Snowbanks to Grassbanks

Progress report for SW19-907

Project Type: Research and Education
Funds awarded in 2019: $349,709.98
Projected End Date: 05/31/2023
Host Institution Award ID: 4W7748 & 4W8243
Grant Recipients: MSU- Animal & Range Sciences; University of Montana Western; The Nature Conservancy; US Fish and Wildlife Service
Region: Western
State: Montana
Principal Investigator:
Dr. Bok Sowell
MSU- Animal & Range Sciences
Co-Investigators:
Dr. Andrea Litt
Department of Ecology, Montana State University
Megan Van Emon
Montana State University
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Project Information

Summary:

High-elevation sagebrush rangelands in southwest Montana are used by livestock producers for summer grazing.  These areas also provide habitat for many wildlife species, including sage-grouse hens rearing broods.  The main source of water for these mesic (high moisture) sites are large snowbanks.  As they melt, snowbanks provide water to first order streams and promote green vegetation, if the release is gradual.  Several studies have documented decreases in the size of these snowbanks and predicted they will continue to decrease in size and melt earlier in the year as a result of climate change.  To slow the release of water from these snowbanks during the summer grazing season, several ranchers and conservation organizations have installed water slowing and spreading structures.  These structures aim to 1) convert the water stored in snowbanks into increased soil moisture that will prolong water release and 2) increase plant production to the benefit of livestock and wildlife.  The purpose of this project is to evaluate the effectiveness of these structures by comparing treated areas to untreated control areas, evaluating 1) cattle use, 2) vegetative changes, and 3) arthropod responses related to sage-grouse brood rearing habitat.  We do not know of another community project like this in the western United States. This unique effort should promote the sustainability of the grazing livestock industry, improve wildlife habitat and promote land stewardship.  Results from this study will be shared with other ranchers, scientists and conservationists.  This is a 3 year study.

Project Objectives:

The objectives of this proposal are to compare areas below snowbanks with water slowing/spreading devices to areas without these structures to answer the following questions:

  1. Do cattle use treated areas more than control areas without any structures?
  2. Do water slowers/spreaders improve herbaceous cover and plant production more than similar control sites without any structures?
  3. Do water slowers/spreaders increase known greater sage-grouse chick food resources (arthropods and vegetation) compared to similar control sites without any structures?
Timeline:

Year 1

April                Hire crew members and two graduate students; prepare for field season. 

                         Identify 30 treated areas and 30 control site areas to be sampled for objectives 1, 

                         2, & 3.

 

June                 Begin cattle observations for objective 1 and sample arthropods for objective 3.

 

July                  Continue cattle observations for objective 1, continue arthropod sampling for

                          objective 3, and sample plant production for objective 2.

 

August            Complete field work for all objectives.

 

Sept.–Oct.       Field students summarize data.

                           Sort and identify arthropods, Data entry and analysis.

 

Year 2   

April                Hire and organize field crew, prepare for field season.

 

June                 Begin cattle observations and arthropod sampling for objectives 1 & 3.

 

July                  Continue cattle observations for objective 1, continue arthropod sampling for

                          objective 3, and begin plant production sampling for objective 2.

 

August            Complete field work for all objectives.

                        On-Site field day

 

Sept-Oct.         Field students summarize and analyze data.

                          Sort and identify arthropods

 

Nov – March   Graduate students and advisors analyze data.

 

 

Year 3   

April                Hire and organize field crew, prepare for field season.

 

June                 Begin cattle observations for objective 1 and arthropod sampling for objective 3.

 

July                  Continue cattle observations for objective 1, begin plant production sampling for

                          objective 2, and compile data for objective 3.

 

August            Complete field work for all objectives.

                        Host 2nd on-site field day and Town Hall meeting in Dillon, MT.

 

Sept-Oct.         Field students summarize and analyze data.

 

Nov – March   Graduate students and advisors analyze data and write results.

                        Extension video completed.

                        Two scientific manuscripts submitted for publication.

                        Extension Reports completed.

