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.
On this page
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.
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.
- 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
- Assess current and long-term trends in vegetation composition, structure and condition within native grasslands, wetlands and forests.
- Track long-term trends in magnitude and extent of browse on vegetation.
- Document status and trends in non-native invasive plant species presence and cover.
- Document plant community change by assigning plots to Minnesota native plant community types in the field and assess quality and condition of native plant communities over time.
- Document status and trends in forest structure.
- Track long-term trends in forest succession by tracking height and diameter (DBH) and by documenting tree mortality and regeneration.
- Determine status and trends in the volume of coarse woody debris.
- Track changes in canopy cover in forest stands.
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
-
Assess soil type and health in order to relate it to trends in grasslands, wetlands, and forests.
- Qualitatively assess topographic position, aspect, and soil type at the onset of monitoring.
- In upland forests, assess forest floor condition and compaction by documenting litter type and depth and amount of exposed bare soil. In addition, using a rapid assessment method, track impacts of earthworms using visual indicators to understand potential effects on vegetation or soil compaction.
- In peatlands, document organic soil depth and depth to mineral soil and track status and trends in substrate as it relates to peat type, hummock height, and dominant bryophytes.
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 wat | 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.
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.
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.
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.
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.