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Monitoring at the

Asylum Lake Preserve:

Prairie and Oak Savanna

2003
by Chad Avery

“One problem that all the sciences face is what to measure and the history of science is littered with examples of measurements that turned out not to be useful.” (Krebs)

 

 

Designing a Monitoring Program for Plants

       In order to observe and track long-term changes of the vegetation in the oak savanna/prairie reconstruction it will be necessary to develop an effective monitoring program.  Monitoring enables land managers to answer questions such as the following.  Over what timeframe did the target species re-establish?  Are weeds or unwanted species increasing in the post-fire environment?  Are management actions still having the desired effects?  Is management having a positive or negative effect on desirable native vegetation?  How have the different species that were originally seeded persisted? (Fire Effects Guide)

           

 

 

  

Recording Plants in a Quadrat Frame

             Due to the time and expense involved in developing and implementing a monitoring program, researchers (Masters, Travis and Sutter) recommend that land managers choose strategically a few factors to monitor and not try to answer several questions with one data set.  It is suggested that issues of concern be ranked in order of importance and that the important questions are answered WELL.

 

            Restoration goals also need to be stated clearly and this will be addressed in the section ‘Site Fire Management Goals’.  If goals are vague or nonexistent, it will be very difficult to develop a method to assess whether management is successful.  If it is decided, for instance, that only large-scale changes are important, say a five-fold increase in a particular plant species, then we will not be concerned with gathering data that will detect small rates of change.  However, most monitoring programs fail because managers desire to detect relatively small changes in populations, yet only the coarsest information is gathered (Masters).  Clarity of intent will make design of the program and analysis of the data clear and concise.

           

 

Plots, Quadrats and Transects

             Because of the size of the oak savanna/prairie, more than 68 acres, it will not be possible to inventory all of the 22 plant taxa that were planted in 2001.  A complete inventory could be useful on small parcels, but inventories don’t track increases or declines in plant populations or tell which plants are common or rare.  Sampling is done when the population or area is too large to census in its entirety (Masters).  When done poorly, sampling can lead to wrong or inaccurate conclusions.  It is important to define the methods and procedures used so they can be repeated by others as well as by the initial investigator.  Three methods for sampling include plots, quadrats and transects.  Plots are permanently located parcels of a given size (.5ha) which may be randomly placed or deliberately placed to detect habitat changes.  They are typically monitored yearly for several years or decades.  Quadrats are circular or square frames, typically .25 m², which may be randomly placed throughout the reconstruction using random X and Y coordinates. 

            More typically, however, quadrat frames are placed along transects through the area to be sampled.  For example, a random location along the sidewalk on the South side of the property is selected. 

 

 

 

This location will mark the first transect going North through the property.  Next, using a random number table to select the number of paces, say 1-20, the distance is stepped off, the quadrat frame is placed on the ground and plants within it are identified and counted.  The process continues through the first transect and then another starting point is selected for the second transect until a representative number of samples has been recorded.  Formulas can be found is most statistics texts (Krebs) to determine the appropriate number of samples to take.  Sampling error decreases with an increase in the number of quadrats sampled (Krebs), but sampling does take time and costs money.  To compromise, Masters (1977) suggests beginning with a larger number of quadrats, say 75-100, and then statistically analyze subsets of 50, 40 or 30 to see where the subset data begins to differ from the larger sample. This will be the number of required quadrats in order to be timely and efficient while still providing the necessary degree of precision.  In this way the samples, as a group, represent the population in which we are interested.  The most critical components of this type of design are randomization which eliminates bias, replication which increase precision and interspersion which spatially represents the area sampled.

           

 

4 Questions

             In addition to clearly stating the procedures and methods used to sample the restoration, Masters (1977) also recommends that land managers carefully answer the following four questions.

            1.  What is the species or area that you are interested in?

            2.  What are your restoration goals and what does success look like?

            3.  What are the indicators of change that can be measured?

            4.  What kind of change needs to occur (over what period of time?) before

            you consider your program/management successful?

 

 

Indicators of Change

             Questions 1, 2 and 4 will be addressed in other parts of this Manual, so let’s take a look at question 3, ‘What are the indicators of change that can be measured?’  As mentioned earlier, quadrat frames are placed along transects through the property and every species found in the quadrat is recorded.  If desired, the Relative Cover of each of the plants can also be recorded either as a percentage or in a range of percentages.  As a management goal we may state our desire is to limit the presence of weeds to no more than 5%.  Relative Cover can help monitor this goal.  As always, it is very important to clearly state the methods used at the outset. For example, will field workers count plants that arch over the quadrat frame, but aren’t rooted in the area occupied by the frame? This will need to be clearly spelled out in order to provide consistency from year to year.

