THE GOOD, THE BAD AND THE UGLY OF MONITORING PROGRAMS: DEFINING QUESTIONS AND ESTABLISHING OBJECTIVES BENJAMIN B. STOUT National Council of the Paper Industry for Air and Stream Improvement, Inc., 1545 Takena SW, Albany, Oregon 97321, U.S.A.
(Received: 15 May 1992)
1. Introduction All of us who have been around graduate students have had the experience of poorly defined thesis research proposals. Something like 'I want to study the vegetation of watershed X ' is an example. The student hopes that if enough data are collected that somehow there will emerge full blown something of value that will pass a dissertation committee. When monitoring programs are planned and implemented it is imperative that we do not regress into the neophytic student mode. In what follows I will attempt to construct some aids to assist those designing and implementing monitoring efforts to resist the penchant for regression to an unproductive but enchanting effort of data collection.
2. Some Definitions Monitoring is defined in my W e b s t e r ' s as: To watch, observe or check for special purposes. In the case at hand we are interested in natural resources. The watching will involve natural resource systems. Natural resource systems can be viewed as wholly environmentally deterministic or as hypergeometric dynamic systems. In the case of environmental determinism, the influence of man is denied and the environment is all powerful or, as the name implies, deterministic. Hypergeometric dynamic systems are those that consist of more than three dimensions and are ever changing. For forests we have at bare minimum five dimensions: light, temperature, water, nutrients and germ plasm. In working situations we use many ways to express the complexities of the factors that constitute the five basic dimensions. I choose to view the forest from this perspective. I do that mainly because the idea of environmental determinism, however enticing, has been shown to be fallacious (Hartshorue, 1939). Environmental Monitoring and Assessment 26:91-98, 1993. (~) 1993 Kluwer Academic Publishers. Printed in the Netherlands.
92
BENJAMIN B. STOUT
ECOSYSTEM MODEL dX 1/dt
- f I (X 1,X2,...Xn; E 1...Era; err)
dX2/dt
• f2 (X1,X2,...Xn;
E1...Em; err)
dX3/dt
- f3 ( X 1 , X 2 , _ X n ;
EI...Em; err)
dXn/dt
• fn (X1,X2,...Xn;
EI...Em; err)
Fig. 1. Lotka and Rashevsky suggest a system of differential equations. Here the Xi represent species, the E~ represent environmental variables, and the ERR the random elements in the system.
3. A System Description Alfred Lotka and then Nicolas Rashevsky used systems of differential equations to describe hypergeometric dynamic sy stems (Lotka, 1956 (1924); Rashevsky, 1960). Their view was highly deterministic. I suggest that we should add a random element to their descriptions. In so doing, however, we do not alter a fundamental principle derived from the system of equations: If rates of change in a system are changed to rates abnormal to the system it is not possible to predict where the system will go. Implicit in this principle is the idea that changes occur in systems and there are normal rates of change. Incidentally, the two works provide the most elegant statement of what ecology is all about that I have ever encountered (Figure 1). From the system of equations we see that a complete linkage exists between all species, the Xi, and the environment, the Ei. What we do not know is the size of the coefficients if the equations were fitted to data. There must be added to this description of the system an historical element, or in the context, a case of a really abnormal set of rates. The forests of North America were severely stressed during the Pleistocene. We have had some 10 000 years since the retreat of the last continental glacier. During that time the forests have reclaimed the land from which they were displaced. The reclaiming is done
MONITORING PROGRAMS: QUESTIONS AND OBJECTIVES
93
within the boundaries of species silvical characteristics and the local sites. Here in the Pacific Northwest we know, for example, that the Douglas fir forests rarely live to be more than 600 years old. We can, as a first approximation, estimate that what we see as old growth is at least the 16th generation of forest on the site since the Pleistocene. One view of this repeated growth and regrowth of the forest is a long running experiment with only one replication per site, and we are unlikely to run another replicate. I mention this because there will need to be judgments made to separate the normal from the abnormal rates of change.