                        Submit final report to SARE

Cooperators

Click linked name(s) to expand/collapse or show everyone's info
  • Jim Berkey - Technical Advisor
  • Rulon Buhler - Producer
  • Kyle Cutting (Researcher)
  • Bryant Jones - Producer
  • Rebekah Levine (Researcher)
  • Barry McCoy - Producer

Research

Hypothesis:

The objectives of this proposal are to compare areas below snowbanks with water slowing/spreading devices to areas without these structures to answer the following questions:

  1. Do cattle use areas with water slowers/spreaders more than control areas without any structures?
    1. We began evaluating this hypothesis in 2021.
  2. Do water slowers/spreaders improve herbaceous cover and plant production more than control sites without any structures?
    1. Vegetation cover tends to increase in mesic meadows, as well as trend towards increased cover of graminoids and forbs.  We expect to see denser canopy coverage in treated sites (n = 26 reaches with slowing structures) when compared to untreated sites (n = 27 reaches without structures).
    2. Production also is likely to increase in mesic meadows.  We expect increased production in treated sites compared to control sites.  This hypothesis will be evaluated starting in 2021.
  3. Do water slowers and water spreaders increase known greater sage-grouse chick food resources (arthropods and vegetation) compared to similar control sites without any structures present?
    1. Early in summer, when most of the soils are still saturated from snow melt, we do not expect to detect differences in cover of herbaceous vegetation preferred by sage grouse chicks between treated and untreated sites.  Later in summer, we expect the water-slowing structures will hold enough soil moisture to allow succulent vegetation to persist.  Therefore, treated sites (n=26 reaches with slowing structures) will be more densely covered by herbaceous vegetation than untreated sites (n=27 reaches without structures) later in the summer. 
    2. Sage-grouse chicks have been shown to overwhelmingly select arthropods in the orders Hymenoptera (mainly ants), Coleoptera, Orthoptera, and larval Lepidoptera as food resources, such that we will focus on these 4 orders.  In treated areas, we expect biomass of Hymenoptera to decrease, Coleoptera to increase, and larval Lepidoptera to increase, relative to untreated controls.  We expect biomass of Orthoptera to differ little between treated and untreated sites.
Materials and methods:

In the summers of 2018 and 2019, field crews and producers installed water slowing and spreading structures.  We sampled 159 water slowing and and 3 water spreading structures. 

The initial selection and construction of these structures was conducted by Bryant Jones, Barry McCoy, and Rulon Buhler of our producer team, Dr. Levine of University of Montana Western, Kyle Cutting of Red Rock Lakes National Wildlife Refuge (NFWF), and Jim Berkey and Sean Claffey of The Nature Conservancy.  Half of the costs associated with the construction and subsequent monitoring came from a National Fish and Wildlife Foundation (NFWF) grant.  The other half of the funds came from matching in-kind funds from the other cooperating institutions, including the Bureau of Land Management.  These funds were used to conduct first season monitoring of soil moisture, total area, water flow and plant cover. The NFWF grant funded a graduate student working with Dr. Litt on sage-grouse habitat, but did not include monitoring of cattle responses, plant production or all of the possible sage-grouse food items.  Our request from SARE is an attempt to assess the effectiveness of these structures from a livestock perspective, evaluate effects beyond the first season post-treatment, and to complement the wildlife habitat component.

These structures are within the 1.5 million acre core sage-grouse areas of southwest Montana, in Beaverhead and Madison Counties.  This region is nestled between the High Divide and the Greater Yellowstone area.  This region is not only important to many family-run cattle grazing operations, but is also vital towards ecotourism.  Most of the structures are located in mountain big sagebrush (Artemisia tridentate spp. vaseyana) plant communities above 6,700 ft, with the majority located on private land, with the remaining structures on public lands. 

Information in this report will focus on the methods and results from the first 2 seasons post-treatment, particularly soil moisture, vegetation structure and composition, as well as possible food items for sage-grouse.  Production-related questions are still being examined.

Experimental Design

We used identical sampling methods in treated and control reaches. Treated reaches had at least three water-slowing structures, whereas control reaches had at least three locations that could have had such structures; we centered our data collection on three by two meter subplots in these locations (Figure 1).  We sampled the most upstream and downstream water-slowing structures (in treated reaches) or the location where structures would have been installed, as well as the most central structure/location.  No sampling took place within a 1.5-m buffer of each water-slowing structure (or the location where it could have occurred in control reaches) to eliminate bias associated with the disturbed ground created during construction; we placed the subplot immediately upstream of this buffer area.

During the summer of 2019, we sampled 16 reaches (8 treatment, and 8 control) in four drainages; this timing of sampling captured responses one year after treatment.  During summer 2020, we collected data in the same 16 reaches, to capture responses two years after treatment.  We also sampled 38 new reaches (18 treatment and 20 control) in three additional drainages in 2020, representing one year after treatment.  We sampled vegetation and arthropods in each reach three times (visits): once in each of the months of June, July, and August.  We chose this sampling schedule to capture the gradient of senescence throughout the summer.  During the summer of 2020, we added one additional vegetation sampling visit during the month of September. 

