| A unit of Lasting Forests
evolving since March 30, 1999 |
|
A Total Forest Management Plan
|
|
|
The Concept
A search for where or when the word "baseline" emerged, while interesting, is likely to shed little light on its potential meaning and role in modern natural resource management. Hirsch (1980:86) looked for a definition but, finding none, deduced that it meant "a description of conditions existing at a point in time against which subsequent changes can be detected through monitoring." If this definition is accepted, it delimits to a large extent the role of baseline studies in environmental impact evaluation. Under this definition, the baseline study is not a predictive tool; its principal use is for post hoc detection of change. As such, a baseline study would be of limited utility in meeting the requirements of an environmental impact statement. These requirements tend to be for predictive work. Nevertheless, the present needs to be defined.
Hirsch acknowledged that the rationale and expectations for baseline studies are not clear or widely accepted. He observed that in practice, "baseline" is quite loosely used to cover a range of information required, at least for purposes of environmental impact assessment. It is often found intermixed with "inventory" and "monitoring."
Hirsch (1980:85) described an ocean baseline sampling program that was designed to detect long-term oceanic changes. The need was seen for "...both one-time and continuing surveys." These implied surveys that were needed to help "...establish a baseline for analysis." The wording suggested that "baseline" is a concept of a standard over time unlike most uses of the word which seem to mean a measure of many factors at one time. The "prior use" concept is a principle of biological taxonomy. Herein we may transgress the principle, but the greater fear is that a full understanding and development of a modern, computer-enhanced and useful concept of baseline will be delayed. Baseline studies have been called "trend assessment surveys." We use "baseline", assuming the first users were not trying to be scientifically precise or rigorous in their definition, that there was no flaw in the definition, and that it has not already been replaced by another word or by someone else's definition. (Figure 1).
|
Fig. 1. There are several general concepts of "base" depicted as (A) a singular or summary observation about a system or a factor. In A may be depicted a particular distribution or pattern to which a point or set may be compared.An alternative, (B), is a set of observations about factors or systems with emphasis on direction or rate of change from a point. C suggests several factors, all to be studied over time. The line in D may be the concept often meant, namely studies done before some time t, where t is the line drawn in time with studies done before that being the base to which other later observations may be compared. |
 |
This section attempts to synthesize concepts of baseline studies. It tries to develop a solid platform of understanding and collective wisdom from which baseline studies might be begun. It explains the point of view and basis for planned work. Hopefully that point of view will be supported but, if not, this section presents a basis for discussion and constructive adaptations for the future. Within The Trevey as used on the land may some day emerge an analysis system that serves the interests of people for each land area and one that can demonstrate usefulness as a "...base for land-management and regional planning decisions involving such uses as forestry, recreation, wildlife, selection of natural areas... transportation..." (Lacate 1973:14), water, utility and other development. Understand baseline will allow us to measure change, compare before-and-after differences, compare conditions to a standard (hopefully one found acceptable), and judge "land health." Baseline studies may help protect the environment, the landowner (and Trevey staff).
Over many years the value of wilderness to science has been listed. This is an assertion about potentials. There are few such studies, studies which are significantly different than studies that can be conducted on non-wilderness or non-"natural area" land and water. Within The Trevey is support for the value of wilderness as "the base datum of normalcy" (Leopold 1949). The "datum"that for land health has always implied that there would be some use of it and that it would be for determining some (1) condition, (2) difference, (3) trend, or (4) future state. (Rangeland health is said to be the degree to which the integrity of the soil and ecological processes of rangeland ecosystems are sustained. Like other concepts of land health, it is considered independent of whether the land is suited to produce livestock, wildlife, or other human benefits. Presumably, health is a general condition that provides maximum opportunities (un-named) for the future. Land is healthy when it is active, maintains its structure and processes over time, is resilient to stress, and achieves reasonable stability.)
There are conditions other than wilderness, natural areas, and critical areas in which baseline concerns are relevant. These include:
A long-term ecological monitoring program provides the framework in which to obtain the required information. Monitoring programs can, with analyses and use:
One of the tasks of the Scientific Committee on Problems of the Environment, International Council of Scientific Unions (Scope 1971:15) was "... to investigate the usefulness of studying past changes in selected parameters in order to establish baseline values and to investigate the possibilities of establishing environmental archives..."
Scope (1971:60) observed:
One of the main objectives of the reference stations is to establish trends. In addition to this it is important to extend the "baseline" backwards in time by a process of "historical monitoring." The chemical stratigraphy of marine and lake sediments, peat bogs, glacier and ice cores and similar time-layered materials should be analyzed. Coral reefs, tree rings, herbarium and museum specimens of biota collected in former times have all been shown to be highly informative in this respect. As an extension of this activity, it would be essential to develop an efficient storage of collected samples as an environmental archive. Soils, water, biota and air filter samples preserved by deep freezing could be of great value in the future.