4. Monitoring Objectives The primary objective of monitoring must be the watching of rates of change in the forest. The hard part is deciding which rates to watch and which are normal and which are abnormal. When a rate becomes abnormal, then concern might be justified. The deciding will be done from at least two perspectives: the amount of variation and the scale over which the variation occurs. Several analogies come to mind in the search for examples of perspectives. The present concern about global climate change seems a likely one. Is the mild winter in this area a manifestation of global warming, an effect of E1 Nifio, or just a part of the normal variation in weather? I have been struck this winter at the number of times when a high temperature has been recorded that the record for the date goes back for 20, 30 or 40 years. Is this change or evidence for a lack thereof or is it normal variation? The global climate change models usually have two or three cells for Oregon. That is small scale. The differences that are being predicted are matched at much larger scales as one goes from valley to mountain and from north to south slope. Rates will need to be site specific and so will the variation in those rates. The watching of the rates will have to be done in a way that is amenable to comparisons over time. This puts a tremendous burden on those who design and implement monitoring programs. The penchant will be strong for each new program manager who comes on line to make changes in protocols. Unless great care is taken to assure comparability over time the watching will have been in vain.
5. The Good, the Bad, the Ugly of Monitoring Programs The classification of monitoring programs into these three categories comes not at my wishing, but from the organizers of this conference. The literature on such categories is, needless to say, not overwhelming. What follows is an effort to use anecdotal evidence to effect the classification.
94
BENJAMIN B. STOUT
5.1. THE UGLY Enthusiasm wanes; programs get abandoned. In Corvallis in 1985 a meeting was held to discuss how all the data that were to be generated by the forest part of the National Acid Precipitation Assessment Program were to be archived. Grand plans were discussed. I believe I am correct in saying that you would find it impossible to get from NAPAP in useable form all the data that were generated in the four cooperatives of the Forest Response Program. Enthusiasm and, particularly, dollars waned. The Black Rock Forest was established in 1928 as a privately funded research forest. In 1950 it was given to Harvard University with an endowment to operate it. Among other things the objectives of the Black Rock Forest included monitoring of forest conditions in the Hudson Highlands of New York. Recently Harvard decided that it has better use for the endowment and divested itself of the forest. In all the materials that the new consortium that has taken over the management of the Forest has produced I have failed to see any mention of the maintenance of the monitoring plots that were established 2/3 of a century ago. That's ugly. 5.2. THE BAD As a less than angelic lad growing up in West Virginia during the great depression I often heard from my father one of his favorite maxims: The road to hell is paved with good intentions. So it is with changes in protocol. The US Forest Service has had a forest monitoring program, called Forest Inventory and Analysis, for at least 50 years. Its objective is to monitor changes in timber volumes on a regional basis. It has met those objectives. However, in one instance at least, the data were used for other purposes. A firestorm of debate has ensued about the validity of the conclusions drawn from the additional analyses. The fuel for the firestorm came from several sources, one of which was changing protocol. At one re-measurement data were collected at one point, at the next cycle at three points, and at the next cycle at five points. As the number of points changed the size of the plots changed and the limiting angle for plotless measurements were changed. The effect was data sets that were not easily compared over time. That's bad. 5.3. THE GOOD The good monitoring program will have several important attributes: . It will be structured and developed in such a way that it will garner long term support, both scientific and political. The scientists at Harvard considered, I suppose, molecular biology more promising than long term monitoring. The consortium members now manging the Black Rock Forest seem more aligned to preservation and environmental determinism, so they see no need for monitoring.
MONITORING PROGRAMS: QUESTIONS AND OBJECTIVES
95
INDIVIDUALTREEGROWTH EI.~K ROCKFOREST, NEWYQf~( 12.0 11.o 10,0 9.0 B.O
I
6.0
4.0 3.0 2,,0 1.0 o.o 1Ek,~l r.
1936
1941
194~;
19fi4
19~1
1965
1975
1978
198~
~r.XR + CF.~STNUTOAK
C!..~.~'tT-.lLtT~%K ¢,
CH£S'PNUTOAK
Fig. 2.
2.
To obtain scientific and political support over the long haul it is imperative that clear-cut objectives of monitoring be developed and implemented. Just collecting a computer full of data will not suffice. Clear, rigorous plans for analysis must be developed prior to data collection. If this sounds like a sound bite out of an elementary statistics course, it is.
3.