Vegetation cover and production tend to increase in wet meadows, as well as trend towards increased cover of graminoids and forbs (Wallestad 1975, Debinski et al. 2000).  We expected to see denser canopy coverage and increased production in treated sites when compared to untreated sites.

 

Fig 1
Figure 1. Visual representation of a reach, our experimental unit, and sampling areas including the sampling area (black hashed line), vegetation frame (gray square), and soil moisture readings (black circles) southwestern Montana.

 

Soil Moisture

We characterized soil moisture by measuring volumetric water content at a depth of 10 cm (Vegetronix VG-Meter-200, Vegetronix, Inc., Riverton, Utah).  In each subplot, we sampled soil moisture along a 10-m transect, which was perpendicular to and centered on the gully, two meters above the structure. We collected measurements at one meter increments, with one measurement taken in the center of the gully (11 total measurements per subplot). Gully widths varied and we only included soil moisture measurements that fell within the bank edges of the gully in analyses.  

Vegetation

We used a canopy coverage approach to quantify plant communities (USDA 1999).  We estimated total cover of vegetation and non-vegetation (e.g. woody debris, bare ground) within a 0.5 x 0.5-m frame randomly placed within each subplot, resulting in three estimates per reach (0-100%; Figure 1; USDA 1999).  If the frame fell in an area that was not representative of the surrounding vegetation (e.g., trampled vegetation from previous visits), we selected a new random location.  We then identified each individual plant to the lowest taxonomic level feasible and estimated percent coverage of each functional group (eg., forbs, grasses, litter; USDA 1999); the total of these estimates did not exceed 100%. 

For our vegetation analysis calculated plant species richness, Shannon-Wiener diversity index, and species evenness of all identified plants.  For our sage grouse food resources analysis, we combine plants known as important plant food resources (see Analysis).

Vegetation and Soil Moisture Data Analysis

To characterize the efficacy of the restoration structures, we analyzed variation in soil moisture, as well as vegetation cover (by functional group) and species composition. We averaged data collected within each reach.  We completed separate analyses for: 1) data collected one year after treatment (in 2019 and 2020) and 2) data collected two years after treatment (in 2020).   

We modeled each response variable as a function of treatment and visit (month) using general linear mixed models; we included a random intercept for drainage to account for repeated sampling and inherent variation among drainages.  To account for variation within each drainage, we included four additional covariates: relative distance, gully slope, bank aspect, and the width-depth ratio for each reach. Relative distance was the distance between the sampled reach and the most upstream reach in the drainage, to account for spatial trends.  Gully slope was the average incline of sampled areas within each reach, to adjust for lower water infiltration and drier conditions with steeper slopes (Famiglietti et al. 1998; Moore et al. 1988; Nyberg 1996; Hills & Reynolds 1969).  To accommodate variation in how quickly soils dry after a period of moisture recharge (Reid 1973; Famiglietti et al. 1998), we averaged the bank aspect (river left) of sampled areas within each reach.  Finally, we computed the width-depth ratio, dividing the width of the gully by the depth, averaged for all sampled areas in the reach, to account for changes in shape leading to changes in soil saturation (Zheng et al. 2006).  Larger values of this ratio denote wider and shallower gullies, which tend to be wetter than narrower and deeper gullies (Zheng et al. 2006).

Arthropod Sampling

To ensure we captured both ground- and vegetation-dwelling arthropods, we used a combination of vacuum (Dietrick 1961) and pitfall (Greenslade 1964) sampling to characterize arthropod communities in treatment and control reaches.  We combined samples from each of the methods collected within a reach during the same visit. 

              Vacuum sampling - To capture flying and vegetation-dwelling arthropods, we used a vacuum/blower (Stihl SH56C, Stihl, Inc., Waiblingen, Germany) with window screen attached to the end (Davis et al. 2014).  One vacuum sample was collected at a random location within each sampling plot, for a total of three for each experimental unit.  At the selected location, we placed a 0.25-m­ plastic barrel with an 800-micron screen covering on top, to prevent arthropods from escaping (Kruess & Tscharntke 2002).  We ran the vacuum for 30 seconds within that barrel at each sampling location (Davis et al. 2014; Brook et al. 2008). To reduce bias from disturbing vegetation, we ensured vacuum sampling was the first task completed after arriving at the experimental unit (Standen 2000). 

