculture

I Reduction of yields and productivities (Dinar et al. 2012, Knox

et al. 2012)

I Lack of knowledge for forestry issuesI Several research projects (Sonwa et al. 2014)

- CSC (GIZ)- COBAM (CIFOR)- COFCCA (CIFOR)- COFORCHANGE (CIRAD)- COFORTIPS (CIRAD)




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MethodologyModel

Datasets


Stationary states


Conclusion

A novel class of forest dynamics model

I Usher matrix model (Usher 1966; 1969)

I Issue of sample size (Picard et al. 2008)

I Species clustering into groups (Gourlet-Fleury et al. 2005)

I 3 processesI GrowthI DeathI Recruitment

I Variables selection for each group (Monni and Tadesse 2009)

I 12 variables (orders 1 and 2)I 2 diameter variablesI 2 stand variablesI 8 climate variables




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MethodologyModel

Datasets


Stationary states


Conclusion

M’Baïki dataset

M’Baïki dataset (Bedel et al. 1998)

I Central AfricanRepublic (CAR)

I 6×4 ha : BoukokoI 4×4 ha : La Lolé

I 30 yrs long monitoring1982-2012

I 239 speciesI 37 539 treesI 639 815 measures

I 3 treatments

I ControlI LoggingI Logging and thinning




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MethodologyModel

Datasets


Stationary states


Conclusion

Climate dataset

I Simulation data

I Atmospheric model CMIP5-CNRM (Voldoire et al. 2013)

I Dynamic vegetation model CARAIB (Warnant et al. 1994)

I 2 RCP (Representative Concentration Pathways)

I RCP45 and RCP85




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MethodologyModel

Datasets


Stationary states


Conclusion

Species clusteringI Ecological traits of groups

Clustering projection on two axes of adult stature (maximal diameter) and light requirement (maximalgrowth). For each group, disk size is proportional to the number of species of the group, whereas diskcolour indicates the species composition according to Hawthorne (1995) regeneration guilds :shade-bearer ( SB), non-pioneer light-demanding ( NPLD), pioneer ( P).




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MethodologyModel

Datasets


Stationary states


Conclusion

Stationary statesI Stand characteristics

I Dynamic characteristics

Diametric growth (cm.yr−1) Death rate Recruitment (trees.yr−1)




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MethodologyModel

Datasets


Stationary states


Conclusion

Stationary statesI Forest recovery after logging

Observed and simulated dynamics of tree basal area (m2.ha−1) recovery after logging.




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MethodologyModel

Datasets


Stationary states


Conclusion

Simulations for 2100I Impacts of climate change on unlogged and logged forests




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MethodologyModel

Datasets


Stationary states


Conclusion

Simulations for 2100I Impacts of climate change on harvest time profiles

1.5 ha−1

Current climate RCP 4.5 RCP 8.5

5 ha−1

Current climate RCP 4.5 RCP 8.5




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MethodologyModel

Datasets


Stationary states


Conclusion

ConclusionI Preliminary results

I Strong impact of climate change on forest ecologyI Acceleration of dynamics, especially recruitmentI Similar pattern in unlogged and logged forests

I Quasi-absence of impact on timber harvest timeprofiles

I Mining-like harvesting of primary forest premiumI Mismatch between timber production and other

services

I Main limitsI No ecophysiological considerationsI Separate fitting of dynamic processesI Static description of forest company

I Future challengesI Integration of uncertaintiesI Endogeneization of logger’s choicesI Continuation of long-term monitoring of forest stands




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Références

References IF. Bedel, L. Durrieu de Madron, B. Dupuy, V. Favrichon, V. Maître, A. Bar-Hen, and P. Narboni.

Dynamique de croissance dans des peuplements exploités et éclaircis de forêt dense africaine : ledispositif de M’Baïki en République Centrafricaine (1982-1995), volume 1 of Série FORAFRI.CIRAD Forêt, Montpellier, France, 1998.

M. Y. Bele, O. Somorin, D. J. Sonwa, J. N. Nkem, and B. Locatelli. Forests and climate change adaptationpolicies in cameroon. Mitigation and Adaptation Strategies for Global Change, 16(3) :369–385, 2011.