Scope (1971:6) recommended that: "...an international, integrated network of reference stations (low exposure or `baseline' and medium exposure or `regional' areas, transects or sites) based on national activities be established for the collection of data pertinent to global environmental monitoring."
Also (Scope 1971:10) recommended:
"The design of a system for preserving samples from air, water, soils, and biota in order to make future re-examination of past environmental conditions possible (environmental archives)."
"Archives are becoming increasingly expensive to maintain and, as data collection continues unabated, increasingly difficult to manage. Still, as the rate of social and environmental change continues to increase, archives and collections are among the few links to the past available to researchers" (George et al. 1986:12).
Baseline studies have become an accepted element of many environmental protection programs, partially in response to NEPA (Hirsch 1980:84). Hirsch observed that the Bureau of Land Management required a 2-year environmental baseline and monitoring program as part of oil shale development, and listed many other baseline studies, mostly needed prior to major developments. "Heavy reliance is being placed upon baseline studies to help decision-makers meet the intent of NEPA and other environmental regulations" (Hirsch 1980:84). He also said that baseline studies are "...necessary to provide understandings which can help minimize environmental impact of various developments and reconcile the inherent conflict between environmental protection and economic development..." (Hirsch 1980:84).
Hirsch observed that for ocean waste disposal sites, baseline studies have the purpose of determining
"...the physical, chemical, geological, and biological structure of a proposed or existing disposal site at the time of the survey. A baseline or trend assessment survey is to be regarded as a comprehensive synoptic and representative picture of existing conditions; each such survey is to be planned as part of a continual monitoring program through which changes in conditions at a disposal site can be documented and assessed."
Bratton (1989:103) said that confusion about management of scientific projects in wilderness has discouraged many environmental monitoring projects from utilizing wilderness. She said (1989:108)
"at the very least, there is a national need for basic environmental monitoring in wilderness, to establish baseline information banks for attacking site integrity questions. This effort needs to encourage basic ecological research, including all types of biological surveys and descriptive studies." As late as 1995, it was surprising to many that a distinguished committee concluded that there was no standard or acceptable condition of rangeland ecosystems or interpretations of the assessments now used.
Wolfe (1983) said that mined land revegetation success standards may best be selected according to post-mine land use and site specific requirements. Formulating those requirements may require a standard and she suggested an historical data base as appropriate. "Historical records allow various means of data manipulation for comparison purposes and is one approach for establishing a flexible, relevant revegetation success standard."
Tiarks et al. (1991) described a network of long-range experiments proposed for forests of the U.S. to evaluate the effects of soil compaction and organic matter removal on soil productivity.
Green and Franklin (1989:115) said
"most of the research being done tends to be short-term and lack an holistic emphasis. Although every study contributes to our overall understanding of ecological processes, baseline information on a wider range of natural processes is needed. Such studies need to be carefully designed to identify trends and improve our understanding of natural processes. A repeated series of measurements or permanent sample plots with certainty of funding is badly needed to identify trends."
Baseline studies (Hirsch 1980:85) establish existing background levels and conditions so that future changes can be ascertained. They may warn of unsuspected effects.
Trends
The objective of most observations among the six conditions above is said to be "to discover trends." Among the objectives of the International Council of Scientific Union's Scientific Committee on Problems of the Environment (Scope 1971) was "projecting current environmental trends into the future on the basis of alternative hypotheses of future human activity, population, use of natural resources and energy requirements..." Herein we discuss the meaning(s) of this apparently straight-forward intent.
An ecosystem, as it presently exists, can be observed, but it is difficult to do so. It is very much a function of and related to nearby things. Rarely can an entire system be observed. Even then, most sensory experiences are limited; the pure one is reached only through careful training. The observed present ecosystem only achieves meaning when its past is included in the observation. It achieves relevance when the future conditions are predicted. An ecological observation, therefore, perhaps more so than any other type of observation, is temporally complex. It is past, present, and future. This section of R* Guidance addresses that complexity; it is a probe of the concept of "baseline."
Presence
There are interval phenomena in the ecosystem: one fern, one bird. Each is named; all names are taxa. One plant of a species is seen. The observation is:
sit
for I saw one specimen of the ith animal of species, s, at time t (e.g., 24 November 1994). A simple observation can become difficult to analyze since the observation may have been of a subspecies or variety, thus slightly misidentified.
We can see several types of errors arising in formulating a description of things present:
Class 1 - Calling something present when it is not (related to statistical Type 1 error)
Class 2 - Calling something present with a high (but not 1.0) probability (based on historical records or observations, known associations, or trends) when it is no longer present.
