1.3 P~rmanent Monitoring Plots: Potential and Limitations
Michael Kohl, Charles T. Scott, Andreas Zingg
Abstract
Forest health monitoring can be based on assessments on sample survey (ECE Level I) plots and on permanent monitoring (ECE Level II) plots. The difference in research objectives leads to a situation where sample plots are available that are representative of the total population, but give only limited information on site conditions and management history. On the other hand, detailed information on site condition and management history is available for monitoring plots, but these do not represent the total population. As an example, permanent monitoring plots from Swiss growth and yield studies are compared to Swiss national forest survey plots in terms of species distribution, stem form, slope class and elevation. The role of permanent monitoring plots in causal inference is discussed.
1.3.l Introduction
In the beginning of the 1980's concern about the health and condition of forests lead to the establishment of forest health monitoring programmes in most European countries. Due to uncertanties about the current state and development of forest condition, national forest health surveys were started, which focus mainly on the attributes crown transparency- formerly called neadle-leaf loss - and discoloration.
The engagement of UN-ECEand EC expert panels on forest health monitoring resulted in harmonized standards and assessment methods for European countries.
During the course of time forest health surveys have been criticized for various reasons. Some authors criticise the assessment of crown transparency, others criticise the lack of data for modelling cause-effect relationships. Therefore, attempts were undertaken to establish permanent monitoring (Level II) plots, where intensive observations and the assessment of various attributes including information on soil, climate and pollution are planned. The cost of establishing and maintaining these plots are high.
; Therefore only a limited number will be available and it is questionable if representative figures can be given for the entire forested area.
A represenative sample requires that all sampling units in the sampling population have a known and positive probability of selection. There should be no segments of the population which cannot be included in the sample. According to COCHRAN (1977) the population to be sampled should coincide with the population about which information is wanted (target population). If the sample and the target population are not the same, the sample is no~ representative and inappropriate conclusions will be drawn. For further detail refer to COCHRAN (1977).
Permanent monitoring plots that were established in the past focused mainly on growth and yield.
Permanent sample survey plots were introduced into forestry by Stott in 1938 (STorr 1947). Rather than based on experimental design, plots are located based on sample survey design. The sample represents the treated and untreated plots in proportion to their occurrence. In national forest surveys such as in the USA, Scandinavia, Germany, Austria or Switzerland, the total area in these countries is represented by permanent sample plots.
The objective of this paper is to outline the potential and limitations of permanent monitoring plots.
As an example monitoring plots from Swiss growth and yield studies will be compared to Swiss national forest inventory plots. This example is taken from a publication by KOHL et al. (1993). We will show that growth and yield plots are not fully representative for all forest conditions. On the other hand, permanent sample plots may not provide the range of treatments of interest, but are representative of all forest types and conditions. The findings are equally applicable for the design of monitoring programmes and the selection of permanent observation plots in forest decline studies. They will be used to illuminate the eventually poor representativity of models and findings based on data obtained on permanent moni- toring plots.
1.3.2 Forest Health Surveys
Since 1984, surveys of forest condition have been conducted in most European countries (KOHL 1990).
Inventories are typically carried out as sample surveys with systematically distributed sampling units.
Between nations, sampling intensity and sampling units vary. The United Nations Economic Commis- sion for Europe, under the auspices of the Convention on Long Range Transboundary Air Pollution, has suggested that a fixed number of trees should be assessed at each sample location and a grid size no wider than 16 km is required for forest damage inventories. However, many countries have adopted denser grids, ranging down to the 1 km grid used in some years in the Nt:therlands. KOHL et al. (1994) describe the variability of results due to different grid densities. Sampling units applied in national forest health surveys are fixed area plots, e.g. in Switzerland, or stem distance methods, e.g. in Germany. In sampling on successive occasions trees, which are cut or die, disappear from the sampling unit and new trees may grow in. The sampled trees are therefore not strictly identical over time. Using fixed area plots, attributes assessed on individual trees can be broken down to mortality, cut, ingrowth and survivor trees and related to the plot area, which keeps constant. The samples are therefore strictly dependent in the statistical sense. Using stem distance methods, a constant number of nearest trees is selected at each sampling location and the sampling unit is a fixed number of trees. Trees have to be replaced on the sampling unit in the course of time due to mortality, cut and ingrowth, and estimation procedures based on dependent samples cannot be applied without modifications. Together with other problems like biased estimates in clustered populations we suggest to replace stem distance methods by fixed area plots.
