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D2) Evidence-Based Management

Evidence-based biodiversity management of forests

Carsten Dormann
Doctoral researchers: Fabian Gutzat (2016 - 2020) and Carlos Miguel Landivar Albis (since 2019)

University of Freiburg, Faculty of Environment & Natural Resources, Institute of Earth & Environmental Sciences,
Chair of Biometry and Environmental System Analysis


Any kind of causal comprehension requires a model, be it formally defined as mathematical or rule-set algorithm, or informally as mental representation or unacknowledged gut feeling. Decision-making builds on a formal, or more often informal, representation of the causal functioning of the system in one's head: increasing, for example, forest structural heterogeneity by management will lead to increases in bird diversity because birds feed in different layers of the forest. To purposefully manage forests for diversity requires that the mechanisms linking a management and the outcome in terms of, say, insect abundance and richness are present in such a model. Otherwise, the outcome of a management would only be linked by chance to biodiversity. The second phase of D2 will focus now on how the processes that link management activities and species abundances and richness for different groups of forest organisms are actually represented in models.


Research questions and hypotheses

D2 attempts to explore the scientific basis of management principles for conservation in forests (e.g. Lindenmayer et al. 2006). One challenge is the plethora of specific studies of different design and quality, sometimes yielding equivocal conclusions about causal links between management and biodiversity responses. Human experts integrate the information available to them into a belief that may or may not be a reasonable summary of the state of knowledge overall. Are the scientifically demonstrated causes of forest biodiversity present in practitioners intentions and actions? How are processes that affect biodiversity at the landscape scale represented when taking actions at the stand level? Which are the effect of retention forestry related forest features on biodiversity composition and interactions?


Approach, methods and linkages

D2 compared three representations of the understanding of how biodiversity in forests is affected by the environment and by management: (1) general causal knowledge with high levels of evidence present in the scientific literature; (2) specific causal assumptions represented in mathematical and computer models with biodiversity as a state variable (e.g. Mönkkönen et al. 2014); and (3) intuitive causal belief of biodiversity-generating processes in forest scientists and forest managers (e.g. Raivio et al. 2001). In all approaches, different groups of organisms are regarded as target organisms, both in their abundance and species richness.

Literature review of which mechanisms (are hypothesised to) link forestry and diversity. Investigation of forest growth models for the explicit presence of these mechanisms in the code (light regime, disturbance, connectivity, stand heterogeneity, etc). Elicit the mental biodiversity model of practitioners, e.g. from observation of practice, through interviews, online image evaluation or by trick-questioning, to explore whether these diversity-affecting mechanisms are present (see attempt by Hauhs and Lange (2008) to do so: present a management and ask what is wrong with that; why?).

D2 will link to B-modules for approaches (1) and (2), and to all projects for the causality workshop. In particular, D2 will integrate with the approaches and experience of D1 for eliciting causal knowledge in practitioners.



In the first three years of D2-research, the focus was on defining and applying an evidence scale for scientific statements about the effect of deadwood on species richness in managed forests. Fabian Gutzat also evaluated potential reservations of forest managers against evidence-based guidelines for forest management, as modelled on evidence-based medical treatment guidelines. His meta-analyses demonstrates the feasibility of approach (1), i.e. summarising scattered knowledge in the scientific literature into quantitative and into systematic reviews.

Since 2019, Carlos Miguel Landivar will identify scientifically demonstrated drivers of species richness (for different groups of organisms,) and match these to processes represented in predictive diversity and forest growth models (both correlative and mechanistic; links to C1). In cooperation with C2, we will continue to link scientific knowledge to foresters intuitive or trained knowledge and its implementation. This can be evaluated using the visual information contributed by A1 and the actual biodiversity data of the B-projects. We hope to identify potential gaps in forest practitioners ecological knowledge and suggest strategies to build a more complete biodiversity management intuition.



The first part of our work specifically focussed on the effect of dead wood on birds and beetles in managed forests. Our systematic review database contains all relevant studies for temperate +forests (and many more). This needs to be complemented by ecological models of any description, in which bird and/or beetle abundance and/or species richness is included. We shall expand this to a wider range of organism groups since often only functional groups are included in models. In particular, all groups studied by the B-projects will additionally be included (i.e. bats, herbivorous arthropods, vascular plants,and mosses).

A key element of this thesis will be a causal diagram representing pathways between (elements of) forest management, forest structure, and biodiversity. Ideally, all three approaches (1-3 above) would yield a similar diagram structure. Discrepancies between what the (incomplete) scientific literature supports and which models scientists or practitioners believe are the dissonances we are actually looking for: they are an important element of uncertainty for predicting management effects.



Hauhs, M., Lange, H. (2008) Die Waldbilder der Forstwissenschaften aus der Sicht der ökologischen Modellbildung. Allg. Forst Jagd-Z. 179, 154-160.

Lindenmayer, D. B., Noss, R. F. (2006). Salvage logging, ecosystem processes, and biodiversity conservation. Conservation Biology, 20(4), 949-958.

Mönkkönen, M., Juutinen, A., Mazziotta, A., Miettinen, K., Podkopaev, D., Reunanen, P., ... Tikkanen, O. P. (2014). Spatially dynamic forest management to sustain biodiversity and economic returns. Journal of Environmental Management, 134, 80-89.

Raivio, S., Normark, E., Pettersson, B., Salpakivi-Salomaa, P. (2001). Science and the management of boreal forest biodiversity-forest industries' views. Scandinavian journal of forest research, 16(S3), 99-104.