Class 3 - Falsifying an observation of presence.
Class 4 - Missing an observation of something; by omission, implying it is absent when it is present related to statistical power or (1 - beta) ( Peterman 1990:2024). (The probability of rejecting the hypothesis of non-presence when there is a species present.)
Class 5 - Saying something is absent. (A logical error, since absence is unlikely to be proven.)
Because there are errors, possible and very likely, then we can, in our final computations and thought processes, include as a standard presence the value of E (of whatever type) that is probably time-specific, thus
sit (1.0 + Eit)
Eit is a proportion (zero to 1.0) and may be plus or minus. Hopefully it will be close to zero. Eit, even though called an "error term", should not connote evil, inefficiency, slovenliness or any such ideas. It is used to recognize the difficulty of a pure sensation or perfect observation and is a means to express and therefore estimate and compute the difference between the observed and actual conditions of the system.
Richness
Presence studies often result in "checklists", the core of so-called richness studies. Taxa can be counted. Richness is the total number of taxa present in some specified group. A "group" could be "riparian (stream side) birds" or "grassland mammals." There are several types of richness used by various students of areas. Where R is richness, then
R = (
i) - 1
meaning simply the sum of all of the "ones" observed (each ith species) for all n species at time t, given that only one species being present cannot be called rich. Richness is a function of the designated area of study and generally follows species-area curve relations. Richness is of interest but discovering trends is said to be a major basis of baseline work. One elementary consideration is to estimate a rate, r1, by
r1 = (Rt+1 - Rt)/Rt (2)
For example, where there were once 50 species and there are now at time (t+1) only 48, then
r1 = (48-50)/50 = - 0.04
and a 4 percent loss is reported. This is equivalent an expression of a condition in relation to its "former self", the baseline, being
r2 = (1.0 - |r1| ) x 100 (3)
where r1 is the rate of change (as absolute value) as above. When r1 is 0, then there is no change and r2 = 100%.
The problem with a simple rate computation is that some system performance measure, Q, may change and the difficulty that may be experienced in determining whether change is significant. If we could somehow learn that the ecological system being studied fluctuated regularly, then we would probably not be worried about drastic-appearing rate changes. The system would soon return, naturally, to some former level. Of course knowing whether a system is cyclic or if future levels are predictable is also the problem and one of the reasons why baseline, monitoring, or inventory projects are needed. Determining whether the time at which Q will level off may be very difficult but importsant. Comparing differences in fluctuating populations can be difficult.
Sokol and Rolf (1969) presented a procedure for comparing the rates of two linear regressions. The test is for whether the beta values are significantly different. It is easy to imagine situations in which, on average, there has been no change. In complex systems, linear time-series are rare. Toxicants, the greenhouse effect, or other phenomena may cause a change in the shape of the curve. Computing complex curves is difficult; comparing them even more so; deciding on whether the difference is ecologically or economically significant or significantly affecting human public health is almost impossible. The comparative nature of these relations leads to the observation that some basis for comparison is needed.
"Has there been change?" is a reasonable question. "As compared to what?" is a reasonable sequel prior to an answer. Answer: as compared to Rt , a previously observed condition. That condition is the basis for comparison, the base. (Whether "-line" must be added to "base" can be discussed. Perhaps prior usage may make it appropriate.)
Stability: Relative Constancy
"Ecological stability" seems intuitively to be desirable. Many people express stability as an objective in various publications. We suspect that it is desirable but we are not sure if the ecological condition today is the level at which we should stabilize things. Herein we temporarily surrender in the face of the enormity of defining the desired level and accept the premise that people desire to retain indefinitely (at least for 50 years) at least the ecosystem of today. This condition might be called a baseline. Over time, this condition should remain approximately constant if the system is stable.
How will I know when we have achieved such stability? It is always difficult to know anything. Knowing about ecosystems is unusually difficult.
Stability problem number 1, a topic unto itself, is one of deciding the system performance measure. What will we measure and plot; what parameter measurement and units will we use for our computations? Not ignoring but merely dodging the issue for now, we assume some parameter can be selected or decided upon. For example only, let us say that birds seen along transects each spring is the agreed-upon integrative measure. Implicitly: if bird numbers are stable, then the ecosystem is stable.
Stability problem number 2, the major emphasis in this section, is its computation. If the observed birds have a value of 60 in the base year and a computed rate of change of zero, then is the system rate of change zero? Perhaps. Some will ask about how variable are the observations over the years and whether the similarity (or differences) could be due to chance or random events, thereby the bird count would not be reflecting the ecosystem at all.