Although the sampling design has been criticized by INNES (1988, 1990); NEUMANN (1989) and others, it has been generally (but erroneously) accepted that the observation of a fixed number of trees at each point on a 16 km net provides a representative picture of the condition of forests in a country. This is clearly questionable in some European countries which are so small or have such a low percentage ofland covered with forests that such a grid produces only a few sample points.
In most national forest health surveys as well as in the annually published UN-ECE report, only parameter estimates of attributes are given, like proportion of crown-transparency classes, but no variance estimates. Therefore the reliability of the results cannot be evaluated, as no information on sampling errors or confidence intervals is provided. Given the diversity of tree species and stand characteristics that are found in individual countries, the sample sizes that are required to produce estimates of forest with meaningful sampling errors may be considerable. Further difficulties may arise when data describing forest condition are broken down into individual regions, resultin_g in particularly small sample sizes for the rarer species. KOHL and KAUFMANN (1993) present procedures for the estimation of sampling errors in forest health surveys and show the limited meaning of results for rare species.
There is a considerable amount of noise present in forest health data sets (KOHL 1991, 1993; KOHL and GERTNER 1992; INNES 1993) making annual comparisons problematical. One way to reduce sampling errors is by having a sufficient sample size for the monitoring programme. Observer bias can be reduced by intensive training and control, but not by an increase of sample size. GERTNER and KOHL (in preparation) combine sampling error and observation error to come up with a meaningful estimate of the total error attributable to crown transparency assessments. They show that beside sample size the number of crews has a significant influence on the overall error.
1.3.3 Growth and Yield Plots
Growth and yield (G & Y) plots are typically installed as part of an experiment. The objective is to study the effect of different planting methods, growth patterns of distinct provenances, thinning regimes and cutting practices. Plots are typically large and surrounded by a buffer strip to ensure uniform treatment and response across the plot. Several plots are installed in each treatment both within the site and across sites for replication. Unfortunately, replication was very often neglected in early growth and yield studies. Measurements and assessments are very intensive, particularly relating to site conditions and treatment history.
Growth and yield plots are established on treated and untreated stands in accessible areas. The focus is put on commercially valuable timber types; other timber types are rarely studied. No ecotones or edge conditions are permitted. Because it is expensive to establish and maintain plots, few plots are taken, resulting in a low sampling intensity. A critical problem was the accessibility of plots in early times when field crews had to use public transportation. Most of the Swiss G & Y plots established before the 1950's follow the main railroad lines. Hence, these plots are not representative spatially. Samples are often confined to a narrow geographic range, for example, low elevations and easily accessible sites in mountainous regions. Concerns regarding the representativeness of G & Y plots arise (KELLER 1978).
This becomes critical when small data sets are used to develop models which are then applied to large areas.
1.3.4 Comparison of Growth and Yield versus Permanent Sample Plots in Switzerland
The study of G & Y in Swiss forests was one of the most important research objectives right after the foundation of the Federal Institute of Forestry Research in 1885. The first Swiss G & Y plots were established in 1888 to study specific species or provenances. One of these plots is still monitored. The plot design and data access has changed over time, resulting in inconsistent data, particularly from old plots.
Much effort was necessary to convert the growth and yield data into a database system.
The comparisons shown below are based on data from G & Yplots between 1977 and 1988. The total area covered was 98.2 ha and was partitioned in 474 blocks with 184 ongoing research projects. The total number of trees registered on these plots was 90,735.
The first Swiss national forest inventory (NFI) was conducted in the mid-1980's (EAFV, BFL 1988).
A uniform 1 x 1 km grid of permanent sample plots was assessed across all of Switzerland. Of these, about 12,000 plots were forested and measured by field groups. The NFI plots are representative of all forest conditions in Switzerland. However, the plots are small and treatment history or site data are more difficult to determine.
