How vulnerable are forest ecosystems to climate change? A methodological approach for an index-based analysis for Brandenburg (northeastern Germany) Jantje Blatt, Björn Ellner, Stefan Kreft, Lena Strixner, Vera Luthardt, Pierre Ibisch Eberswalde University for Sustainable Development (University of Applied Sciences), Germany Introduction Climate change is increasingly exposing conservation targets (e.g., ecosystems, species, ecological processes) to a diversity of stresses, both directly and in interaction with other anthropogenic stresses. Therefore any proactive and strategic nature conservation management should be based on a thorough vulnerability assessment of its targets. Here, we propose an index of ecosystem vulnerability to climate change, taking the forests of Brandenburg State, northeastern Germany, as an example (see Fig. 1). The index is designed to facilitate the identification of adaptive conservation strategies and thus support managers who are supposed to deal with increasing non-knowledge concerning the highly uncertain future dynamics of climate change. It may be also used in prioritization exercises in the context of climate change-adaptive management. Berlin Legend Boarder of federal state Forest area http://www.moskau.diplo.de/Vertretung/moskau/de/01/Informationen/Deutschlandkart e.gif Lowland beech forest. Figure 1: Forests in Brandenburg state, north-eastern Germany. (Picture by V. Luthardt) Pine-Plantation after fire. (Picture by V. Luthardt) Drought impact on beech treelet. (Picture by P. Ibisch) Methodological Approach The indicators build upon valid (empirical) data from literature and expert valuation. They are Based on a literature review of vulnerability assessments, scored on a scale from 1 (very low impact/ adaptive capacity) to 5 (very high). To calculate a sound classification of stresses for ecosystems an overall vulnerability score (V), the index combines all information on caused by global climate change (Geyer et Criteria exposure and corresponding sensitivity (I) as well as adaptive capacity (A) al. in press) and the participation of external in the following algorithm: experts, we identified relevant criteria for the analysis of vulnerability, Change of average Sensitivity against n n n temperature and temperature and composed by exposure Ai precipitation precipitation changes ( EDi * S Di ) ( EIi * S Ii ) change and corresponding I i 1 i 1 i 1 A V I sensitivity (= impact) as well as AZ I A Changes of type, Z Sensitivity against Components of adaptive capacity of forest frequency, intensity of abiotic extreme events Vulnerability abiotic extreme events ecosystems (see Fig. 3). Within ED / SD = Indicators of direct exposure and corresponding sensitivity. IMPACT each criterion, semidirect EI / SI = Indicators of indirect exposure and corresponding sensitivity. quantitative (proxy) indicators Sensitivity against Changes of the Sensitivity changes of the hydrological regime Exposure define the corresponding The vulnerability scores are assigned to five relative vulnerability hydrological regime values. The indicators are categories form slightly vulnerable to extremely vulnerable. The indirect designed to factor in all systemrelative vulnerability categories serve to compare the assessed Changes of type, frequency, intensity relevant elements reflecting Sensitivity against systems regarding their different vulnerability-levels to climate of biotic (pest) biotic damages 1. the most important climate disturbance regimes change. We point out it is difficult to determine ‘absolute’ Adaptive Capacity change stressors for forest vulnerabilities due to the complexity of ecological processes and ecosystems (water scarcity, uncertainty of climate change factors that influence the systems biotic damage, fire risk, storm resilience and capacity to adapt. Structural diversity Ecological viability calamity) The scores are graded logarithmically in order to achieve a more appropriate and balanced weighting of impact and adaptive capacity 2. the relevant ecological Potential for natural Ecological strategy factors. processes that are affected by regeneration type of characteristic species Additionally, we integrated two ecological threshold values that such exposure change come into play, when one (two) high exposure factor(s) (sensitivity factors) correspond(s) with one (two) highly rated sensitivity factor(s). We 3. those ecological