              Pitfall sampling - We used pitfall trapping to capture ground-dwelling arthropods (Greenslade 1964).  Ideally, pitfall traps should have a diameter between 6.5 and 15 cm, to allow for relatively efficient sampling without drastically increasing effort (Work et al. 2002).  With this in mind, we used 9.5 x 12-cm plastic cups (Solo Cup Company, Lake Forest, Illinois) dug deep enough to be flush with the ground (Greenslade 1964).  Additionally, we removed vegetation and debris from the area immediately surrounding the pitfall trap to allow for free movement of ground-dwelling arthropods (Greenslade 1964).  We partially filled each pitfall trap with propylene glycol (LowTox Antifreeze/Coolant, Prestone Products Corporation, Lake Forest, Illinois) to kill and preserve trapped insects (Hohbein & Conway 2018).  

We placed one pitfall trap within each sampling unit, for a total of three for each experimental unit, for one 24-hour period.  For the first visit (June), we randomly selected a location within each sampling plot.  For the second (July) and third (August) visits, we reused the pitfall hole dug during the first visit to reduce soil disturbance. 

Arthropod Processing

After collection, arthropods were cleaned of debris and stored in 90% ethanol until further processing.  We identified each collected arthropod to order.  We measured all collected arthropods from frons to the tip of the abdomen to the nearest millimeter, then converted arthropod length to estimated biomass using taxon-specific length/mass regression equations (Rogers et al. 1977; Davis et al. 2014). We computed an estimated biomass by order for each reach, combining data from the three sampled locations during one visit.  We focused known important arthropod foods (see Analysis).

Sage Grouse Analysis

We used information from the literature to characterize important arthropod and plant foods for sage grouse.  Although we aimed to find diet information from nearby areas, we used references from other locations as needed.

              Arthropod models. -  Sage grouse chicks overwhelmingly consume arthropods belonging to the orders of Hymenoptera (mainly Formicidae), Coleoptera, Orthoptera, and larval Lepidoptera (Klebenow & Gray 1968; Peterson 1970; Gregg & Crawford 2009).  We used the combined estimated biomass (in µg) for important arthropod foods in each reach for each visit as the response variable for sage grouse chicks.

              Vegetation models. - In Montana, Idaho, and Oregon, chicks primarily consume forbs belonging to the families Brassicaceae, Fabaceae, Polemoniaceae, Asteraceae, and Lilaceae in areas dominated by big sagebrush (Artemisia tridentata; Patterson 1952; Klebenow & Gray 1968; Drut, Pyle, et al. 1994; Peterson 1970; Wallestad 1975; Martin 1970).  We compared canopy coverage of known important genera within these families as the response variable in models for sage grouse chicks.

              Model structure. – We completed separate analyses for: 1) data collected one year after treatment (in 2019 and 2020) and 2) data collected two years after treatment (in 2020).  We modeled each response variable as a function of treatment (treated or control) and visit (month) using general linear mixed models: we included a random intercept for drainage to account for repeated sampling and inherent variation among drainages.  To account for variation within each drainage, we included four additional covariates: relative distance, gully slope, bank aspect, and the width-depth ratio for each reach.

Research results and discussion:

We sampled 16 reaches (8 treatment, 8 control) and 4 water spreaders (2 treatment, 2 control) 3 times during the summer of 2019, and 53 reaches (26 treatment, 27 control) and 4 water spreaders (2 treatment, 2 control) in the summer of 2020. 

Soil Moisture and Vegetation

We did not detect differences in soil moisture between treated and control reaches during any sampling period (Table 1).  We also were unable to detect differences in vegetation cover or composition (Tables 2-3).  However, despite the uncertainty around our estimates, canopy coverage often was higher in treatment reaches compared to controls two years after treatment, for all functional groups except rushes/sedges and native grasses (Figure 2).  Similarly, diversity and evenness were higher in treatment compared control reaches during some summer months (Figure 3).

Sage Grouse

We did not detect differences in important forage items for sage grouse chicks between treated and control reaches one or two years post treatment (Table 4).

Thomas Sutton is scheduled to defend his thesis focused on this project in April 2022.  Field data collection will continue in summer 2022.

Table 1. Soil volumetric water content (% saturation, means and 95% CIs) in sampled reaches, one year post-treatment (summers 2019 and 2020, n = 53, 27 control, 26 treatment) and two years post-treatment (summer 2020, n = 16, 8 control, 8 treatment), southwestern Montana.