The sampling design must be developed that encompasses variation at large, intermediate and small scales. The variation at large scale (small area) will be the most difficult to assess. In the accompanying figures we see all sorts of variation that must be understood if we are to assess rates of change correctly. At intermediate and small scales (larger and larger areas) such variation is likely not detected. In Figure 2-4 we see that, essentially without notice, trees die. The reasons for the demise is not obvious; some were growing slower than others; some faster. In Figures 5 and 6 we see two very different patterns of growth of individual trees on two plots on the Piedmont of Georgia. All of the figures exhibit variation that exists in the forest, Monitoring will need to detect such differences if it has to provide useful information.
4.
The protocols must produce chronologically collected data that will permit rigorous tests of null hypotheses. The data will represent both integral and rate measures. Hypotheses to be tested will surely include ones that state that rate changes in the system are not abnormal to the system and that rate changes are not due to man's activities. Related to both of these will be determinations that with null hypothesis rejections decisions to restore rates will not induce other abnormal rates in the system that are even more undesirable.
96
BENJAMIN B. STOUT
INDIVIDUAL TREE GROWTH BI,ACKROCKFOIEST, NEWYOlk( 20.0 19.0 18.0 17.0 16,0 15.0 14"0
12"0 11"0 10,0 9"0 8"0 7"0 6"0 5"0 4.0
i
19..32 0
1937
REI)DAK
i
1942 4
194"7
1984 1961
1965
o
RED OAK
1973 1979
1~4
RED OAK
Fig. 3.
INDIVIDUAL TREE GROWTH BLACKROCKFORE-~', NEWyQRI( '~3.0 12"0 11,0 10.0 9.0 8,0
7,0 z
5"0 o
4.0 3.0 2"0 1"0 o'0 1931
19...~6 194't 1946
CI'I~fl'NUTOAK
1964
1E~1 1 ~
1973
1978
1983
YEAR + CHESTNUT OAK CHES'~UTOAK
Fig. 4. The data for Figure 2 - 4 come from plots established in the early 1930s. Note that trees die unexpectedly over the course of the history of the plots. The trees were all either dominant or co-dominant at the beginning of the plot measurements. Variation such as seen here must be accounted for in any productive monitoring system.
97
MONITORING PROGRAMS: QUESTIONS AND OBJECTIVES
O
d
.
.
.
.
.
.
.
.
.
''
.
.
.
.
locotion -rl--
.
.
.
.
.
i
.
.
.
.
.
.
.
.
.
i
.
.
.
.
.
.
•
•
,.
1 01411 5156
d
o
< .J
<
(Received: 15 May 1992)
1. Introduction All of us who have been around graduate students have had the experience of poorly defined thesis research proposals. Something like 'I want to study the vegetation of watershed X ' is an example. The student hopes that if enough data are collected that somehow there will emerge full blown something of value that will pass a dissertation committee. When monitoring programs are planned and implemented it is imperative that we do not regress into the neophytic student mode. In what follows I will attempt to construct some aids to assist those designing and implementing monitoring efforts to resist the penchant for regression to an unproductive but enchanting effort of data collection.
2. Some Definitions Monitoring is defined in my W e b s t e r ' s as: To watch, observe or check for special purposes. In the case at hand we are interested in natural resources. The watching will involve natural resource systems. Natural resource systems can be viewed as wholly environmentally deterministic or as hypergeometric dynamic systems. In the case of environmental determinism, the influence of man is denied and the environment is all powerful or, as the name implies, deterministic. Hypergeometric dynamic systems are those that consist of more than three dimensions and are ever changing. For forests we have at bare minimum five dimensions: light, temperature, water, nutrients and germ plasm. In working situations we use many ways to express the complexities of the factors that constitute the five basic dimensions. I choose to view the forest from this perspective. I do that mainly because the idea of environmental determinism, however enticing, has been shown to be fallacious (Hartshorue, 1939). Environmental Monitoring and Assessment 26:91-98, 1993. (~) 1993 Kluwer Academic Publishers. Printed in the Netherlands.
92
BENJAMIN B. STOUT
ECOSYSTEM MODEL dX 1/dt
- f I (X 1,X2,...Xn; E 1...Era; err)
dX2/dt
• f2 (X1,X2,...Xn;
E1...Em; err)
dX3/dt
- f3 ( X 1 , X 2 , _ X n ;
EI...Em; err)
dXn/dt
• fn (X1,X2,...Xn;
EI...Em; err)
Fig. 1. Lotka and Rashevsky suggest a system of differential equations. Here the Xi represent species, the E~ represent environmental variables, and the ERR the random elements in the system.
