Models for supporting forest management in a changing environment

Abstract

Forests are experiencing an environment that changes much faster than during the past several hundred years. In addition, the abiotic factors determining forest dynamics vary depending on its location. Forest modeling thus faces the new challenge of supporting forest management in the context of environmental change. This review focuses on three types of models that are used in forest management: empirical (EM), process-based (PBM) and hybrid models. Recent approaches may lead to the applicability of empirical models under changing environmental conditions, such as (i) the dynamic state-space approach, or (ii) the development of productivity-environment relationships. Twenty-five process-based models in use in Europe were analyzed in terms of their structure, inputs and outputs having in mind a forest management perspective. Two paths for hybrid modeling were distinguished: (i) coupling of EMs and PBMs by developing signal-transfer environment-productivity functions; (ii) hybrid models with causal structure including both empirical and mechanistic components. Several gaps of knowledge were identified for the three types of models reviewed.The strengths and weaknesses of the three model types differ and all are likely to remain in use. There is a trade-off between how little data the models need for calibration and simulation purposes, and the variety of input-output relationships that they can quantify. PBMs are the most versatile, with a wide range of environmental conditions and output variables they can account for. However, PBMs require more data making them less applicable whenever data for calibration are scarce. EMs, on the other hand, are easier to run as they require much less prior information, but the aggregated representation of environmental effects makes them less reliable in the context of environmental changes. The different disadvantages of PBMs and EMs suggest that hybrid models may be a good compromise, but a more extensive testing of these models in practice is required.

Review. A brief overview of forest management decision support systems (FMDSS) listed in the FORSYS wiki

“…In those cases, GIS may have a double role: an integrator and a user interface. Among the sub-components, simulator (32), optimizer (16), GIS (20) and database management system (15) are common, confirming the earlier reviews on simulators Fontes et al, 2010;Muys et al, 2010) and conclusions by Marques et al (2013a) stating that GIS and DBMS as prerequisites for FMDSS. Some applications exist where a matrix generator (6) is used instead of a simulator.…”

Growth decline assessment in Pinus sylvestris L. and Pinus nigra Arnold. forest by using 3-PG model.

“…Extreme drought events strongly affect the physiological functioning of forests, and an increase of their intensity and severity will negatively affect tree growth and vigour in sites where these are strongly limited by water availability (Breshears et al 2 index models (Ung et al, 2001). However, efforts at physiological modelling of forest decline in relation to the effects of silviculture on Mediterranean pine and the impact of global change on forest in general are scarce (Fontes et al, 2010;Medlyn et al, 2011). Process-based stand growth simulation models, based on available empirical data or known physiological processes, or both, have been developed to improve our understanding of forest behaviour on the basis of descriptions of plant-soil and carbon-nutrient-water interactions.…”

Modeling Italian forests: state of the art and future challenges

“…Issues of accuracy and scale have been associated to the use of empirical growth and yield models in Europe (Corona & Scotti 1998). Those who intend to adopt them face two major challenges: (1) the availability of repeated forest inventories for the focus landscape that provide the input and output variables needed for calibrating empirical growth equations; (2) the inclusion of the impact of climate and site changes on future productivity (Fontes et al 2010). To accomplish the latter, model developers must link their input to external, process-based or bioclimatic envelope models, as discussed in his invited talk at the FMWG meeting by Nicholas Crookston from the US Forest Service (Crookston et al 2010).…”