Switzerland can be divided into five productive regions, which reflect similar growth and site conditions. The distribution of G & Y and permanent plots in the productive regions is shown in Table 1. The Jura, Prealps and Southern Slopes of the Alps are sufficiently represented. Most G & Y plots are located in the plateau region; the Alpine Region is under-represented.
Other indicator variables for site and growth conditions are the elevation (Table 2) and the slope (Table 3) of the plots. Approximately 80 percent of the area of G & Y plots are located in altitudes below 1000 m. The results of the NFI indicate that more than 50 percent of the Swiss stands are above· 1000 m.
A similar situation can be found for the slope of the plots. Roughly 20 percent of the NFI plots are located in areas with slopes larger than 60 percent, but only 5.3 percent of the G & Y plots were established in these steep slopes.
Table 1. Proportion of plots in productive regions. Table 2. Proportion of plots per elevation class.
Region Growth and Yield NFI Plots Elevation Growth and Yield NFI Plots
Plots [% area] [%] Plots[% area] [%]
Jura 13.3 16.4 > 1800 m 3.0 7.8
Plateau Region 35.7 19.2 1601 m-1800 m 2.9 8.6
1401 m -1600 m 2.2 10.2
Prealps 21.8 18.3 1201 m - 1400 m 7.2 11.7
Alps 11.9 32.2 1001 m - 1200 m 4.8 13.5
801 m- 1000 m 23.8 13.3
Southern Slopes 17.2 13.9 801 m- 800m 13.1 16.0
of the Alps < 600m 42.9 19.0
Table 3. Proportion of plots by slope classes.
Slope Growth and Yield NFI Plots Plots[% area] [%]
>100%
-
1.8-100% 0.1 4.6
-80% 5.2 12.8
-60% 12.8 23.2
-40% 24.6 27.2
<21% 40.8 30.5
Table 4. Proportion of tree species by number of stems and basal area.
Tree Species Number of Stems Basal Area
Growth and Yield Plots
[%]
Picea abies (L.) Karst 30.0
Abies alba Mill. 11.0
PinusL. 5.0
Larix L. 19.4
Pinus cembra L. 0.6
Other coniferous 9.9
Fagus sylvatica L. 4.6
AcerL. 6.6
Fraxinus excelsior L. 0.6
QuercusL. 4.0
Castanea saliva Mill.
-
Other deciduous 8.4
Table 5. Stem form (height/diameter at breast height ratio).
h/d Ratio Growth and Yield Plots NFI Plots
[%] [%]
<0.8 45.4 67.7
-1.0 30.9 22.3
>1.0 23.7 10.0
NFI Plots Growth and Yield Plots
[%] [%]
39.4 28.0
11.8 22.6
4.2 3.6
4.2 17.3
0.8 1.5
0.4 7.7
19.2 8.1
3.6 0.3
3.6 0.2
2.4 7.4
2.4
-
8.0 3.4
Table 6. Maximum forested area where growth and yield tables can probably be applied.
Forested Area by Predominant Species and Region [1,000 ha's]
-Trees Plateau Southern
Jura Region Prealps Alps Alps
Spruce 12.6 22.0 42.2 67.8 10.3
Fir 2.7 3.7 3.3 0.6 0.4
Larch 0.2 0.4
-
11.3 4.8Beech 10.6 6.6 3.3 4.1 7.5
Total 26.1 32.7 48.8 83.8 23.0
In% of total
Forested area 13.4 14.3 22.5 22.0 14.0
NFIPlots [%]
28.6 13.6 4.7 6.2 1.0 0.8 17.7 6.6 5.8 3.8 1.3 9.9
Switzerland 154.9
10.7 16.7 32.1 214.4 18.1
The comparison of the proportion of G & Y plots and permanent plots by productive region, elevation and slope indicate that the G & Y plots do not represent the growth and site conditions of Swiss forests.
The G & Y plots tend to be established in lower elevations and relatively flat terrain. A large proportion of Swiss forests is located in the unique conditions of higher elevations, but these conditions are poorly represented by G & Y plots.
Almost 29 percent of the G & Y plots were established to study selection forests, but the NFI results show that only about 9 percent of the Swiss forest have been managed using this regime.