3. A System Description Alfred Lotka and then Nicolas Rashevsky used systems of differential equations to describe hypergeometric dynamic sy stems (Lotka, 1956 (1924); Rashevsky, 1960). Their view was highly deterministic. I suggest that we should add a random element to their descriptions. In so doing, however, we do not alter a fundamental principle derived from the system of equations: If rates of change in a system are changed to rates abnormal to the system it is not possible to predict where the system will go. Implicit in this principle is the idea that changes occur in systems and there are normal rates of change. Incidentally, the two works provide the most elegant statement of what ecology is all about that I have ever encountered (Figure 1). From the system of equations we see that a complete linkage exists between all species, the Xi, and the environment, the Ei. What we do not know is the size of the coefficients if the equations were fitted to data. There must be added to this description of the system an historical element, or in the context, a case of a really abnormal set of rates. The forests of North America were severely stressed during the Pleistocene. We have had some 10 000 years since the retreat of the last continental glacier. During that time the forests have reclaimed the land from which they were displaced. The reclaiming is done
MONITORING PROGRAMS: QUESTIONS AND OBJECTIVES
93
within the boundaries of species silvical characteristics and the local sites. Here in the Pacific Northwest we know, for example, that the Douglas fir forests rarely live to be more than 600 years old. We can, as a first approximation, estimate that what we see as old growth is at least the 16th generation of forest on the site since the Pleistocene. One view of this repeated growth and regrowth of the forest is a long running experiment with only one replication per site, and we are unlikely to run another replicate. I mention this because there will need to be judgments made to separate the normal from the abnormal rates of change.
4. Monitoring Objectives The primary objective of monitoring must be the watching of rates of change in the forest. The hard part is deciding which rates to watch and which are normal and which are abnormal. When a rate becomes abnormal, then concern might be justified. The deciding will be done from at least two perspectives: the amount of variation and the scale over which the variation occurs. Several analogies come to mind in the search for examples of perspectives. The present concern about global climate change seems a likely one. Is the mild winter in this area a manifestation of global warming, an effect of E1 Nifio, or just a part of the normal variation in weather? I have been struck this winter at the number of times when a high temperature has been recorded that the record for the date goes back for 20, 30 or 40 years. Is this change or evidence for a lack thereof or is it normal variation? The global climate change models usually have two or three cells for Oregon. That is small scale. The differences that are being predicted are matched at much larger scales as one goes from valley to mountain and from north to south slope. Rates will need to be site specific and so will the variation in those rates. The watching of the rates will have to be done in a way that is amenable to comparisons over time. This puts a tremendous burden on those who design and implement monitoring programs. The penchant will be strong for each new program manager who comes on line to make changes in protocols. Unless great care is taken to assure comparability over time the watching will have been in vain.
5. The Good, the Bad, the Ugly of Monitoring Programs The classification of monitoring programs into these three categories comes not at my wishing, but from the organizers of this conference. The literature on such categories is, needless to say, not overwhelming. What follows is an effort to use anecdotal evidence to effect the classification.
94
BENJAMIN B. STOUT
5.1. THE UGLY Enthusiasm wanes; programs get abandoned. In Corvallis in 1985 a meeting was held to discuss how all the data that were to be generated by the forest part of the National Acid Precipitation Assessment Program were to be archived. Grand plans were discussed. I believe I am correct in saying that you would find it impossible to get from NAPAP in useable form all the data that were generated in the four cooperatives of the Forest Response Program. Enthusiasm and, particularly, dollars waned. The Black Rock Forest was established in 1928 as a privately funded research forest. In 1950 it was given to Harvard University with an endowment to operate it. Among other things the objectives of the Black Rock Forest included monitoring of forest conditions in the Hudson Highlands of New York. Recently Harvard decided that it has better use for the endowment and divested itself of the forest. In all the materials that the new consortium that has taken over the management of the Forest has produced I have failed to see any mention of the maintenance of the monitoring plots that were established 2/3 of a century ago. That's ugly. 5.2. THE BAD As a less than angelic lad growing up in West Virginia during the great depression I often heard from my father one of his favorite maxims: The road to hell is paved with good intentions. So it is with changes in protocol. The US Forest Service has had a forest monitoring program, called Forest Inventory and Analysis, for at least 50 years. Its objective is to monitor changes in timber volumes on a regional basis. It has met those objectives. However, in one instance at least, the data were used for other purposes. A firestorm of debate has ensued about the validity of the conclusions drawn from the additional analyses. The fuel for the firestorm came from several sources, one of which was changing protocol. At one re-measurement data were collected at one point, at the next cycle at three points, and at the next cycle at five points. As the number of points changed the size of the plots changed and the limiting angle for plotless measurements were changed. The effect was data sets that were not easily compared over time. That's bad. 5.3. THE GOOD The good monitoring program will have several important attributes: . It will be structured and developed in such a way that it will garner long term support, both scientific and political. The scientists at Harvard considered, I suppose, molecular biology more promising than long term monitoring. The consortium members now manging the Black Rock Forest seem more aligned to preservation and environmental determinism, so they see no need for monitoring.
