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A comparison between data requirements and availability for calibrating predictive ecological models for lowland UK woodlands: learning new tricks from old trees

View Article: PubMed Central - PubMed

ABSTRACT

Woodlands provide valuable ecosystem services, and it is important to understand their dynamics. To predict the way in which these might change, we need process‐based predictive ecological models, but these are necessarily very data intensive. We tested the ability of existing datasets to provide the parameters necessary to instantiate a well‐used forest model (SORTIE) for a well‐studied woodland (Wytham Woods). Only five of SORTIE's 16 equations describing different aspects of the life history and behavior of individual trees could be parameterized without additional data collection. One age class – seedlings – was completely missed as they are shorter than the height at which Diameter at Breast Height (DBH) is measured. The mensuration of trees has changed little in the last 400 years (focussing almost exclusively on DBH) despite major changes in the nature of the source of value obtained from trees over this time. This results in there being insufficient data to parameterize process‐based models in order to meet the societal demand for ecological prediction. We do not advocate ceasing the measurement of DBH, but we do recommend that those concerned with tree mensuration consider whether additional measures of trees could be added to their data collection protocols. We also see advantages in integrating techniques such as ground‐based LIDAR or remote sensing techniques with long‐term datasets to both preserve continuity with what has been performed in the past and to expand the range of measurements made.

No MeSH data available.


(A) Size class distribution in terms of DBH in cm of eight tree species in Wytham Wood, with a negative exponential distribution overlaid for each species. The vertical line at 10 cm corresponds to the maximum size of saplings. (B) Species‐specific detail of size class distributions in terms of DBH of the eight tree species. For most species, there are fewer saplings than you would expect and no seedlings, the vertical red line marks the upper size limit for saplings. This makes it hard to assess whether there is lack of recruitment and impossible to calibrate predictive models.
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ece32217-fig-0001: (A) Size class distribution in terms of DBH in cm of eight tree species in Wytham Wood, with a negative exponential distribution overlaid for each species. The vertical line at 10 cm corresponds to the maximum size of saplings. (B) Species‐specific detail of size class distributions in terms of DBH of the eight tree species. For most species, there are fewer saplings than you would expect and no seedlings, the vertical red line marks the upper size limit for saplings. This makes it hard to assess whether there is lack of recruitment and impossible to calibrate predictive models.

Mentions: The use of any measurement of tree size that is taken at some distance from the ground will automatically exclude any individual that is shorter than the height at which this measurement is made, and this is why there are no data on seedlings in any dataset to which we have access. Obviously, the higher the measurement is taken, the greater the number of individuals that will be excluded. This is shown in Figure 1, which demonstrates that a substantial number of individuals are predicted to exist below the currently recorded minimum size classes. Seedling data from ECN taken within the same plots on which larger trees are measured reveal that for some species, seedlings make up a very large fraction of the individuals in the population – for example, for ash, 26% of all individuals are seedlings (Table 4). Overall, the lack of lower size classes is a serious data omission not only for predictive modeling calibration but also for forest assessment as it is impossible to detect whether there is a lack of recruitment for some species.


A comparison between data requirements and availability for calibrating predictive ecological models for lowland UK woodlands: learning new tricks from old trees
(A) Size class distribution in terms of DBH in cm of eight tree species in Wytham Wood, with a negative exponential distribution overlaid for each species. The vertical line at 10 cm corresponds to the maximum size of saplings. (B) Species‐specific detail of size class distributions in terms of DBH of the eight tree species. For most species, there are fewer saplings than you would expect and no seedlings, the vertical red line marks the upper size limit for saplings. This makes it hard to assess whether there is lack of recruitment and impossible to calibrate predictive models.
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4979709&req=5

ece32217-fig-0001: (A) Size class distribution in terms of DBH in cm of eight tree species in Wytham Wood, with a negative exponential distribution overlaid for each species. The vertical line at 10 cm corresponds to the maximum size of saplings. (B) Species‐specific detail of size class distributions in terms of DBH of the eight tree species. For most species, there are fewer saplings than you would expect and no seedlings, the vertical red line marks the upper size limit for saplings. This makes it hard to assess whether there is lack of recruitment and impossible to calibrate predictive models.
Mentions: The use of any measurement of tree size that is taken at some distance from the ground will automatically exclude any individual that is shorter than the height at which this measurement is made, and this is why there are no data on seedlings in any dataset to which we have access. Obviously, the higher the measurement is taken, the greater the number of individuals that will be excluded. This is shown in Figure 1, which demonstrates that a substantial number of individuals are predicted to exist below the currently recorded minimum size classes. Seedling data from ECN taken within the same plots on which larger trees are measured reveal that for some species, seedlings make up a very large fraction of the individuals in the population – for example, for ash, 26% of all individuals are seedlings (Table 4). Overall, the lack of lower size classes is a serious data omission not only for predictive modeling calibration but also for forest assessment as it is impossible to detect whether there is a lack of recruitment for some species.

View Article: PubMed Central - PubMed

ABSTRACT

Woodlands provide valuable ecosystem services, and it is important to understand their dynamics. To predict the way in which these might change, we need process‐based predictive ecological models, but these are necessarily very data intensive. We tested the ability of existing datasets to provide the parameters necessary to instantiate a well‐used forest model (SORTIE) for a well‐studied woodland (Wytham Woods). Only five of SORTIE's 16 equations describing different aspects of the life history and behavior of individual trees could be parameterized without additional data collection. One age class – seedlings – was completely missed as they are shorter than the height at which Diameter at Breast Height (DBH) is measured. The mensuration of trees has changed little in the last 400 years (focussing almost exclusively on DBH) despite major changes in the nature of the source of value obtained from trees over this time. This results in there being insufficient data to parameterize process‐based models in order to meet the societal demand for ecological prediction. We do not advocate ceasing the measurement of DBH, but we do recommend that those concerned with tree mensuration consider whether additional measures of trees could be added to their data collection protocols. We also see advantages in integrating techniques such as ground‐based LIDAR or remote sensing techniques with long‐term datasets to both preserve continuity with what has been performed in the past and to expand the range of measurements made.

No MeSH data available.