It is apparent that there are 2 major dimensions of the question, one is structural, the other functional. Fedorov (1975) made this point. He said stability is a measure of the temporal inconstancy of a function. It expresses the variation around a mean over time. The variation can be measured as:
These values express annual changes (from whatever causes or forces). These expressions of variation are seen as evidence of functional aspects of the ecosystem.
We may use one measure, such as for birds, vi, and average them for a period then compute variance during that period. "How different is the system each year from the mean?" seems like a reasonable question. "How different is it from the first year, the base year?" also seems reasonable. The ratio of the standard deviation to the mean, the coefficient of variation, is well known in statistics. By comparing the variance to the mean, the results is Federov's G which is
G = 1-(V/M) x 100 (4)
Note that when M = V the conditions are Poissonal, often said to be evidence of a random state, and thus G = 1 and the condition is equivalent to a non-system each year or a completely new system apparently arising at random. (This analysis of G, while useful, falls short when we consider thresholds (excessive deviation) or the combined action of factors (e.g., pH on aluminum toxicity). We may then make G conditional upon no ecosystem element, xi passing a "permissible measure.") Similarly there may be invariants in the system (e.g., cyclic lynx or P/R ratio). Thus, we may make V conditional upon vi being constant or greater than some level.
Many people prefer factor-by-factor analyses of large systems. Even they realize that there are uncertainties in each factor and most of them are changing over time, some increasing, some decreasing but all at different, varying rates. Decision makers usually only do one thing (e.g., build a pond) but every act potentially influences at least 500 nameable, measurable ecological factors in regional ecosystems. A simple table for 50 years suggests 50 x 500 entries, an equivalent table for uncertainties, ranges, and probabilities. The minimum question seems always to be: Is is all right to change the environment? Then: Compared to what? Net effect. Then: Compared to what? and eventually the answer returns to: baseline. This answer to what is a good baseline for most practical ecologists is wilderness (with no value connotation, no notion of "good" or "best", only a standard), or today's measured condition. Foresters use "the normal forest." I believe there should be devised a computerized concept of the standard ecosystem. Its singular role (beside education) would be as a basis for making comparisons of other ecosystems before and after actual or proposed change.
Significance of Change
Whether any change over time is "significant" or whether there is a "significant difference" between two numbers like 50 and 48 is a matter of economics, not ecology, for it is grounded in the worth, value, or relative importance of one unit of change and influences how scarce resources will be allocated. The loss of a prize race horse would seem to be a good example. It may be worth millions of dollars and have great personal value but that total value will surely shift if the horse is in a stable of 100 horses or in one containing merely 3 horses. The points: (1) each species has value and that value is likely to differ between species, (2) the loss of one species may not be equivalent to the loss of another, and (3) the value is related to the context; the value of some species is probably related to knowledge of the presence of other species. (The more there are, the less important is any one species on the grounds of ecological adaptability, niche shifts, and our limited ability to perceive changes.)
Hirsch (1980:91-92) talked about 2 kinds of "so what?" questions. The first is about changes produced and whether irreversible damage has occurred. In one sense, this is the question of real impact, but it is unresolvable because "damage" is not definable without a concept of value. This problem leads to the second "so what?" which is a question of tradeoffs between the benefits of a proposed project or action and the costs, losses, or perceived damage. The latter, Hirsch said, is outside the arena of environmental impact assessment but this seems very controversial in light of the need to compare alternatives. He suggested that assessments should provide the information that make the value choices more clear.
Taxonomy
The list of life forms present, plants or animals, may increase by 10 percent in one year. This may be due to migration, drastic land use change, or the presence of a taxonomic specialist! I am not raising the debate of "lumpers and splitters" but only recognizing the role of the taxonomist in the issue of deciding presence or absence of a taxon. There are groups of plants, for example, that are difficult to identify for various reasons (e.g., Carex, Vitis, Salix). That is why they are so frequently listed as genus followed by spp. A specialist arriving in an area at the proper time (e.g., flowering) can name species and subspecies, adding many taxa to one listed before as "Genus spp."
Baseline work teeters on an edge, perhaps a point, called taxonomy. A world expert on a group such as the microlepidoptera might prepare a long list of species for a forest. The insects may never again receive the same scrutiny. How will such lists be handled? Is past knowledge to be ignored? Must all species and subspecies lists be collapsed to Genus? The insects, as other life forms, suggest the need for taxonomists able to discriminate species or subspecies differences among larvae, pupae, and adults... for each species! Plant seeds suggest a similar taxonomic problem in the plant realm. A seed is seen and collected. It cannot be identified. Is it to be placed into some "other" class? Will it be withheld to await naming? What are the consequences (to anyone, to a baseline list, to a species count, to an index expressive of biodiversity) of ignoring things not nameable with confidence or naming everything?! What are the consequences of changing names:
I see no easy or likely-to-be-acceptable way out of this extremely complex area. It needs resolution because laws now require attention to biodiversity and for many people that means giving attention to lists of species, and to most people it implies that the list should not be shortened (i.e., species "extirpated"). That the list can be shortened by a taxonomist (not by biological or other extinction phenomena) needs to be recognized. Overly long lists probably exist (due to misidentification and unjustified separations which are now seen as being the same through DNA analyses, etc.) and these need to be shortened. Use of DNA analyses will increase the length of others.
