Ecological Monitoring Network

image of a quadrat plot and a plant ecologist assessing plant species abundance

Ecological Monitoring Network

Improving land use decision-making and sustainable resource management through greater reliance on scientific knowledge

The Minnesota Department of Natural Resources established the Ecological Monitoring Network in 2017 to track ecological change throughout the state. We will provide data on how the state’s native plant communities are changing in the face of new challenges, such as climate change, invasive species and increasing habitat fragmentation. This effort is being led by the Minnesota Biological Survey, in collaboration with other DNR divisions and partners such as The Nature Conservancy, the University of Minnesota, and the U.S. Fish and Wildlife Service.


Why Monitor?

Minnesota’s native grasslands, wetlands, and forests provide recreation, timber, water filtration, habitat for wildlife and pollinators, flood protection, carbon storage and other valuable ecosystem services to Minnesotans. These services are threatened by direct and indirect stressors, such as changes in climate and management, increases in non-native invasive species and pollution, and increased pressure on land and water use.

ecological monitoring in near Pembina, MNEcological monitoring in near Pembina, MN

Bees and other insect pollinators are also facing similar environmental challenges, in addition to habitat loss and degradation and population declines related to parasites and disease. Pollinators are vital to maintaining the diversity and reproduction of flowering plants, which are essential components of grasslands, wetlands and forests. There is currently no comprehensive statewide monitoring network that consistently measures and evaluates changes in the vegetation that comprises native grasslands, wetlands, and forests. Without such information, it will be increasingly difficult to detect which factors are driving environmental changes


Goals

University, federal, and state scientists met to define specific goals for how the EMN will compliment other efforts, further scientific knowledge through long term monitoring and deliver results to stakeholders.

ecological monitoring in near Kertsonville, MN Ecological monitoring near Kertsonville, MN
  • Create a statewide vegetation monitoring network.
  • Provide information on the status and trends in structure, composition, and condition of native grasslands, wetlands, and forests. 
  • Design a scientifically rigorous monitoring approach that is fiscally responsible.
  • Provide information to managers and others in a timely manner so that inferences can be made about ecosystem health as a result of stressors.
  • Aid decision making by natural resource managers, legislators, local units of government, conservation organizations, and land owners to improve conservation, management, policy and land-use decisions.
  • Complement existing long-term monitoring projects in grasslands, wetlands and forests that span several agencies and organizations.
  • Design a monitoring network that can be used for research by ecologists, wildlife biologists, entomologists, and other scientists.   
  • Collect a baseline survey of selected groups of pollinating insect species occurring in targeted vegetation types and use this information to inform future monitoring of pollinators related to vegetation.

Objectives

Objectives are essential to research and analysis. They help determine specific metrics (i.e., deer browse pressure, plant species abundance, water conductivity, canopy cover) to quantify for analyses. The objectives below were defined in 2017 and are the foundation for all future EMN research. Preliminary analyses (where available) are linked to specific objectives.

Vegetation

Landscape Context

  • Determine relationships between landscape context (e.g., size of surrounding natural area and proximity of anthropogenic land use) and changes in native grassland, wetland, and forest vegetation.

Soils

Hydrology

  • Assess hydrology and its relationships to trends in wetland vegetation.
    • Document long-term changes in hydrology in select sites that represent a spectrum of wetland types.
    • Assess status and trends of pH in wetland vegetation.

Pollinators and Other Wildlife

  • Collect baseline surveys of select groups of pollinating insect species occurring in targeted vegetation types.
  • Document high priority vegetation characteristics related to wildlife habitat (e.g. snags and depth of leaf litter).

Pests and Pathogens

  • Assess the extent and degree of known pest and pathogen outbreaks.  

Field Methods

Data are collected along three 45-meter parallel transects. Woody plants in the tree canopy and subcanopy layer are sampled in a 45-by 10-meter subplot centered along each transect. Woody plants and vines in the shrub layer, and groundlayer plants, are sampled in 24, 1-meter² quadrats (includes a small nested plot) placed every 5 meters along each transect.