 

            Cover estimate is one measure of floristic change over time.  Other indicators of change that can be measured are Species Frequency, Relative Frequency (RFRQ) Relative Cover (RCOV) and Relative Importance Value (RIV).  Species Frequency is simply the number of quadrats in which a species occurred.  Relative Frequency (RFRQ) is obtained by dividing the frequency of one species by the total frequency of all species.

Similarly, Relative Cover (RCOV) is calculated by dividing the total cover of one species by the total cover of all species.  The most important indicator in this group, Relative Importance Value (RIV) is obtained by adding Relative Frequency (RFRQ) and Relative Cover (RCOV) and dividing by 2:  RIV = RFRQ + RCOV ∕ 2.  See Figure 17.2, below, and notice how smaller, but more numerous plants can have a higher Relative Importance Value than larger, but less numerous plants.

 

  .

 

            It is important to note that Relative Importance Value (RIV) is like a snapshot in time and may change through the season and even from season to season.  Relative Importance Value will change as new plants are added and others drop out, and even as rainfall, drought and temperature extremes fluctuate from year to year.

 

 

 

 Floristic Quality Assessment

 

            Relative Cover and Relative Importance Value are important field measures, but it is not enough to know whether species are increasing or decreasing.  Land managers must know whether these changes are positive or negative.  As a result of management, are the changes in floristic quality resulting in a diverse, sustainable community?  How then, can we quantitatively measure the changes in floristic quality?

            In 1979, Dr. Gerould Wilhelm required a method to communicate the floristic quality of natural areas to laypeople, engineers and developers in Northeastern Illinois.  Gerry co-authored, with Floyd Swink, the first edition of Plants of the Chicago Region in 1994 and introduced a system known as the Natural Area Rating Index (NARI).  With some changes and refinements, NARI is now known as Floristic Quality Assessment (FQA) and is used by natural area managers in several mid-western states, including Michigan.

            Floristic Quality Assessment (FQA) is based on the concept of species conservatism- the degree to which an experienced field botanist(s) has confidence that a species is representative of a high-quality remnant habitat.  The system does not take into account aesthetics of the species such as showiness, color and season of bloom or even rarity.  FQA rates species strictly on how resistant they would be to disturbance in a pre-settlement condition.  Using a rating scale from 0-10, called the Coefficient of Conservatism (C), 988 plants native to Midwestern Tallgrass prairies and savannas were given C values in 1996 (Herman & Wilhelm). 

A C value of 0 would represent a plant that is opportunistic and very resistant to disturbance (weedy) while a value of 10 would be assigned to a plant that is extremely sensitive to habitat disturbance - a very conervative plant.

 

  

        

Plants such as Cocklebur and Common Ragweed may have C values of 0 or 1.

 

 Pale Purple Coneflower (Echinaceae pallida) has a C value of 5.

 

             To illustrate this concept, let’s consider three native plants, Common Ragweed, Pale Purple Coneflower and Prairie Lily.  Few would argue that Ragweed can be found in just about any degraded site in Southern Michigan from cracks in sidewalks to railroad ballast. Common Ragweed has the lowest C value of 0. Next, the Coneflower is fairly sensitive to habitat degradation and has been assigned a C value of 5. The Prairie Lily, however, is narrowly restricted to high-quality prairie remnants and has a C rating of 10, the highest possible score.

If all the C values for plants observed in the quadrats during a monitoring session are added together and divided by the number of plants (n) observed, the average or Mean C value is obtained:  Mean C = ∑ C ∕ n , where ∑ is the symbol for summation.

For any given area, the higher the Mean C value the more conservative and ‘higher quality’ the plants are. More degraded sites will have much lower Mean C values.

           
The plants and their corresponding C values that were seeded in 1991 at Asylum Lake Preserve are given in the chart below.  The symbol ‘T’ indicates a plant that is threatened in the state of Michigan.

  

 

 

 

                                             

Very highly conservative plants, such as this Michigan Lily may have C values of 9 or 10.

