The simulation model by researchers at the University of Tartu indicates that within the next 30 years the proportion of old forests in Estonia’s economic forests will significantly decrease, which will also jeopardize the sustainability of Estonian forestry. Strictly protected forests help relieve pressure on forest dwellers, says Ants Kaasik, a biostatistician at the University of Tartu.
Over the last decade or so, much has been said and written about intensive deforestation. Mathematically speaking, deforestation and regeneration rates are not overly complex problems on a national scale. To the extent that the results of the assessment of the current state of the forest are also not unambiguous, it is much more difficult to answer the practical question of what the state of the forest will be in the future.
Conservation biologists Raido Kont and Asko Lõhmus came to help me answer this question. We started from data from the Estonian forestry register, updated them and created a tool that allows us to simulate deforestation and the subsequent regeneration of forests throughout Estonia. As a result of the simulation it is possible to obtain the expected age and composition of each forest reserve in the future.
In the published study we made the source code of this tool available to everyone and tested several general future scenarios up to 2050. It turned out that the future prospects of different groups of owners are quite different.
What makes our model special is that it simulates the aging and possible felling of each forest reserve, of which there are around two million in Estonia. To the extent that the forestry law prescribes that excessive cutting cannot occur after deforestation, this means in practice that to carry out deforestation, in addition to the sufficient age of the forest growing on the plot, it is also necessary to verify the situation of the plots neighbors.
Since each forest patch typically has 5-10 neighbors, revisiting nearby areas under conditions of intensive logging in each simulation year means a lot of additional work. Therefore, when creating the model, an important challenge was to minimize the computer time required for a repetition of the simulation such that the user of the model could receive the results within a few days. Using parallel computing, we can run multiple simulation iterations at the same time. In this way, we obtain the future age distribution of each performance as an aggregate output.
Based on the results of this model, in addition to the overall prediction of different categories of forest use, such as strictly protected forests, different restricted areas and economic forests, it is also possible to find, for example, estimates of the coherence of forest massifs of a certain age. These assessments help, for example, to identify possible future problems for species whose habitat is only suitable for such old forests. In the same way you can study the landscape forecasts of the woods suitable for stopping, in search of berries or mushrooms.
In the first part of the work we used aerial laser scanning data. Aerial laser measurements helped us identify forest areas where forest cadastre data needed to be updated. Among other things, it emerged that at the beginning of 2022 in the area of Estonian economic forests, approximately 13% of pine trees, 21% of spruce trees and 17% of spruce trees had been felled in the last ten years.
In the case of spruce, the forestry industry justifies such intensive cutting by the age distribution of the forests. They also point out that postponing the felling of mature spruce trees would cause immediate economic damage. However, pine is a classic example of a tree where subsequent cutting does not cause such problems. However, taking into account the statutory felling age of these tree species, our results indicate that all three main Estonian tree species were felled in excess of their uniform use.
In the second part of the work, we created a simulation model in which different forest owners, such as the state and natural and legal entities, have different logging intensities that depend on the logging maturity of the allocation. This means that the amount of logging to be carried out is proportional to the number of plots ripe for cutting, but this proportion is different for different forest owners. We used the period 2012-2021 to calibrate the corresponding proportions, or cut the probabilities. annual registration data.
In the third part of the work, we applied the model to study different possible future scenarios. The results showed that over the next 30 years the share of old-growth forests in economic forests and protected forests will decline even more. At the same time, cutting maturity was taken into account in these simulations even more rigorously than in reality: cutting was predicted only based on age, although in reality many forests are cut earlier for various reasons.
In Estonian forests as a whole, the existence of strictly protected forests, where clear cutting is prohibited, has made it possible to reduce this decline. The latter demonstrates the great importance of this forest for species that use old-growth forests.
We have also seen that in intensively managed private forests the absolute volume of cuttings decreases significantly compared to today and that by 2050 there will be a situation where there will be a shortage of even middle-aged forests.
In the state forest the future situation is satisfactory, but even there a decrease in the cutting volume is inevitable. In private forests managed at low intensity, the future prospects are the best, because the cutting intensity so far has respected the principles of uniform use of the forest.
The created tool can be used in practice for any territory with allocation-based forest management. Therefore, in case of different starting parameters, they may also be applied outside Estonia.
Since the future is not yet set in stone, it is possible that the results obtained will also convince decision makers that the current direction of intensive forestry has not been the right one. There is now a tool to estimate what we might get when the chances of registration decrease.
The article was published in the journal PLOS ONE.
2023-12-04 07:54:00
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