Depending on the habitat, various other components are added that are not shown, such as deer browse and coarse woody debris metrics, water chemistry or measurements of grassland structure. A few of the elements of this design are subject to change as we continue to refine our metrics to best capture the data.


Ecological Monitoring Network Update (July, 2024)

The tables and figures below summarize Ecological Monitoring Network (EMN) progress and patterns in the data being collected, as plots are installed and surveyed across Minnesota. Regular resurveys in the future will document long-term change, or stability, in vegetation in the monitoring plots. The patterns highlighted below relate to several of EMN’s basic objectives: for example tracking trends in invasive species and detecting change in measures of forest health. These highlights represent just a small fraction of the information and patterns that can be extracted from data that will be collected over time at EMN plots.

Summary

  • EMN has established and surveyed 387 plots, from the beginning of the project in 2017 through the 2023 field season.
  • We plan to install and collect data from 500-550 plots in total to monitor change in Minnesota’s native forest, prairie and wetland vegetation.
  • 45% of the plots established so far are in upland forests, 23% in open wetlands, 15% in forested wetlands, 12% in upland prairies and 5% in wetland prairies.
  • 42% of plots are on land managed by the state of Minnesota (such as wildlife management areas, scientific and natural areas, and state forests), 20% on federally managed lands, 18% on privately owned lands, and 17% on lands managed by local governments (such as county parks, city parks, and tax forfeited land).

Figure 1. Map of 387 installed EMN monitoring plots through 2023.