Four Premises
It seems reasonable that all analyses of the taxa of an area should report counts made within each taxon. These should start at "Kingdom" and become more precise, even to the level of "variety" (for there are areas where domesticated plants are very much a part of the present ecosystem (as in historic areas of the National Park Service).) Such listing allows cumulative or dis-aggregated analyses to be performed for various purposes.
A second premise for baseline work is one of pragmatism. We must use the names of the taxa available to us. We must at least attempt to count and understand the meaning of the presence of each nameable entity. When we see or suspect important roles of observed but un-named forms, then we must get taxonomic work done.
A third premise is general but of utmost importance. We have not collected and named the creatures and plants of hardly any area. We cannot study the interactions (said by some to be "ecology") of plants and animals if we cannot even name those that occur. We, at least, must allow and encourage biological identification (systematics) to regain a respectable role in university education and agency work.
You see it; it is present! Perhaps. Perhaps it is misidentified, perhaps a flawed observation. You do not see it; it is absent! Of the following statement we can be certain: that statements about the certainty of being absent cannot be proven. Many biological phenomena are transitory. There are plants that only appear briefly in the spring; birds migrate; some species are cyclic; some animals hibernate; some have very long periods of development and behavioral stages (See Time Section). As always, rarity decreases the probability of observation. Even developmental stage (e.g., a fern embryo) influences observability and detection of presence. Many people will say "species X should be here. The conditions are right; all of the associates are present. I do not see it today." Timing, time spent, conditions, and the observer's abilities are all part of the observation of "presence." While extreme uncertainty of taxonomy may engulf the observer, an equal difficulty enshrouds those that are knowledgeable about ecosystem changes over short periods. For example, birds shift migration patterns. Hundreds are present one year, none seen the next (even though the total abundance is the same). Salamanders migrate to the upland woods and return to breeding areas 8 years later. The time is "t" but to the ecologist the time is always in error. It must be many years, at least a decade to be practical. Quibbling about sample sizes of less than 30 on statistical grounds and ignoring temporal change is folly.
The result of these premises about "presence" is that the phenomenon requires use of a presence probability table (Table 1). Creating the table has all of the requirements for appropriate observation techniques and sampling intensity. Some species, e.g., species 5 was not observed (as seen in Table 1); it has a probability, however small, of being present.
| Table 1. Example of a presence probability table. The probability of presence (indicated by X) of five hypothetical species is based on four sampling periods. | |||||
|---|---|---|---|---|---|
| Species | Date | Probability (K) | |||
| 1 | 2 | 3 | 4 | ||
| 1 | X | X | X | 0.75 | |
| 2 | X | X | 0.50 | ||
| 3 | X | X | 0.50 | ||
| 4 | X | 0.25 | |||
| 5 | >0 | ||||
"Expected richness", an observation for some year t, is the sum of the products of the probability and actual occurrence, and is:
R2t = Sit K
Richness (also viewed by some as "biodiversity") has been described as:
R3t = R2t - 1
One basis for this formulation is that an area with only one species has no richness (i.e., 1 - 1 = 0). When the number of species is very large, the subtraction rarely has any effect on other computations on their results.
When all items are counted giving a total of N (e.g., plants), then richness can be formulated as
R4t = R3t / log10 (N+1) (6)
providing a statistically unbiased estimator. For example, when there are 20 species and 2000 creatures, then
R4t = (20-1)/log (2000 + 1)
R4t = 19/3.3 = 5.76
This can be compared to a situation of more species, say 30, but with fewer specimens in each, say 1000, as
R4t = 29/log (1000 + 1)
R4t = 9.67
The larger numbers (greater richness?) exist when there are few creatures per taxon (i.e., evenly distributed). When there are only 20 creatures and 20 taxa, then R4t = 14.37. When there are 300 creatures and 300 taxa, then R4t = 120.6. It is doubtful if maximizing richness or this index is what is intended by well-meaning advocates of "biodiversity." It represents one basis for making comparisons but keeping the two measures (species and abundance) separate may be a reasonable strategy for the near future.
Abundance
Counts of things represent abundance. There are 3 plants of species No. 1, 10 of species No. 2. Abundance is typically species- or taxon-specific. In some studies, biomass is an expression of abundance.