 

 

June

July

August

September

1 year post- treatment

 

Treatment

43.6 (25.9 - 61.4)

30.6 (12.8 - 48.3)

21.8 (4.0 - 39.6)

16.3 (-2.1 - 34.8)

Control

44.2 (26.3 - 62.2)

31.2 (13.3 - 49.1)

18.0 (0.0 - 35.9)

18.0 (-0.7 - 36.8)

2 years post-treatment

Treatment

46.8 (28.6 - 65.0)

62.4 (41.9 - 82.8)

32.0 (14.0 - 50.0)

48.2 (32.5 - 64.0)

Control

31.4 (11.5 - 51.2)

60.7 (45.0 - 76.5)

29.6 (13.8 - 45.3)

48.0 (32.5 - 63.0)

 

Table 2. Canopy coverage of functional groups (% coverage, means and 95% CIs) in sampled reaches, one year post-treatment (summers 2019 and 2020, n = 53, 27 control, 26 treatment), southwestern Montana.

 

 

June

July

August

September

Total Vegetation

Treatment

64.5 (52.4 - 76.6)

72.7 (60.7 - 84.6)

69.3 (57.3 - 81.2)

70.3 (57.6 - 83.1)

Control

60.5 (48.5 - 72.4)

69.3 (57.2 - 81.4)

69.0 (56.8 - 81.1)

70.8 (58.0 - 83.6)

Total green vegetation

 

Treatment

61.8 (48.7 - 74.9)

67.8 (48.7 - 74.9)

50.6 (37.5 - 63.7)

27.5 (13.5 - 41.4)

Control

65.9 (52.6 - 79.1)

65.0 (51.7 - 78.3)

46.4 (33.1 - 59.7)

28.8 (14.7 - 42.8)

Native forbs

Treatment

20.1 (13.2 - 26.0)

20.5 (13.6 - 27.4)

21.3 (14.3 - 28.2)

18.9 (11.4 - 26.3)

Control

20.0 (13.0 - 27.1)

21.4 (14.4 - 28.4)

17.4 (10.4 - 24.4)

15.4 (7.9 - 22.9)

Green native forbs

Treatment

18.9 (12.3 - 25.5)

18.6 (12.0 - 25.2)

15.6 (9.0 - 22.2)

14.3 (7.3 - 21.4)

Control

19.1 (12.4 - 25.8)

19.8 (13.1 - 26.5)

13.5 (6.8 - 20.2)

11.5 (4.4 - 18.7)

Rushes/sedges

Treatment

5.5 (-2.4 - 13.3)

7.3 (-0.6 - 15.1)

7.3 (-0.6 - 15.1)

8.7 (-5.8 - 11.5)

Control

13.1 (5.1 - 21.1)

10.8 (2.8 - 18.8)

10.8 (2.8 - 18.8)

12.1 (4.0 - 20.1)

Green rushes/sedges

 

Treatment

6.4 (-0.3 - 13.1)

7.2 (0.4 - 13.9)

5.9 (-0.8 - 12.7)

0.1 (-0.8 - 12.7)

Control

13.3 (6.5 - 20.1)

10.3 (3.4 - 17.1)

7.3 (0.5 - 14.2)

7.3 (0.5 - 14.9)

Native grasses

Treatment

3.6 (-0.7 - 7.8)

13.6 (9.9 - 17.3)

8.4 (4.6 - 12.1)

3.6 (-0.7 - 7.8)

Control

9.6 (5.9 - 13.4)

11.4 (7.6 - 15.2)

8.3 (4.4 - 12.1)

4.7 (0.4 - 8.9)

Green native grasses

 

Treatment

5.3 (-1.4 - 11.9)

7.1 (-1.5 - 15.7)

16.8 (9.2 - 24.2)

13.7 (6.1 - 21.4)

Control

8.2 (1.5 - 14.8)

9.8 (3.1 - 16.5)

15.0 (8.3 - 21.6)

18.0 (9.8 - 26.3)

Non-native grasses

Treatment

14.1 (6.4 - 21.9)

20.6 (12.9 - 28.4)

17.6 (9.9 - 25.4)

24.4 (15.8 - 33.1)

Control

13.0 (7.4 - 21.1)

15.2 (7.3 - 23.1)

17.0 (9.0 - 24.9)

26.7 (18.0 - 35.4)

Green non-native grasses

Treatment

14.9 (8.2 - 21.6)

19.1 (12.4 - 25.8)

12.9 (6.1 - 19.6)

4.9 (-2.7 - 12.4)