Plot ID Land Manager County
99003 FOR Olmsted
99004 FOR Olmsted
99002 FOR Olmsted
127 SNF St. Louis
3604 FOR Houston
2560 Private Rock
752 Private Lyon
713 WMA Kanabec
3271 WMA Big Stone
1763 WPA Grant
1967 FOR Koochiching
1869 County Hennepin
1074 County St. Louis
1693 city Anoka
4436 Private Olmsted
3928 County Scott
857 FOR Pine
673 SNF Lake
1172 Private Wabasha
994 SNF St. Louis
2653 County Hennepin
1236 WMA Olmsted
3414 FOR Cass
59 SNF Cook
1015 FOR Lake of the Woods
561 SNF Lake
585 Private Kanabec
870 County Cass
473 Private Stearns
4968 Private Blue Earth
422 CNF Cass
319 County Koochiching
171 WMA Roseau
658 CNF Itasca
734 County Hubbard
335 bwca Lake
5584 County Jackson
166 County Cass
378 WMA Polk
261 MN Power St. Louis
975 FOR Koochiching
180 Private Fillmore
721 SNF St. Louis
799 FOR Lake of the Woods
851 PAT Douglas
1223 Private Big Stone
3290 TNC Wilkin
1124 PAT Goodhue
387 WPA Swift
187 SNF Lake
2088 WMA Le Sueur
3305 WPA Renville
270 sna Polk
1003 SNF Cook
1511 nwr Marshall
1925 PAT Carlton
278 FOR St. Louis
99 WPA Douglas
3107 Private Pope
905 County Aitkin
956 Private Murray
14 Private Norman
575 FOR Koochiching
324 FOR St. Louis
277 FOR Pine
2300 TNC Lincoln
9524 FOR Olmsted
5480 ama Faribault
230 County Crow Wing
417 County Lake
1586 County St. Louis
82 CNF Cass
704 Private Pipestone
1652 Private Houston
571 SNF Cook
2051 Private Kandiyohi
421 FOR Morrison
155 WMA Pennington
49 bwca Lake
1935 FOR Koochiching
8456 Private Dodge
429 County Aitkin
1574 County Hubbard
8124 WMA Pipestone
305 SNF Lake
1689 WMA Chisago
951 WMA Marshall
1187 Private Pope
650 County Hubbard
6925 sna Hennepin
1499 Private Marshall
949 PAT Pine
659 WMA Douglas
482 SNF St. Louis
625 SNF Lake
223 FOR Koochiching
198 FOR St. Louis
893 County St. Louis
2730 TNC Clay
2840 sna Rice
1041 County St. Louis
2068 sna Houston
239 FOR Koochiching
36 WMA Olmsted
4340 Private Dodge
1365 County Pine
38 FOR Hubbard
1591 WMA Marshall
136 WMA Dakota
1085 County Carlton
2509 County Washington
2768 WMA Cottonwood
1192 WMA Blue Earth
2 FOR Aitkin
352 Private Murray
898 WMA Itasca
1246 County Beltrami
427 sna Kittson
79 bwca Lake
1544 PAT Steele
646 County Itasca
111 PAT Beltrami
1479 nwr Otter Tail
1747 WPA Stevens
7565 city Hennepin
1070 County Clearwater
773 County St. Louis
1250 County Beltrami
3252 FOR Fillmore
110 WPA Polk
2812 Private Pipestone
122 Private Polk
1434 WPA Otter Tail
536 WMA Rice
262 meriwether Koochiching
921 WMA Kanabec
456 Private Goodhue
978 County Hubbard
1786 sna Norman
685 County Itasca
210 FOR Cass
57 WMA Wright
1133 WMA Aitkin
22 FOR St. Louis
416 Private Redwood
6176 sna Jackson
546 CNF Cass
1514 WMA Clay
637 FOR St. Louis
226 County Beltrami
358 County Cass
302 Private Polk
568 WMA Martin
706 CNF Itasca
169 public waters Meeker
609 SNF St. Louis
877 CNF Cass
481 SNF Lake
139 WMA Marshall
1581 Private Chisago
929 SNF Cook
109 FOR Aitkin
1457 SNF Cook
212 County Olmsted
69 FOR St. Louis
63 FOR Koochiching
934 County Crow Wing
1706 nwr Clay
5004 Private Blue Earth
2010 sna Clay
2057 County Aitkin
2282 WPA Becker
955 SNF St. Louis
763 SNF St. Louis
470 CNF Cass
450 CNF Itasca
406 WMA Cass
4211 WMA Lac qui Parle
2567 sna Big Stone
400 Private Jackson
486 FOR Cass
1488 Private Jackson
2015 FOR Koochiching
3379 WPA Otter Tail
55 Private Marshall
3301 FOR Crow Wing
833 SNF Lake
284 Private Blue Earth
1820 WMA Le Sueur
1140 Private Fillmore
1417 PAT Mille Lacs
636 Private Lincoln
693 sna Benton
664 Private Carver
85 County Pine
4061 PAT Chisago