T. Beyene, F. Ludwig, and W. Franssen. The potential consequences of climate change in the hydrologyregime of the Congo River Basin. In A. Häensler, D. Jacob, P. Kabat, and F. Ludwig, editors, ClimateChange Scenarios for the Congo Basin, volume 11. Climate Service Centre Report, Hamburg,Germany, 2014.

N. S. Diffenbaugh and F. Giorgi. Climate change hotspots in the CMIP5 global climate model ensemble.Climatic change, 114 :813–822, 2012.

A. Dinar, R. Hassan, R. Mendelsohn, J. Benhin, et al. Climate change and agriculture in Africa : impactassessment and adaptation strategies. Routledge, 2012.

M. Faramarzi, K. C. Abbaspour, S. A. Vaghefi, M. R. Farzaneh, A. J. Zehnder, R. Srinivasan, and H. Yang.Modeling impacts of climate change on freshwater availability in africa. Journal of hydrology, 480 :85–101, 2013.

S. Gourlet-Fleury, L. Blanc, N. Picard, P. Sist, J. Dick, R. Nasi, M. Swaine, and E. Forni. Groupingspecies for predicting mixed tropical forest dynamics : looking for a strategy. Annals of forest science,62(8) :785–796, 2005.

W. Hawthorne. Ecological profiles of ghanaian forest trees. Tropical Forestry Papers, 29, 1995.

J. Knox, T. Hess, A. Daccache, and T. Wheeler. Climate change impacts on crop productivity in Africaand South Asia. Environmental Research Letters, 7(3) :034032, 2012.

S. Monni and M. G. Tadesse. A stochastic partitioning method to associate high-dimensional responsesand covariates. Bayesian Analysis, 4(3) :413–436, 2009.

N. Picard, F. Mortier, and P. Chagneau. Influence of estimators of the vital rates in the stock recovery ratewhen using matrix models for tropical rainforests. Ecological Modelling, 214(2) :349–360, 2008.




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References II

O. A. Somorin, H. C. P. Brown, I. J. Visseren-Hamakers, D. J. Sonwa, B. Arts, and J. Nkem. The congobasin forests in a changing climate : policy discourses on adaptation and mitigation (REDD+). GlobalEnvironmental Change, 22(1) :288–298, 2012.

D. J. Sonwa, P. Scholte, W. Pokam, P. Schauerte, M. Tsalefac, C. Bouka Biona, C. Peach Brown,A. Haensler, F. Ludwig, F. K. Mkankam, A. Mosnier, W. Moufouma-Okia, F. Ngana, and A.-M. Tiani.Climate change and adaptation in Central Africa : past, scenarios and options for the future. In C. DeWasseige, D. Louppe, F. John, K. Heiner, B. Bedoret, D. de Beauffort, and C. Halleux, editors, Lesforêts du Bassin du Congo - État des Forêts 2013, pages 99–119. Weyrich Édition, Weyrich,Belgique, 2014.

M. Usher. A matrix approach to the management of renewable resources, with special reference toselection forests. Journal of Applied Ecology, pages 355–367, 1966.

M. Usher. A matrix model for forest management. Biometrics, pages 309–315, 1969.

A. Voldoire, E. Sanchez-Gomez, D. Salas y Mélia, B. Decharme, C. Cassou, S. Sénési, S. Valcke,I. Beau, A. Alias, M. Chevallier, M. Déqué, J. Deshayes, H. Douville, E. Fernandez, G. Madec,E. Maisonnave, M.-P. Moine, S. Planton, D. Saint-Martin, S. Szopa, S. Tyteca, R. Alkama,S. Belamari, A. Braun, L. Coquart, and F. Chauvin. The CNRM-CM5.1 global climate model :description and basic evaluation. Climate Dynamics, 40(9-10) :2091–2121, 2013. ISSN 0930-7575.

P. Warnant, L. François, D. Strivay, and J.-C. Gérard. CARAIB : a global model of terrestrial biologicalproductivity. Global Biogeochemical Cycles, 8(3) :255–270, 1994.