Control

14.3 (7.4 - 21.1)

14.4 (7.5 - 21.3)

9.9 (3.0 - 16.9)

5.0 (-2.6 - 12.6)

Non-native forbs

Treatment

8.6 (4.6 - 12.6)

6.8 (2.9 - 10.8)

3.6 (-0.4 - 7.6)

0.3 (-4.0 - 4.6)

Control

5.3 (1.2 - 9.3)

5.9 (1.9 - 10.0)

3.9 (-0.17 - 7.9)

0.6 (-3.8 - 4.9)

Green non-native forbs

Treatment

8.9 (5.2 - 12.7)

5.5 (1.7 - 9.2)

1.8 (-2.0 - 5.6)

-0.1 (-4.1 - 4.0)

Control

5.8 (2.0 - 9.6)

5.2 (1.4 - 9.1)

2.9 (-0.9 - 6.7)

0.2 (-3.9 - 4.3)

Table 3. Species richness, Shannon-Weiner diversity, and species evenness (means and 95% CIs) in sampled reaches, one year post-treatment (summers 2019 and 2020, n = 53, 27 control, 26 treatment), southwestern Montana.

 

 

June

July

August

September

Richness

Treatment

21.3 (15.8 - 26.9)

21.8 (16.3 - 27.4)

18.8 (13.2 - 24.3)

17.1 (11.3 - 22.9)

Control

22.8 (17.2 - 28.4)

24.4 (18.8 - 30.0)

20.0 (14.4 - 25.6)

18.3 (12.5 - 24.1)

Diversity

Treatment

1.46 (1.07 - 1.85)

1.54 (1.15 - 1.93)

1.47 (1.08 - 1.85)

1.40 (0.99 - 1.81)

Control

1.62 (1.23 - 2.01)

1.76 (1.37 - 2.16)

1.70 (1.31 - 2.09)

1.62 (1.21 - 2.03)

Evenness

Treatment

45.9 (36.3 - 55.4)

48.8 (39.3 - 58.4)

49.7 (40.1 - 59.2)

51.9 (41.8 - 62.1)

Control

51.9 (42.3 - 61.6)

55.2 (45.5 - 64.8)

57.9 (48.2 - 67.6)

59.6 (49.3 - 69.8)

Table 4. Means (and 95% CIs) from models assessing important arthropod (biomass in µg) and plant foods (percent canopy coverage) for sage-grouse in sampled reaches, one year post-treatment (summers 2019 and 2020, n = 53, 27 control, 26 treatment) and 2 years post-treatment (summer 2020, n = 16, 8 control, 8 treatment), southwestern Montana.  We sampled plants in June, July, August, and September, and arthropods in June, July, and August.

 

 

 

June

July

August

September

1 year post-treatment

 

Arthropods

 

Treatment

40.6 (-89.8 - 171.0)

123.7 (6.3 - 241.1)

565.0 (445.2 - 684.8)

 

Control

111.1 (-18.0 - 240.2)

186.5 (60.2 - 312.8)

470.0 (343.6 - 596.3)

 

 

Plants

 

Treatment

6.6 (4.5 - 8.8)

6.5 (4.3 - 8.7)

8.5 (6.3 - 10.8)

8.1 (5.1 - 11.0)

 

Control

6.5 (4.2 - 8.7)

7.1 (4.9 - 9.3)

7.3 (4.9 - 9.6)

7.5 (4.3 - 10.6)

2 years post-treatment

Arthropods

 

Treatment

48.1 (-111.0 - 207.2)

123.0 (-86.9 - 332.9)

200.7 (16.4 - 385.1)

 

Control

86.2 (-73.6 - 246.0)

110.7 (-49.2 - 270.5)

501.8 (342.0 - 661.6)

 

 

Plants

Treatment

10.0 (7.0 - 13.0)

6.0 (2.1 - 9.9)

6.7 (3.3 - 10.2)

3.8 (-0.53 - 8.1)

 

 

Control

6.0 (2.7 - 9.2)

6.7 (3.5 - 10.0)

7.8 (4.6 - 11.0)

6.9 (2.6 - 11.2)

Figure 1

Figure 2. Canopy cover of each functional group (means and 95% CIs) in treatment (black) and control (white) reaches, two years post-treatment (summer 2020, n = 16, 8 control, 8 treatment), southwestern Montana.

 

Figure 2

Figure 3. Shannon-Weiner diversity, species richness, and species evenness (means and 95% CIs) in treatment (black) and control (white) reaches, two years post-treatment (summer 2020, n = 16, 8 control, 8 treatment), southwestern Montana.