458 County Becker
4210 Private Waseca
1271 WMA Marshall
941 County Aitkin
5224 WMA Faribault
17 County St. Louis
413 Private Isanti
33 PAT St. Louis
133 FOR St. Louis
495 County Koochiching
2355 WPA Otter Tail
246 WMA Becker
3584 nwr Rock
11 FOR Roseau
338 FOR Itasca
1091 WPA Pope
113 Private Lake
66 WMA Aitkin
257 PAT Cook
548 Private Wabasha
1325 university Isanti
423 FOR Beltrami
765 County St. Louis
1805 Private Hennepin
242 WMA Itasca
1242 TNC Wilkin
1044 Private Houston
709 WMA Pine
545 County St. Louis
2399 FOR Lake of the Woods
726 FOR Cass
1121 SNF Lake
46 FOR Clearwater
1442 CNF Beltrami
3128 WMA Martin
5252 FOR Lake of the Woods
434 FOR Itasca
159 FOR Lake of the Woods
478 County Hubbard
1856 Private Renville
701 FOR Aitkin
466 FOR Hubbard
1062 FOR Hubbard
494 Private Yellow Medicine
731 Private Pennington
3587 Private Kandiyohi
2371 WMA Swift
983 WMA Roseau
947 nwr Lac qui Parle
1167 FOR Koochiching
2144 WMA Lyon
591 SNF St. Louis
3513 TNC McLeod
480 sna Brown
401 SNF St. Louis
443 SNF St. Louis
542 Private Mahnomen
1352 PAT Rice
847 FOR Koochiching
318 PAT Chippewa
1562 PAT Otter Tail
3652 WMA Lake of the Woods
145 SNF St. Louis
3888 Private Brown
255 FOR Koochiching
1161 FOR Mille Lacs
2752 Private Pipestone
50201 nwr Becker
267 WMA Lake of the Woods
5132 PAT Freeborn
498 CNF Itasca
733 WMA Anoka
383 FOR Koochiching
94 WMA Mahnomen
397 County Carver
1626 PAT Otter Tail
862 County Becker
390 County St. Louis
273 County St. Louis
202 County Becker
786 CNF Cass
523 WMA Roseau
1229 County Ramsey
108 WMA Brown
550 FOR Wadena
497 County St. Louis
1411 WMA Swift
578 WMA Aitkin
177 SNF Cook
1988 Private Winona
3411 Private Otter Tail
753 County St. Louis
518 CNF Itasca
804 FOR Wabasha
50202 nwr Becker
820 Private Fillmore
2074 FOR Becker
7689 WMA Kanabec
2951 Private Traverse
321 County Lake
1137 County Lake
514 County Aitkin
146 CNF Cass
141 city Hennepin
612 Private Goodhue
3220 sna Wabasha
2306 County Aitkin
3293 County Anoka
3357 County Dakota
418 CNF Beltrami
1210 nwr Polk
678 FOR Cass
511 FOR Koochiching
939 WMA Kittson
2269 PAT Washington
3688 WMA Faribault
5405 Private Dakota
515 WPA Kandiyohi
2598 County Hubbard
541 nwr Sherburne
565 WMA Morrison
572 wmd Yellow Medicine
1146 sna Polk
2841 WMA Isanti
689 SNF Cook
1249 SNF Cook
1026 FOR Aitkin
521 Private Kanabec
2004 WMA Winona
65 WMA Cook
926 County Clearwater
433 SNF Cook
90 Private Otter Tail
70 FOR Itasca
18 FOR Koochiching
558 sna Polk
1090 FOR Aitkin
2191 FOR Koochiching
214 CNF Cass
897 FOR Lake
4950 FOR Wadena
3123 Private Otter Tail
990 FOR Clearwater
1079 WMA Roseau
957 County Aitkin
45 PAT Chisago
31 WMA Lake of the Woods
2425 Private Stearns
186 WMA Polk
513 FOR Cook
850 FOR Cass
5917 FOR Sherburne
487 WMA Marshall
769 SNF Cook
2163 WPA Lac qui Parle
1186 CNF Beltrami
1253 Private Morrison
867 WPA Pope
30 WPA Mahnomen
683 WMA Kittson
986 WPA Wilkin
172 Private Sibley
235 TNC Kittson
2748 Private Pipestone
895 FOR St. Louis
225 SNF Cook
789 County Pine
801 SNF Lake
381 County Carlton
852 Private Wabasha
661 FOR Todd
614 CNF Cass
1314 County Hubbard
976 Private Jackson
1111 Private Wilkin
50 County Itasca
5512 WMA Dakota
48001 Private Redwood
584 Private Goodhue
1698 County Beltrami
405 Camp Ripely Morrison
4556 County Nicollet
1624 WMA Waseca
341 Private Pine
1666 FOR Itasca
747 WMA Kittson
5604 PAT Fillmore
913 SNF St. Louis
2423 WMA Marshall
48002 Private Redwood
260 Meriwether Koochiching
419 Private Pope
3498 Private Clay
7958 FOR Wadena
99000 WMA Freeborn
99001 WMA Pope