functions Figure 3:. Conceptual framework of vulnerability (based on IPCC Fourth assume, that in this case the ecosystem type will react vulnerable that support adaptation Assessment Report, cf. Parry et al. 2007) and criteria of forest ecosystem against climate change, independent from the other indicator ratings processes to altered climatic vulnerability against climate change (adopted from Polsky et al. 2003). and calculated vulnerability scores. In such a case the ecosystem conditions (adaptive capacity will be ranked in the second highest (highest) vulnerability class. factors) (see Fig. 4). I M P A C T Nr Indicators of exposure change Ed1 Projected change of average temperature, period 2031–2060 (reference: 1971-00) (according to Linke et al. 2010) Ed2 Projected change of hydrological regime  change of climatic water balance (vegetation period) 2031-2060 (reference 1961-90), Star 2 modell (PIK), combined with available water capacity (awc) (adopted from Konopatzky 1998) Nr Indicators of adaptive capacity Nr Indicators of sensitivity A1 Ecosystem architecture Sd1 Sensitivity against change of average temperature  Valuation by means of ‘bioclimatic envelopes’ (Kölling 2007, adopted) A2 Species diversity of tree species Sd2 Sensitivity against change of hydrological regime  Valuation by means of Climate-Species-Matrix (KLAM) (Roloff und Grundmann 2008, adopted) Sensitivity against change of max. windspeed  Valuation by means of Rottmann (1986, adopted) Projected change of max. windspeed, period 2031-2060 (reference 1961-90), Star 2 modell (PIK) Si1 Ei2 Projected change of fire risk  Change in Forest Fire Risk Index M68 for projection 2001-50 (reference 1951-98) (according to Badeck et al. 2004) Si2 Sensitivity against change of fire risk  Valuation by means of ‘Resistance against forest fires’ (Otto 1994) Ei3 Change of frequency and intensity of biotic damages  expert valuation Si3 Sensitivity against change of frequency and intensity of biotic damages  expert valuation Ei1 A3 Potential for natural regeneration (Otto 1994 and expert valuation) A4 Ecological strategy type  Valuation of characteristic plant species by means of Grimes triangle-model (Grime et al. 1988) A5 Ecological viability/ state of preservation (of Natura 2000 habitats) Forest ecosystem vulnerability to climate change Figure 4: Indicators of exposure change, sensitivity and adaptive capacity that compose the forest ecosystem vulnerability index. Outlook Literature cited The index can be applied on different scales, depending on the available data. A next step will include the distinction between the ‘generalised vulnerability’ of a generic forest type, and ‘realised vulnerability’ of a concrete forest of that type in its particular location, where specific abiotic and biotic conditions influence the system’s sensitivity and adaptive capacity. Due to its ‘lean structure’, the index can easily incorporate new knowledge or be developed further. It is open to being adapted to different sets of forest ecosystems in other regions. The general framework of the vulnerability index can equally serve for the development of indices for other ecosystem groups such as wetlands or grasslands. Contact Prof. Pierre Ibisch, Email: [email protected]; Jantje Blatt, Email: [email protected] Badeck, F.-W., Lasch, P., Hauf, Y., Rock, J., Suckow, F., Thonicke, K. (2004): Steigendes klimatisches Waldbrandrisiko. AFZ – Der Wald 2/2004: 90-93 Geyer, J., Kiefer, I., Kreft, S., Chavez, V., Jeltsch, F., Salafsky, N., Ibisch P.L. (in press): Classification of climate change-induced stresses of biological diversity. Conservation Biology. Grime, J. P., Hodgson, J. G., Hunt, R. (1988): Comparative plant ecology. Verlag Unwin Hyman; London, 742 S Kölling, C. (2007): Klimahüllen für 27 Waldbaumarten. AFZ – Der Wald 23/2007: 1242-1245 Konopatzky, A. (1998): Kennzeichnung der substratbedingten Feuchte von grundwasserfernen Sandstandorten mit Hilfe der Standortskartierung und ihre Anwendung. Unveröffentlicht. Linke, C., Grimmert, S., Hartmann, I., Reinhardt, K. (2010): Auswertung regionaler Klimamodelle für das Land Brandenburg – Darstellung klimatologischer Parameter mit Hilfe vier regionaler Klimamodelle (CLM, REMO10, WettReg, STAR2) für das 21. Jahrhundert. (Fachbeiträge des Landesumweltamtes des Landes Brandenburg; 113); Potsdam: 305 S. Otto, H.-J. (1994): Waldökologie. 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