The G & Y plots do not reflect the proportions of tree species in Switzerland. Table 4 shows that regarding number of stems and basal area, fir, spruce, oak, and a few other species are fairly well represented. Beech is one of the most frequent tree species in Switzerland, but is poorly represented in growth and yield studies.
Stem form can be expressed as the ratio of tree height (h) to diameter at breast height (d). Trees with a high hid ratio are slender and are more desirable for timber production. The h/d ratios were grouped into three classes and are presented in Table 5. On G & Y plots, trees have higher h/d ratios than those on NFI plots, which means that stems with a low h/d ratio are under-represented and stems with high h/d ratios are over-represented. This can create problems, such as in the development of taper and volume equations. Any model deficiencies will be accentuated by the differences in how well the G & Y plots represent the entire population.
Data on G & Y plots were used to develop Swiss growth and yield tables for spruce, fir, larch and beech (BADoux 1983a, 1983b, 1983c, 1983d). They are applicable for even-aged, pure stands with high thinning. The thinning methods cannot be determined on NFI plots. The NFI showed that even-aged pure stands occupy about 18 percent of the total forested area of Switzerland (Table 6). Only in these stands can the growth and yield tables be applied.
The comparisons shown above support the hypothesis that G & Y plots are not representative of Swiss forests. For the commercially important areas, especially in the Plateau Region and in the Prealps, sufficient information is available for some tree species like oak, spruce and fir. Spruce stands in mountainous regions are underrepresented and chestnut ( Castanea sativa Mill.) stands on the Southern Slopes of the Alps are not represented at all but are essential forest types in those areas.
Growth and yield models- in Europe almost exclusively presented as tables or charts -were derived primarily from G & Y plots. The plots were established not only to study the growth of single tree species or provenances, but to assess various tretments to the plots to get information on the effect of distinct management methods. Regardless of the initial research objectives, the data from these plots are often used to develop growth and yield models and management guidelines.
1.3.S Permanent Monitoring Plots and Causal Inference
It is widely accepted that data assessed in forest health surveys are not suitable to set up cause-effect relationships, as there is a limited number of attributes assessed on the plots and information on potential causes like pollution or climatic conditions is missing. To fill this gap and to provide sound information on both, potential causes and effects, is one of the most important research objectives in establishing permanent monitoring plots. However, heterogeneous forest conditions, the variety of and interactions between potential causes, and the limited number of observations (plots) due to the enormous cost of data assessment lead to a situation where causal inference has to be made despite of imperfect knowledge. Problems will arise concerning the significance and general applicability of cause-effect relationships, which are based on data from permanent monitoring plots. In this chapter the benefit of permanent monitoring plots will be discussed in the context of causal inference.
According to RoTIIMAN (1986) an event, condition, or characteristic that plays an essential role in producing an occurrence of an effect can be called a cause of the effect. Causality is a relative concept than can be understood only in relation to conceivable alternatives. Essential factors that have to be considered in causation are the strength of the causes, the interaction among causes, the proportion of effects due to specific causes, and the induction period.
Causal inference can be made in the wide range between "statistical inference" and "inductive inference". In statistical inference we may specify the size of the true difference which an experiment is to detect by means of a test of significance, or we may specify how closely we wish to estimate the true difference by stating the width of the confidence interval for the true difference. HILL (1965) set up a set of standards for inductive inference. In attempting to distinguish causal from noncausal associations nine aspects should be considered: strength, consistency, specificity, temporality, biologic gradient, plausi- bility, coherence, experimental evidence, and analogy. In ecological systems the study of cause-effect relationships is commonly based on three criteria: consistency, responsiveness, and known mechanism.
ScHLAEPFER (1991) gives the following definitions of consistency, responsiveness, and known mech- anism:
Consistency: There is a consistent association in time and space between the observed injury or change and the hypothetical causal agent.
Responsiveness: When exposed to the hypothetical causal agent under controlled conditions, the exposed object responds by exhibiting the expected injury or change in a reproducible and quantitative way.
Known mechanisms: There is a known mechanism or series of processes by which the expected injury or change can reasonably result from the hypothetical causal agent.