In baseline studies, change in abundance is of equal and usually more interest than change in richness. "Species 1 increased to 10 and species 2 decreased to 3 plants over 10 years" would be a representative finding. "I was correct in my listing within 3 or 4 plants" would not be an unexpected statement of a field observer. There are many reasons for such apparent imprecision as field workers well known. With small numbers observed, the results can, if taken literally, result in an observation of 3 plants meaning "from minus 1 to 7 plants." Except for temperature, I know of few natural minus-number conditions in ecology. This usually means that the frequency of observations is not normally distributed (not bell-shaped).
Baseline work may require comparisons of abundance observations each with an error term, sometimes expressed as a percentage of the observation. ("I am no more than 1 or 2 percent off!") Table 2 shows some results of this phenomenon and the difficulty of comparisons. Element 4 may not have changed at all. Ratliff and Mori (1993) provide a "squared Euclidian distance" procedure for analyzing these changes.
Because density estimates are so poor, and error terms so large, one reasonable thing to do with them is to collapse them into "probable richness", larger numbers merely suggesting larger probability of detection in future studies. No matter how sophisticated the analyses (e.g., Poisson analyses of the probability of rare events or binomial analyses of presence-absence sequences), there is little that can be done to improve the environment for decisions about rareness.
Diversity
Patil and Tallie (1982) have analyzed diversity indexes, found most of them flawed or closely correlated, and found that many, at best, expressed average rarity. Between-period comparisons cannot be made properly with most diversity indexes. They are notoriously equifinal; very different numbers produce the same Simpson or Shannon diveristy index. If there is a "point" to be made, the probability of any ability to compare ecosystems using baseline observations is very low. It will be contended by some people that the upper limit of goodness is the upper estimate on the error term (the largest percentage as in Table 1.) See the section of R* Guidance on "Variety."
Analyses of Changes in Elements
Changes determined by the post hoc analyses that are assumed for baseline studies are likely to occur in at least the following areas (partially based on Ohmann and Ream 1971):
| Table 2. A set of hypothetical observations made with baseline at time t. Trying to find an appropriate means to discuss ecosystem change (here a simple one of 5 elements) is difficult. Change in each element is a commonplace. | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|
Ecosystem Element |
Time t
Observed % Range Value |
Time t + 1
Observed % Range Value |
||||||||
|
|
||||||||||
| 1 | 3 | 5 | 2.85 | - | 3.15 | 4 | 10 | 3.60 | - | 4.4 |
| 2 | 5 | 10 | 4.50 | - | 5.50 | 8 | 5 | 7.60 | - | 8.4 |
| 3 | 20 | 2 | 19.60 | - | 20.40 | 17 | 3 | 16.49 | - | 17.51 |
| 4 | 30 | 5 | 28.50 | - | 31.50 | 35 | 10 | 31.50 | - | 38.50 |
| 5 | 6 | 20 | 4.80 | - | 7.20 | 12 | 30 | 8.40 | - | 15.60 |
A list of the factors for evaluation and comparisons:
General
Vegetation
Fauna
Water
Soil
Use
The very general questions being raised (perhaps not adequately addressed) are "where have we been in our descriptions and analyses; where should we go?" Interpretation is needed. Field experience needs to be applied somehow.
The Comparisons and Procedures
Comparisons with a baseline value are usually made between areas, times, and proposed or actual treatments.
The Measures:
Positive or negative change; change in percentage or proportion; squared difference are conventional measures. A chi-square test may suggest significant differences in the counts. Similarity indices: For example: The insects now are similar (0.89) to those during the baseline.
Means (averages):
The means appear to be different but a t test indicates the apparent difference could easily be due to chance.
Variance:
An F test indicates that the variance is now greatly reduced from that prior to the baseline. Changes in variance ratios can be helpful.
Range:
The smallest values observed are identical in the two periods but a much larger value was observed in the recent work.
Kurtosis:
The baseline data were normal. The kurtosis is now for example, some computed number, interpreted as, "strongly skewed toward the higher values." This is suggested by both of the above observations of variance and range.
Baseline Photographs
 |
| Example of area comparison using baseline photographs conducted by George Gruell, US Forests Service, Bridger-Teton National Forest, from Forestry Research West, June 1981 |
The photo record is designed to detail changes, or lack of change, in the topography, vegetation, and surroundings of the area. It is in opposition to current traditional research that covers a relatively short period, defines a problem, studies the conditions, and quantifies the results. Comparing photographs made years apart can provide an opportunity to investigate changes in wildlands and relations within them. It has been done by many people.
 |
| Example of area comparison using baseline photographs conducted by George Gruell, US Forests Service, Bridger-Teton National Forest, from Forestry Research West, June 1981. Top photo 1893; bottom, 1968; lodgepole pine establishment and stream channel changes |
Unifying Baseline Studies with Environmental Models
Baseline studies in the past have extensive sets of observations, data collections, files, and reports. They have been descriptive. They are analyses awaiting events to negate them. Their value was highly speculative, a high investment with a very uncertain payoff on an unspecified date at an unspecified and variable interest rate.