Plants and Corresponding C values

                                                            Seeded in 1991

                                           

Botanical Name                                    Common Name                           C value

 

Aquilegia canadensis                             Columbine                                            5

Asclepias syriacus                                 Common Milkweed                              1

Asclepias tuberose                                Butterfly Milkweed                               5

Aster nova-angliae                                New England Aster                              3

Astragalus canadensis                           Canada Milkvetch                                9 (T)

Baptisia lactaea                                     White False Indigo                                10 (T)

Ceanothus americanus                          New Jersey Tea                                   8

Coreopsis lanceolata                             Lance-leaved Coreopsis                       8

Dalea candida                                       White Prairie Clover                             8

Dalea purpurea                                     Purple Prairie Clover                            10

Helianthus occidentalis                          Western Sunflower                               8

Heliopsis helianthoides                          Ox-eye Sunflower                                5

Liatris aspera                                        Rough Blazing Star                               4

Lupinus perennis                                   Lupine                                                  7

Monarda punctata                                Dotted Mint                                          4

Rudbeckia hirta                                    Black-eyed Susan                                 1

Solidago rigida                                      Stiff Goldenrod                                     5

Zizia aptera                                           Golden Alexander                                 9 (T)

Andropogon gerardi                             Big Bluestem Grass                               5

 

 

Botanical Name                                    Common Name                                    C value

 

 

Schizachyrium scoparium                      Little Bluestem Grass                            5

Sorghastrum nutans                               Indian Grass                                         6

Sporobolus heterolepis                         Prairie Dropseed Grass                         10 (T)

                                                                                                    ∑ C =         136

                                 Mean C = ∑ C ∕ number of plants

                                 Mean C = 136 ∕ 22 = 6.18 (example A)

 

Notice how the Mean C goes down when the Switchgrass that is volunteering in the prairie is included in the equation…

Panicum virgatum                                  Switchgrass                                          4

                                                

                                                                                                      ∑ C =       140

 

                                  Mean C = 140 ∕ 23 = 6.08 (example B)

 

…and increases if highly conservative plants are added

Silphium integrifolium                            Rosinweed                                           10 (T)

Silphium laciniatum                                Compass Plant                                     9 (T)

Silphium perfoliatum                              Cup Plant                                             10 (T)

Eryngium yuccifolium                            Rattlesnake Master                               10 (T)

                                                                                                        ∑ C =      179

                                    Mean C = 179 ∕ 27 = 6.62 (example C)

 

            It should be noted than the Mean C values given above are POTENTIAL values. These statistics will be valid only if all the plants survive and are recorded during a monitoring session. 

            Computing the Mean C for an area is the start of calculating the next and most important statistic, the Floristic Quality Index (FQI).  The FQI is obtained by multiplying the Mean C by the square root of the total number of species recorded:

                                       FQI = Mean C  ∙ √n

           

            If we substitute the values for C and n in examples A, B and C, above, we obtain the following POTENTIAL FQI values:

A:  FQI = 6.18 ∙ √22 = 28.9    

B:  FQI = 6.08 ∙ √23 = 29.15 and

C:  FQI = 6.62 ∙ √27 = 34.4

 

           
As management (prescribed fire, planting) takes place the Mean C and Floristic Quality Index will reflect the extent to which conservative species are being recruited and floristic quality of the site is improving.  Original prairies and oak savannas were highly diverse with 20 native species per ¼ meter² and a FQI of 20 or higher.  Compare this to a low quality restoration with 5 or fewer native species per ¼ meter squared and a Floristic Quality Index between 2 and 5.

           
It can easily be seen how Floristic Quality Assessment can enable the restorationist to reduce a complex pattern of change to a few key statistics.  Floristic quality can now be quantified. The addition of plants such as the Compass Plant and the Rattlesnake Master can increase the C value and corresponding Floristic Quality Index.

 

 

 (Above)

 Compass Plant in August

 

 

(Above) Rattlesnake Master

 

 Recommendations:

• Monitoring and site observations should begin as early as spring 2003 and be conducted 3-4 times per year.  “It is professionally unacceptable to conduct no prefire or postfire monitoring or site observation.”(Fire Effects Guide, Chap.X)
• WMU students, especially those enrolled in ENVS 401, ‘Ecological Restoration’ in fall 2003, should be trained to perform surveys for Relative Cover, Relative Frequency and Relative Importance Value.  RIV for weeds versus seeded plants, as determined in Site Fire Management Goals, will be extremely helpful
• WMU faculty botanists and plant taxonomists should conduct surveys using Floristic Quality Assessment techniques at least twice a year while the prairie/savanna is establishing and at least once every 2 years thereafter.  Funds should be available through the Environmental Institute.  Species recruitment, such as that provided in the following table, should be provided yearly.