Current proportions of EMN monitoring plots

Proportion of plots by system group

 
  Plant Community System Number of Plots
Forested wetlands (15%) Acid Peatland (AP) 15
Floodplain Forest (FF) 12
Forested Peatland (FP) 20
Wet Forest (WF) 12
Open wetlands (27%) Acid Peatland (AP) 12
Forested Peatland (FPn73) 2
Marsh (MR) 4
Open Pealand (OP) 29
Wet Meadow (WM) 41
Wet Prairies (WP) 18
Upland forests (45%) Fire Dependent Forest (FD) 52
Mesic Hardwood Forest (MH) 122
Upland prairies (12%) Upland Prairie (UP) 48

Proportion of plots by land ownership

 
  Land Manager(s) Number of Plots
Federal (20%) Boundary Waters Wilderness 3
Chippewa National Forest 17
National Wildlife Refuge 9
Superior National Forest 32
Wetland Management District 1
Waterfowl Production Area 16
Local (17%) City 3
County (Parks and Tax Forfeit) 64
Other (3%) Private Companies, Universities, The Nature Conservancy, and others 12
Private (18%) Privately Owned by Individuals 69
State (42%)
(DNR Managed)
Aquatic Mangement Area 1
State Forest 67
Parks and Trails 18
Scientific and Natural Area 14
Wildlife Management Area 61

Objective: Track effects of browsing on vegetation

Heavy browsing by herbivores such as white-tailed deer can negatively impact forest vegetation. Deer eat tree seedlings and saplings and can suppress regeneration of the species that would otherwise form the future tree canopy. This can lead to shifts in forest composition and structure. Over-browsing of forests can also lead to reduced deer populations long-term and reduce other ecosystem benefits provided by healthy forests.

  • EMN evaluates the effect of deer browsing on woody vegetation less than two meters from the forest floor.
  • Browse pressure is measured as a ratio of browsed to total branches of all woody species in the plot.
  • EMN forest plots in southern Minnesota appear to be experiencing consistently higher browse pressure than plots in northern Minnesota.

Figure 4. Levels of browse pressure within individual EMN plots (ratio of browsed:total branches). Larger dot size represents higher browse pressure by deer. Click to enlarge


Relative browse pressure on canopy tree species for all forested EMN plots

Relative Browse Pressure (RBP) is the ratio of browse pressure on a single woody species in a plot (such as sugar maple) to the total browse pressure on all woody species in the plot. A RBP value greater than 1 indicates higher browse pressure on that species relative to the collective pressure of all other woody species in the plot.

 
 
Species Minimum First quartile Median Third quartile Maximum
Big-toothed aspen
Populus grandidentata
1.1 1.7 1.8 2 3.1
Blue beech
Carpinus caroliniana
0.3 1.0 1.3 1.75 2.5
Quaking aspen
Populus tremuloides
0 0.9 1.2 1.7 3.8
Red elm
Ulmus rubra
0 0.8 1.1 1.5 2.9
Sugar maple
Acer saccharum
0 0.6 1.1 1.4 2.4
Black ash
Fraxinus nigra
0 0.6 1 1.4 2
Green ash
Fraxinus pennsylvanica
0 0.7 1 1.3 2.6
Hackberry
Celtis occidentalis
0 0.8 1 1.4 1.9
Box elder
Acer negundo
0 0.5 0.9 1.2 3.5
Bur oak
Quercus macrocarpa
0 0.6 0.9 1.3 1.7
Paper birch
Betula papyrifera
0 0.4 0.9 1.2 1.5
Ironwood
Ostrya virginiana
0 0.4 0.9 1.2 2.6
White ash
Fraxinus americana
0.6 0.6 0.9 1.1 1.5
Basswood
Tilia americana
0 0.5 0.8 1.3 1.9
American elm
Ulmus americana
0 0.5 0.7 1.1 2.8
Bitternut hickory
Carya cordiformis
0 0.2 0.6 1 1.8
Northern red oak
Quercus rubra
0 0.1 0.6 1.0 2.2
Red maple
Acer rubrum
0 0.4 0.6 1.1 3
White pine
Pinus strobus
0.3 0.5 0.6 0.6 0.7
Balsam fir
Abies balsamea
0 0 0.2 0.6 1.2