These three criteria focus on different methodological approaches. Consistency refers to the repeated observation of a relation between cause(s) and effect(s) under different circumstances and may be proved by forest surveys, which provide data over a large scale of forest conditions, or by experiments with varying treatments. Responsiveness is found under the controlled conditions of experiments.
Known mechanisms may be the result of experiments or basic research and can be based on biological plausibility, experimental evidence or analogy.
The scientific value of permanent monitoring plots depends on the approach used in data assessment.
They can be set up as experiments, case studies, or sample surveys. Experiments follow the rules of experimental designs and are characterized by randomization, replication and homogeneity. Treatments are allotted to the experimental material by randomization. Depending on the objectives and the experimental design the number of replications is determined. For each treatment enough replications are available and by replicating the treatments the experimental errors are decreased. Homogeneity relates to both homogeneous application of the treatments and homogeneous experimental material.
Homogeneity reduces the amount of unknown sources of variation and ensures that differences between treatments are mainly attributable to treatment effects. Treatments are applied under controlled conditions and experiments are aimed at statistical inference.
Case studies are characterized by only one or very few observations. They are commonly not designed, but are accidental observations of striking interrelations. The lack of homogeneity, rando- mization and replication is obvious. The findings of a case study are not subject to statistical inference and misleading results of causal inference are likely to occur. Therefore, case studies should not be utilizt:d for inference, but are a beneficial tool in generating hypotheses for further research activities.
Sample plots assessed in surveys cover the whole area of interest and give representative results.
However, the sampling intensity in regional inventories is typically too low and the plots are too small to adequately characterize individual stands. Treatment history is normally not known and cannot be developed from the data assessed on the plots. The number of attributes assessed is low and as sample plots are usually visited only once per year trends during the vegetation period cannot be observed. The number of trees observed on plots is not sufficient to characterize interrelations in forest ecosystems.
Conseque.ntly surveys give representative figures of distinct attributes, but are too coarse to provide data for ecosystem modeling and for the detection of cause-effect relationships.
Due to the overwhelming variability of forest ecosystems and environmental impact on the one hand and the time and cost consumptive assessment of information like data on trees, stands, soils, pollution levels, or climatic factors on the other hand, controlled conditions can only be provided for a limited number of observations. The objective of permanent monitoring plots is to provide data for the description of forest ecosystems and for causal inference. Therefore, they should neither be case studies nor sample surveys, but their layout has to follow the rules of experimental design.
Two major constraints for studies based on permanent monitoring plots arise: (1) limited budgets force a concentration on a selected set of forest ecosystems and environmental impacts, and (2) causal inference will not be representative for the entire forest population. The available resources have to be used for repeated treatments ( environmental impacts) under homogeneous conditions ( tree species, soil types, or management regimes) to allow causal inference. The findings are constrained by the experi- mental situation and must not be applied to other than the investigated setup of treatments and conditions. Causal inferences cannot be generalized. It is a bad mistake to fake representativity by spreading monitoring plots over a wide range of different conditions. Monitoring plots are no longer experiments but become case studies and due to the lack of repeated observations causal inference is infeasible.
1.3.6 Conclusions
Some limitations of both permanent sample plots and permanent monitoring plots have been demon- strated. Permanent sample plots cover the whole area of interest and give representative results.
However, the sampling intensity in regional inventories is typically too low and the plots are too small to adequately characterize individual stands or ecosystems. Environmental conditions are normally not known and causal inference cannot be developed from the data assessed on the plots.
On permanent monitoring plots, site conditions and environmental impact are carefully monitored.
However, they are not representative of the total forest population due to the low number of obser- vations.
Permanent sample plots can be used to identify the range of applicability of ca use-effect relationships.
They can also be used to identify conditions and areas to establish new permanent monitoring plots, thus expanding the applicability of causal inference and cause-effect models.
The layout of permanent monitoring plots has fo follow the rules of experimental designs if they lack of homogeneity, randomization and repetition, they become case studies and causal inference is infeasible. It is well-known that caution should be used in applying causal inference to conditions other than those represented by the plots upon which they are based. Unfortunately only in very rare cases are the underlying conditions and assumptions described in detail.
When permanent sample plots are appropriately modified, they can be used to augment permanent monitoring plots to develop models which are more generally applicable.
1.3. 7 References
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