 

 

• All ecological data that is gathered should be coded so it can be used in computer programs like Exel, DBASE, Foxpro and Access.  “No ecological data ever suffered by coding.” (Krebs)  Statistical analysis programs for PC’s include SAS, SYSTAT, JMP and NCSS.        

 

 

Designing a Monitoring Program for Birds

 

             Bird species that inhabit a grassland or prairie are a good indicator of the quality and success of the management.  The richer and more diverse the habitat is, the more diverse will be the fauna, including birds.  Unfortunately, estimating the numbers of grassland birds, many of which are small and not brightly colored, can be difficult.  Bias in sampling can easily be introduced since those that sing loudest, may not be the most abundant.

 

 

The range of point-counts can be extended through the use

of spotting scopes.  Photos are taken to document a species presence.

 

           

           Spot-mapping, in which individual territories are noted on scale maps, is the most accurate method of monitoring, but also the most-time consuming.  This technique involves tracking an individual bird or breeding pair through their daily routines and recording their travels and activities.

            Point-counts are another method of monitoring species abundance.  In this technique an observer stands in a fixed location or station for a designated period of time, typically 20 minutes, and counts all birds seen or heard.  This is a good way to get quantitative data.  Locations of the stations may be selected randomly using the transect method described in Monitoring for Plants, or may target the habitat of a species of particular interest.

 

Recommendations:

• Birds should be monitored every 2-3 years while the habitat is developing and at least every 5 years afterward when the habitat is more stable.
• If WMU faculty or staff expertise is lacking, managers should approach local organizations and clubs such as the Audubon Society, and the Kalamazoo Nature Center to conduct monitoring.
• It is interesting to note that one bird species, the horned lark, was seen in the prairie reconstruction in 2002, but was not noted in the bird census conducted by the Kalamazoo Nature Center in 2001.  We’re doing something right!

 

 

A female Horned Lark feeds her chicks.  Horned Larks prefer the habitat provided by

Tallgrass prairies and inhabit burned areas almost immediately.

 

Designing a Monitoring Program for Insects:

 

            Insects are much more easily eliminated from prairie remnants and reconstructions than are plants.  Insects must complete their life cycle in one season or they perish.  There is no “seed bank” upon which an insect species can rely as is the case with plants.  Many insects are termed Remnant Reliant.  While Remnant Independent species commonly occur in agricultural fields and open lands, Remnant Reliant species are found only in prairie/savanna remnants or high-quality prairie reconstructions.

 

 

 

The Karner Blue butterfly is an endangered, Remnant Reliant

species in Michigan

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There are many techniques in use to monitor insects, including butterflies.  A sampling of these is given below:

• Species surveys: which are present or absent
• Censuses: these detect changes in population size
• Mark and recapture: extrapolate total population from the number recaptured
• Annual census: species abundance from year to year
• Count circles: record all insects that appear within randomly placed circles that have a given radius
• Transects: may be randomly selected or permanently marked

             Some authors recommend monitoring butterflies and skippers as a way of monitoring general insect population health and diversity.  Nearly 30% of all butterflies and skippers are Remnant Reliant and monitoring for them can be used as a model for insects in general.

            Monitoring insect diversity by using butterflies has several advantages.  Butterflies are conspicuous and easily seen and identified.  Since there are only approximately 100 species in Illinois

and Michigan, the group is small enough to be learned in a season or two by non-professionals.  Butterflies can be monitored without a lot of expensive equipment and with a minimal impact to the Preserve.  Because of their beauty, there is an enormous body of readily available literature and they are extremely popular with the public.  Their popularity would make it easier to recruit volunteers to monitor populations.

            The Butterfly Monitoring Network is a very active volunteer group in Illinois

that works year-round.  In the off-season, volunteers prepare and present slide shows, presentations and conduct other educational activities to recruit new members.  During the growing season they actively monitor preserves and other natural areas.  Volunteers are not expected to gather complex data or be expert in the field.  Beginners are welcome and if a species is not recognized, it is simply recorded as ‘unidentified.’  This type of network has great potential here at WMU and in Southwest Michigan.

 

 

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