Relative browse pressure on woody understory species for all forested EMN plots

 
 
Species Minimum First quartile Median Third quartile Maximum
Round-leaved dogwood
Cornus rugosa
1.7 1.7 2 2.2 2.7
Downy arrowwood
Viburnum rafinesquianum
0 0.9 1.5 1.7 2.3
Chokecherry
Prunus virginiana
0 0.9 1.5 1.7 3
Gray dogwood
Cornus racemosa
0.8 1.3 1.5 2 2.5
Missouri gooseberry
Ribes missouriense
0 1.2 1.5 1.8 2.9
Mountain maple
Acer spicatum
0.5 1.1 1.5 1.6 3.9
American hazelnut
Corylus americana
0.4 1.0 1.4 1.6 2.2
Common buckthorn
Rhamnus cathartica
0 1.3 1.4 1.7 2.3
Fly honeysuckle
Lonicera canadensis
0 0.9 1.4 1.8 3
Beaked hazelnut
Corylus cornuta
0 1 1.4 1.7 2.8
Red-berried elder
Sambucus racemosa
0.4 0.7 1.4 2.0 3.8
Nannyberry
Viburnum lentago
0.8 1.1 1.3 1.9 3.1
Pagoda dogwood
Cornus alternifolia
0.6 0.8 1.2 1.6 2.2
Prickly gooseberry
Ribes cynosbati
0 0.9 1.2 1.6 3.2
Juneberry
Amelanchier sanguinea/spicata
0 0.8 1.1 1.7 2.5
Juneberry
Amelanchier laevis/interior
0 0.6 1.0 1.4 2.6
Bush Honeysuckle
Diervilla lonicera
0 0.4 1.0 1.3 2.1
Black cherry
Prunus serotina
0 0.6 0.9 1.4 2.7
Morrow's honeysuckle
Lonicera morrowii
0 0.4 0.9 1.4 1.5
Prickly rose
Rosa acicularis
0 0.7 0.9 1.5 1.6
Thimbleberry
Rubus parviflorus
0.5 0.6 0.7 1.05 1.8
Velvet-leaved blueberry
Vaccinium myrtilloides
0 0.4 0.7 0.9 2.1
Canada moonseed
Menispermum canadense
0.4 0.7 0.8 1.0 1.3
Lowbush blueberry
Vaccinium angustifolium
0 0.2 0.5 0.8 2.3
Prickly ash
Zanthoxylum americanum
0 0.3 0.5 1.1 1.6
Wild grape
Vitis riparia
0 0.3 0.5 0.6 1.7
Tall blackberry
Rubus (Blackberry)
0.3 0.4 0.5 0.7 0.8
Wild red raspberry
Rubus idaeus
0 0 0.4 0.9 1.6
Greenbrier
Smilax tamnoides
0 0.2 0.3 0.9 1.8
Woodbine
Parthenocissus vitacea
0 0 0.3 0.4 1.1
Black raspberry
Rubus occidentalis
0 0 0.2 0.3 0.9
Eastern poison ivy
Toxicodendron radicans
0 0 0 0.1 0.3
Leatherwood
Dirca palustris
0 0 0 0 1.8
Snowberry
Symphoricarpos albus
0 0 0 0.3 1.8
Western poison ivy
Toxicodendron rydbergii
0 0 0 0 0.4

Objective: Document status and trends in non-native invasive plant species

Initial Work and Observations

  • Non-native species cover is the ratio between the sum of non-native species cover compared to total species cover in a plot.
  • EMN plots installed in prairies have higher relative non-native species cover than plots installed in forests. This difference is likely driven by two invasive grasses, Kentucky bluegrass (Poa pratensis) and smooth brome (Bromus inermis), that occur largely in non-forested habitats.
  • EMN plots installed in southern communities have higher relative non-native species cover than plots installed in northern communities.
thumbnail Figure 7.