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Acronyms I

BIOSE Biosystems Engineering (Ingénierie des biosystèmes)

BSEF Tropical Forest Goods and Ecosystem Services (Biens et Services desÉcosystèmes Forestiers Tropicaux, CIRAD, Montpellier, France)

CAR Central African Republic

CARAIB CARbon Assimilation In the Biosphere

CFCC Our Common Future under Climate Change

CIFOR Centre for International Forestry Research

CMIP5 Coupled Model Intercomparison Project Phase 5

CIRAD Centre for International Cooperation in Agricultural Research for Development(Centre de coopération internationale en recherche agronomique pour ledéveloppement, Montpellier, France)

CNRM French National Centre for Meteorological Research (Centre national derecherches météorologiques, Toulouse, France)

CNRS French National Centre for Scientific Research (Centre national de la recherchescientifique, Paris, France)

COBAM Climate Change and Forets in the Congo Basin

COFCCA Congo Basin Forests and Climate Change Adaptation

COFORCHANGE Predicting the Effects of Global Change on Forest Biodiversity in the Congo Basin

COFORTIPS "Congo basin forests : tipping points for biodiversity conservation and resilience offorested social and ecological systems"

COMIFAC Central African Forests Commission (Commission des forêts d’Afrique centrale)

CSC Climate Change Scenarios for the Congo Basin

ECOFOG Guyanese Forest Ecology (Écologie des forêts de Guyane,AgroParisTech-CNRS-CIRAD-INRA-UAG, Kourou, France)




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Références

Acronyms II

ENGREF National School of Agricultural Engineering, Waters and Forestry (ÉcoleNationale du Génie Rural, des Eaux et Forêts, AgroParisTech, Paris, France)

FAO Food and Agricultural Organization (Rome, Italy)

GIZ Deutsche Gesellschasft für Internationale Zusammenarbeit Société allemande decoopération internationale

INRA National Institute of Agricultural Research (Institut National de RechercheAgronomique, Paris, France)

JJA June-July-August

LEF Laboratory of Forest Economics (Laboratoire d’Économie Forestière, Nancy,France)

RCP Representative Concentration Pathway

REDD+ "Reducing emissions from deforestation and forest degradation and the role ofconservation, sustainable management of forests and enhancement of forestcarbon stocks in developing countries"

UAG University of the French West Indies and Guiana (Université des Antilles et de laGuyane, Pointe-à-Pitre, France)

UMCCB Unit for Modelling of Climate and Biogeochemical Cycles (Unité de Modélisationdu Climat et des Cycles Biogéochimiques, University of Liège)

UMR Joint Research Unit (Unité Mixte de Recherche)

UNESCO United Nations Educational, Scientific and Cultural Organization

USA United States of America




Florian CLAEYS

Références

Predicting the combined impacts of climate change andselective logging in production forests of Central Africa

Florian CLAEYS1,2,3,4,? , Frédéric MORTIER2, Dakis-Yaoba OUÉDRAOGO5,Louis FRANÇOIS6, Bruno HÉRAULT7, Romain GASPARD7, Adeline

FAYOLLE5, Nicolas PICARD8,9,10, Mahlet G. TADESSE11, SylvieGOURLET-FLEURY2

Our Common Future under Climate Change (CFCC)UNESCO, Paris, July 8th, 2015

1 ENGREF, AgroParisTech, Paris, France ; 2 BSEF, CIRAD, Montpellier, France ; 3 LEF, AgroParisTech,Nancy, France ; 4 UMR 356 Forest Economics, INRA, Nancy, France ; 5 BIOSE, University of Liège, Gem-bloux, Belgium ; 6 UMCCB, University of Liège, Liège, Belgium ; 7 ECOFOG, Kourou, France ; 8 BSEF,CIRAD, Yaounde, Cameroon ; 9 COMIFAC, Yaounde, Cameroon ; 10 Silva Mediterranea, FAO, Rome, Italy ;11 DMS, Georgetown University, Washington DC, USA.

? Corresponding author : [email protected].


mailto:[email protected]