 

2020

All of our research reaches were rested from grazing, so no observations were made.

 

2021

It became apparent that the pastures which housed our treatments were too large to contain the cattle enough to make any meaningful comparisons. Instead, we asked how much time cattle grazed mesic areas compared to uplands. This would provide an estimate of the importance of mesic areas in each pasture.

                Tributary:                            Little Basin and Clover

                Number of head:              400 cow-calf pairs

                Dates:                                   July 6-Aug 29

 

               Tributary:                            Keystone

                Number of head:              50 cow-calf pairs

                Dates:                                   July 1 – Sept 1

 

                Tributary:                            Little Basin

                Number of head:              50 cow-calf pairs

                Dates:                                   July 6 – August 31

 

                Total Observation Time:                85 hours

 

Cattle spent 60% of their grazing time in mesic areas compared to uplands when averaged across all locations.

 

2022

                Tributary:                            Little Basin and Clover

                Number of head:              30 cow-calf pairs

                Dates:                                   August 1 – Sept 15

 

               Tributary:                            Keystone

                Number of head:              80 cow-calf pairs

                Dates:                                   July 14 – August 23

 

                Tributary:                            Little Basin 24

                Number of head:              30 cow-calf pairs

                Dates:                                   July 5 – September 1

 

                Total Observation Time:                35 hours

 

Cattle spent 61% of their time in mesic areas compared to upland areas while grazing.

PRODUCER PARTICIPATION

Our three producers selected the sites and helped construct the dams prior to the evaluations.

 

 

Research conclusions:

Discussion

Vegetation regeneration after a disturbance event (e.g., fire, mining) can vary greatly based on the type of disturbance, soil properties, habitat type, or climate (Greipsson 2011); the same is true for restoration efforts.  Contrary to Silverman et al. (2019), we did not detect differences in vegetation characteristics immediately after treatment; several factors may have contributed to these disparate findings.  First, low-tech restoration structures need flowing water to be effective (Zeedyk & Clothier 2009), such that differences in climate could lead to variation in results.  Our study area receives more annual precipitation (50 cm) than the Gunnison Basin (27 cm), the location of Silverman et al. (2019), but the average annual temperatures are much lower (1.6°C in Red Rock Lakes and 3.1°C in the Gunnison Basin; USFWS 2009, Aldridge et al. 2012).  Second, timing of installation also may influence results.  In the Gunnison Basin, restoration structures were installed between July and October (TNC & GCWG 2017), whereas structures were installed in our study area in October, when freezing temperatures become more common (USFWS 2009).  In colder climates and areas with longer periods of freezing temperatures, plants likely grow more slowly (Li et al. 2008; Tonin et al. 2019).  Therefore, local environmental conditions may be an important influence on the timing of the effectiveness of low-tech wet meadow restoration.

We also used different metrics to assess the efficacy of treatments compared to Silverman et al. (2019); they focused on comparing plant productivity based on Normalized Difference Vegetation Index (NDVI), whereas we used plant canopy coverages, collected in the field.  NDVI uses satellites to measure the amount of near-infrared light reflected by green leaves, and is used as an index of plant productivity (Sellers et al. 1992). Although plant canopy coverage and productivity are likely related, canopy coverage simply measures the amount of aerial cover of a plant within a specified area (Daubenmire 1959).  Further, these metrics typically represent very different spatial scales; Silverman et al. (2019) measured vegetation from the air within a 30-m grid cell, whereas our measurements were collected on the ground, in small plots at the field site.  Influences of these restoration structures occur at a small scale, which would seem to require fine-scale measurements, although measuring biomass may have better captured possible vegetation changes.

Although we expected initial treatment effects to be most pronounced for soil moisture, this was not the case.  We measured moisture instantaneously at a depth of 10 cm; these methods were most feasible in terms of time and cost.  Soil moisture at that depth can vary greatly during the summer due to changes in air temperature and humidity (Ajmal et al. 2016). Detecting meaningful changes in soil moisture could require measuring moisture at 20-cm or deeper (Ajmal et al. 2016) or using continuous soil moisture loggers. 