Click to enlarge

Figure 7. Ratios of non-native species cover to the total species cover at each EMN plot. Larger cylinders represent higher relative non-native species cover. Plots are categorized into four groups by ecological classification system: Fire Dependent Forest (red), Mesic Hardwood Forest (green), Upland Prairie (yellow), and Wet Prairie (orange). There appears to be a spatial pattern of increases in relative non-native species cover from north to south and east to west. In addition, upland and wet prairie systems appear to have high relative non-native species cover than fire dependent and mesic hardwood forests.

Ratios of non-native species cover to total species cover in northern vs. southern floristic regions in four Ecological Systems

 
 
 
Percent Non-native
Ecological Systems median max min q25 q75
Northern Fire Dependent Forest (FDn) 0.00 0.46 0.00 0.00 0.00
Southern Fire Dependent Forest (SDn) 3.61 26.89 0.00 2.00 6.98
Northern Mesic Hardwood Forest (MHn) 0.00 1.54 0.00 0.00 0.03
Southern Mesic Hardwood Forest (SHn) 1.35 64.58 0.00 0.22 13.37
Northern Upland Prairie (Upn) 16.86 43.44 5.14 12.33 29.33
Southern Upland Prairie (Ups) 29.40 85.54 0.00 13.52 53.11
Northern Wet Prairie (WPn) 8.88 28.96 1.77 3.93 13.70
Southern Wet Prairie (WPs) 11.96 42.30 1.60 4.17 31.20

Ratios of non-native species cover to total species cover in northern vs. southern floristic regions in four Ecological Systems. Overall, plots in the southern floristic regions of each system appear to have higher cover of non-native species compared to their northern counterparts. The prairie systems have noticeably larger invasive species cover than the forest systems.


Objective: Determine status and trends in volume of coarse woody debris

Coarse woody debris (CWD) is the large dead wood present in the forest. CWD includes both snags (standing dead trees) and fallen logs. CWD plays a major role in natural forest processes, including providing habitat (e.g., small mammals, invertebrates), cycling nutrients, and storing carbon.

Click to enlarge

Initial Work and Observations

  • EMN staff measure the diameter of all downed woody debris (and strongly leaning snags) ≥ 7.5cm in diameter that intersects the 45-meter-long center lines of EMN plot transects.
  • From these measurements, volume is estimated for the amount of CWD that would occur in a full hectare ( m^3/ha )

Objective: Assess multiple factors impacting forest floor conditions

Under natural conditions in forests, leaf litter breaks down slowly leaving the forest floor layered with organic matter in various stages of decomposition, from intact leaves to finely decomposed particles. Several native forest plant species are adapted to this slow layering process, requiring finely decomposed leaf litter, called duff and humus, to survive. Invasive earthworms, transported into Minnesota by human activity since the 1700s, are rapidly removing duff and humus layers in forests throughout many parts of Minnesota. Measurements of leaf litter and humus are collected in forested EMN plots to assess the presence and impact of invasive earthworms.

Click to enlarge

Initial Work and Observations

  • EMN uses the Invasive Earthworm Rapid Assessment Tool (IERAT) to evaluate depletion of leaf litter on forest floors by earthworms. IERAT scores range from 1 in plots with intact, unfragmented litter, duff, and humus layers (i.e., no worm effects), to 5 in plots characterized by bare mineral soil with abundant earthworm casts and middens (i.e., high worm effects).
  • IERAT was developed for mesic hardwood forest systems, like sugar maple and basswood dominated communities.
  • EMN plots show higher levels of invasive earthworm impacts in mesic forests in the southern half of the state relative to the northern half.

Questions

Nathan Dahlberg, Project Coordinator
Ecological Monitoring Network
651-259-5726
[email protected]


Funding for this project was provided by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources (LCCMR). The Trust Fund is a permanent fund constitutionally established by the citizens of Minnesota to assist in the protection, conservation, preservation, and enhancement of the state’s air, water, land, fish, wildlife and other natural resources.

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