MONITORING PROGRAMS: QUESTIONS AND OBJECTIVES
95
INDIVIDUALTREEGROWTH EI.~K ROCKFOREST, NEWYQf~( 12.0 11.o 10,0 9.0 B.O
I
6.0
4.0 3.0 2,,0 1.0 o.o 1Ek,~l r.
1936
1941
194~;
19fi4
19~1
1965
1975
1978
198~
~r.XR + CF.~STNUTOAK
C!..~.~'tT-.lLtT~%K ¢,
CH£S'PNUTOAK
Fig. 2.
2.
To obtain scientific and political support over the long haul it is imperative that clear-cut objectives of monitoring be developed and implemented. Just collecting a computer full of data will not suffice. Clear, rigorous plans for analysis must be developed prior to data collection. If this sounds like a sound bite out of an elementary statistics course, it is.
3.
The sampling design must be developed that encompasses variation at large, intermediate and small scales. The variation at large scale (small area) will be the most difficult to assess. In the accompanying figures we see all sorts of variation that must be understood if we are to assess rates of change correctly. At intermediate and small scales (larger and larger areas) such variation is likely not detected. In Figure 2-4 we see that, essentially without notice, trees die. The reasons for the demise is not obvious; some were growing slower than others; some faster. In Figures 5 and 6 we see two very different patterns of growth of individual trees on two plots on the Piedmont of Georgia. All of the figures exhibit variation that exists in the forest, Monitoring will need to detect such differences if it has to provide useful information.
4.
The protocols must produce chronologically collected data that will permit rigorous tests of null hypotheses. The data will represent both integral and rate measures. Hypotheses to be tested will surely include ones that state that rate changes in the system are not abnormal to the system and that rate changes are not due to man's activities. Related to both of these will be determinations that with null hypothesis rejections decisions to restore rates will not induce other abnormal rates in the system that are even more undesirable.
96
BENJAMIN B. STOUT
INDIVIDUAL TREE GROWTH BI,ACKROCKFOIEST, NEWYOlk( 20.0 19.0 18.0 17.0 16,0 15.0 14"0
12"0 11"0 10,0 9"0 8"0 7"0 6"0 5"0 4.0
i
19..32 0
1937
REI)DAK
i
1942 4
194"7
1984 1961
1965
o
RED OAK
1973 1979
1~4
RED OAK
Fig. 3.
INDIVIDUAL TREE GROWTH BLACKROCKFORE-~', NEWyQRI( '~3.0 12"0 11,0 10.0 9.0 8,0
7,0 z
5"0 o
4.0 3.0 2"0 1"0 o'0 1931
19...~6 194't 1946
CI'I~fl'NUTOAK
1964
1E~1 1 ~
1973
1978
1983
YEAR + CHESTNUT OAK CHES'~UTOAK
Fig. 4. The data for Figure 2 - 4 come from plots established in the early 1930s. Note that trees die unexpectedly over the course of the history of the plots. The trees were all either dominant or co-dominant at the beginning of the plot measurements. Variation such as seen here must be accounted for in any productive monitoring system.
97
MONITORING PROGRAMS: QUESTIONS AND OBJECTIVES
O
d
.
.
.
.
.
.
.
.
.
''
.
.
.
.
locotion -rl--
.
.
.
.
.
i
.
.
.
.
.
.
.
.
.
i
.
.
.
.
.
.
•
•
,.
1 01411 5156
d
o
< .J
<