Many of the plant species that provide important forage for our birds of interest also may provide habitat for arthropods.  Although arthropods have short generation times and can to respond rapidly to ecological changes, some degree of a time lag is likely required between detecting changes in vegetation and subsequent changes in arthropod populations.  Continued monitoring of these areas seems warranted to detect potential changes.  We also identified arthropods at a reasonably coarse level of taxonomic resolution, which could mask varied habitat needs and responses to wet meadow restoration (Niemela et al. 1992; Xiao-Dong & Hong-Zhang 2006; Stamps et al. 2009; Borchard et al. 2013; Perez-Sanchez et al. 2018; Pinedo-Escatel & Moya-Raygoza 2018).  Identifying arthropods to family or genus could provide important insights. 

Restoration often takes time, such that it is uncommon to detect immediate effects of restoration efforts (Ton et al. 1998; Meyer et al. 2010; Hopple & Craft 2013; Jing et al. 2014; Cooper et al. 2017).  Although we had lower sample sizes to evaluate effects two years after treatment, canopy coverage often was higher in treatment reaches compared to controls. Rushes/sedges and native grasses were exceptions to this pattern, which may be due to the most common species in these groups.  Coverage of rushes and sedges was dominated by Baltic rush, a species adapted to very wet soils (Lesica et al. 2012), and our wettest reaches (i.e. reaches where we observed water running through them for most of the study period) were typically controls (T. Sutton, pers. observ.).  Further, the lower canopy coverage of native grasses in treated reaches likely resulted because most native grasses we found were more xeric-adapted species, namely Idaho fescue (Festuca idahoensis). 

We also observed some qualitative evidence that these structures are beginning to positively effect wet meadows.  In our wettest drainages (e.g., Little Basin 1 and Little Basin 2), we observed structures slowing water and sediment deposits, when compared to control reaches (Figure 4).  With additional time, restoration may improve the function of these wet meadows in more measurable ways, allowing them to retain nutritious vegetation late in the growing season, in the face a dwindling snowpack. 

Fig 3
Figure 4. Water being slowed at a one-rock dam, Little Basin 1, July 2019, southwestern Montana. Photo by Thomas Sutton

Conclusions

Low-tech solutions are a relatively new method of wet meadow restoration (Zeedyk & Clothier 2009) and continued monitoring is crucial to developing our understanding.  Comparing findings from the growing number of studies focused on these tools also will help us to understand the factors that may alter efficacy and the time needed to detect effects.  Timing of installation and the local environmental conditions may be important influences on the effectiveness of low-tech wet meadow restoration and variation in the time needed to detect effects.

We also are still learning which methods are most appropriate for post-treatment monitoring; our findings provide some new insights.  For vegetation, we recommend exploring changes in vegetation biomass, as an alternative response metric.  To understand changes in arthropods (as food resources or as another community of interest), focusing on finer taxonomic resolution (e.g., family, genus) could minimize masking responses of disparate groups. 

Compared to other restoration methods (Zeedyk & Clothier 2009), the low cost and relative speed of installation make a compelling case for low-tech solutions in wet meadows.  If these low-tech solutions do not provide a “cure-all”, there is a continued need to find effective and practical tools feasible for widespread use.  Climate change, overgrazing, and habitat conversion (Knight et al. 2014), combined with the importance of wet meadows to many plant and wildlife species, suggest an “all hands on deck” approach is needed to restore wet meadows in arid landscapes and improve habitat for sagebrush-associated species (Silverman et al. 2019).

Participation Summary
3 Producers participating in research

Research Outcomes

2 Grants received that built upon this project
2 New working collaborations

Education and Outreach

Participation Summary:

3 Farmers participated
Education and outreach methods and analyses:

None to report at this time.

Field work has not been completed.

3 Farmers intend/plan to change their practice(s)
3 Farmers changed or adopted a practice

Education and Outreach Outcomes

Recommendations for education and outreach:

None to report at this time.

Field work has not been completed.

3 Producers reported gaining knowledge, attitude, skills and/or awareness as a result of the project
Key areas taught:
  • Knowledge of Riparian Restoration: After our interaction or field day, we hope that producers have some knowledge of the overall goal of the project. They should be able to remember material we present, including the long-term predictions of snowfall and runoff in this area, as well as describe and recognize predictions for abiotic factors that will influence the production of beef cattle in this valley for the next 25-50 years. They should also be able to describe and recognize the role of small, water-slowing structures to offset these changes.
  • Application of Riparian Restoration: We are most interested in the application of this information and how willing producers would be to adopt our methods and apply our findings on their property. We want to assess their willingness to adopt or modify their current management practices to include some water-slowing structures to increase soil moisture to benefit livestock and wildlife.
Key changes:
  • Riparian Restoration

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.