We seek to understand each area but first we must understand "to understand." For the modern citizen, understand means that we want to know an area well, so well that we can predict what it will do or be next under normal conditions or to what it will change if influenced by some outside forces. We topically want to use the baseline as a giant simile. We want to use our deductive powers of: if area X is like the baseline, then the factors, processes, phenomena, and forecasts will be approximately the same.
Within The Trevey are the concerns, on a limited scale, of those expressed in the Tbilis, Georgia, USSR, symposium in 1974 (EPA 1975). These and others are:
Not as expansive as the above list, the philosophy within The Trevey is one that baseline studies may be and need to be wed to regular, short-term analyses of change and to predictive models with model validation, then used with corrective feedback as part of an on-going clarification of the past, improved estimates of the present, and insightful predictions and scenarios of the future.
Descriptive information on large-scale ecosystems could prove more meaningful if structured to accomplish "ecological characterization." An ecological characterization is a description of the current important components and processes comprising an ecosystem and an understanding of their functional relationships. A guiding element in the characterization is a conceptual or descriptive model. Ecological classification systems based on hierarchical concepts, combined with conceptual ecosystem modelling emphasizing simple linear processes or rules should help provide a structured approach to the definition of reasonable study boundaries.
To these ends, a set of models that is representative of what is believed to be a universal set of general land "uses" or types of human-related changes to wildland and related ecosystems have been formulated. Work will continue as resources become available. The universal set is for events in space. Each will include change over time, usually tied to the concept of ecological succession and using the concept of probable transitions. The set is of: points, lines, areas, and volumes. (Throughout ecology, there are strong relations among these four.) The strategy planned for the future (not in order of development) includes creating ecological and economic computer models, typically simulations. The models produce estimates of likely or expected future consequences of work on the area in each of the following major spatial categories (with examples).
Points
1. Building a single structure (e.g., a tower, a building, an ampitheatre)
2. Experiencing a point catastrophe (e.g., a small fire, an airplane crash)
Lines (or Corridors)
3. Placing an underground pipeline
4. Placing a road or trail
5. Placing a powerline
Areas
6. Effects of wild fire (consequence tables)
7. Deer harvest in the country
8. Timber harvest (hypothetical)
9. Gypsy moth effects (on stand composition and other factors)
10. Insecticide use for gypsy moth control (primary emphasis on litter decomposition)
11. Wildland preservation (primary emphasis on litter, residue, and fuel buildings)
12. Litter and residue buildup (effective on rodents and raptors)
13. Trail use (emphasis on water budget) and user perception of "crowded")
14. Heavy metal build up from traffic (effects on litter decomposition)
15. Levels of human use (on noisescape)
16. Temperature change (e.g., global warming)
17. Solar spectrum change (e.g., ozone layer depletion) on forest stand structure
18. Forest stand structure changes (on faunal richness, abundance, and diversity)
Volumes
19. Pumping of groundwater
20. Timber harvest or area preservation on the multilayered ecosystem volume
21. Estimating pollutants in air over an ecosystem.
Many such models have been created in the past by scientists and agencies. Suggestions are sought for how to obtain existing models and to get them operational within The Trevey. The models can be created one at a time. Great efficiencies due to synergism can be experienced by joint work on several or all of them simultaneously.
General
Baseline studies can be very expensive. Some require expensive equipment and logistic support. The direct cost of preserving land and protecting it are high -- as well as are the accumulated foregone benefits usually associated with such protection. Difficulties arise in the concept, for without it there can be little assurance that a scientifically sound program will emerge for determining changes. Without assurances, study funds can be claimed to be wasted. "Baseline" studies have been thrown in with impact studies for criticism due to their ineffective design and execution and "...emphasis on indigestible descriptive data." Other criticism has been directed at the enormous costs of museums, libraries, data storage, photo storage, and low scientific returns (Hirsch 1980:85). "Even if these major obstacles to the design of meaningful studies can be overcome, the baseline approach is probably of quite limited value unless it is an integral part of a much larger effort to address ecosystem characteristics and dynamics" (Hirsch 1980:91). Overcoming this latter criticism, and some of the others may be achievable through modeling within The Trevey.
Literature Cited
Bratton, S. D. 1989. Environmental monitoring in wilderness, p. 103-112 in H. R. Freilich compiler, wilderness benchmark 1988: Proc. of the Natl. Wilderness Colloquirum, USDA For. Serv. S. E. Forest Exp. Sta., Gen. Tech. Rpt. SE-51, Ashville, NC. 228 pp.
EPA. 1975. Proceedings of the first US/USSR symposium on comprehensive analysis of the environment. EPA, 600/9-75-004, Washington, D.C. 188 pp
Gogan, P. J. P. and R. H. Barrett. 1986. Tule elk at Point Reyes National Seashore, p. 32-81 in F. S. Singer, Ed. Wildlife management and habitats: Proc. of the Conf. on Science in National Parks, Vol. 2, Colorado State Univ., Ft. Collins, CO. 184 pp.
Greene, S. E. and J. F. Franklin. 1989. The state of ecological research in Forest Service wilderness, p. 113-119 in H. R. Freilich compiler, wilderness benchmark 1988: Proc. of the Natl. Wilderness Colloquirum, USDA For. Serv. S. E. Forest Exp. Sta., Gen. Tech. Rpt. SE-51, Ashville, NC. 228 pp.
Gruell, G. E. 1980. Five's influence on wildlife habitat on the Bridger-Teton National Forest, Wyoming. Vol. 1 - Photographic record and analysis. U.S.D.A. For. Serv. Res. Paper INT-235, Internt. For. and Range Exp. Sta., Ogden, Utah, 207 pp.
Hirsch, A. 1980. The baseline study as a tool in environmental impact assessment, p. 84-93 in Biological evaluation of environmental impacts: Proc. of a Symposium in 1976, U.S. Fish and Wildlife Service, Washington, D.C. FWS/OBS-80/26, 237 pp.
Jensen, C. E. 1979. e-k, a function for the modeler. USDA For. Serv., Intermountain For. and Range Exp. Sta., Res. Paper. INT-240, Ogden, Utah. 8 pp.
Leopold, A. 1949. A Sand County almanac and sketches here and there. Oxford Univ. Press. New York, N.Y. 226 pp.
Lacate. 1973. Collecting, storing, and evaluating data for nature conservation purposes. p. 13-25 in A. B. Costin and R. H. Groves eds. Nature Conservation in the Pacific, AUN Press, Australian National University.
Margules, C.R. and M.P. Austin. 1991. Nature conservation: cost effective biological surveys and data analysis, CSIRO, Australia
Ohmann, L. F. and R. R. Ream. 1971. Wilderness ecology: a method of sampling and summarizing data for plant community classification, USDA For. Service, North Central For. Exp. Sta., NC-49, St. Paul, MN, 14 pp.
Patil, G. P. and C. Tallie. 1982. Diversity as a concept and its measurement. J. Am. Stat. Assoc. 77:548-561.
Peterman, R. M. 1990. The importance of reporting statistical power: the forest decline and acidic deposition example. Ecology 71(5):2024-2027.
Raine, R. M., J. L. Kansas, B. Shelast, and J. Martin. 1990. Biophysical inventory of the Siffleur and White Goat Wilderness Areas, Alberta Recreational and Parks, Provincial Parks Service, Report by Beak Associates Consulting Ltd., Calgary, Alberta (Abstract).
Ratliff, R. D. and S. R. Mori. 1993. Squared Euclidian distance; a statistical test to evaluate plant community change. U.S.D.A. Forest Serv. Res. Note PSW-416, Pacific Southwest Res. Sta., Berkeley, CA 4 pp.
SCOPE. 1971. Global environmental monitoring: a report submitted to the United Nations Conference on the Human Environment, Stockholm, 1972. Stockholm. 67 pp.
Sokol, R. R. and F. J. Rolf. 1969. Biometry, W. H. Freeman Co., San Francisco, CA 776 pp.
Tiarks, A. E., M. S. Kimble, M. L. Elliott-Smith. 1991. The first location of a national, long-term forest soil productivity study: methods of compaction and residue removal. P. 431-442 in S. S. Coleman and D. G. Neary, eds. Proc. 6th Biennial Silvicultural Research Conf. Vol. 1. Gen. Tech Rpt. SE-70, Ashville, NC.
Wolfe, M. H. 1983. Use of a [sic] historical data base as a revegetation success standard, p. 47-49 in E. F. Redente, W. E. Sowards, D. G. Steward, and T. L. Ruiter eds., Symposium on Western coal mining regulatory issues: land use, revegetation, and management, Colorado State Univ., Range Sci. Dept. Sci. Series No. 35, Ft. Collins, Co.
| Quick Access to the Contents of LastingForests.com |
|---|
This Web site is maintained by R. H.
Giles, Jr.
Last revision January 17, 2000.