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 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. Details on EMN field methods may be found in the EMN Standard Operating Procedures (SOPs).


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 IDLand ManagerCounty
99003FOROlmsted
99004FOROlmsted
99002FOROlmsted
127SNFSt. Louis
3604FORHouston
2560PrivateRock
752PrivateLyon
713WMAKanabec
3271WMABig Stone
1763WPAGrant
1967FORKoochiching
1869CountyHennepin
1074CountySt. Louis
1693cityAnoka
4436PrivateOlmsted
3928CountyScott
857FORPine
673SNFLake
1172PrivateWabasha
994SNFSt. Louis
2653CountyHennepin
1236WMAOlmsted
3414FORCass
59SNFCook
1015FORLake of the Woods
561SNFLake
585PrivateKanabec
870CountyCass
473PrivateStearns
4968PrivateBlue Earth
422CNFCass
319CountyKoochiching
171WMARoseau
658CNFItasca
734CountyHubbard
335BWCALake
5584CountyJackson
166CountyCass
378WMAPolk
261MN PowerSt. Louis
975FORKoochiching
180PrivateFillmore
721SNFSt. Louis
799FORLake of the Woods
851PATDouglas
1223PrivateBig Stone
3290TNCWilkin
1124PATGoodhue
387WPASwift
187SNFLake
2088WMALe Sueur
3305WPARenville
270snaPolk
1003SNFCook
1511nwrMarshall
1925PATCarlton
278FORSt. Louis
99WPADouglas
3107PrivatePope
905CountyAitkin
956PrivateMurray
14PrivateNorman
575FORKoochiching
324FORSt. Louis
277FORPine
2300TNCLincoln
9524FOROlmsted
5480amaFaribault
230CountyCrow Wing
417CountyLake
1586CountySt. Louis
82CNFCass
704PrivatePipestone
1652PrivateHouston
571SNFCook
2051PrivateKandiyohi
421FORMorrison
155WMAPennington
49BWCALake
1935FORKoochiching
8456PrivateDodge
429CountyAitkin
1574CountyHubbard
8124WMAPipestone
305SNFLake
1689WMAChisago
951WMAMarshall
1187PrivatePope
650CountyHubbard
6925snaHennepin
1499PrivateMarshall
949PATPine
659WMADouglas
482SNFSt. Louis
625SNFLake
223FORKoochiching
198FORSt. Louis
893CountySt. Louis
2730TNCClay
2840snaRice
1041CountySt. Louis
2068snaHouston
239FORKoochiching
36WMAOlmsted
4340PrivateDodge
1365CountyPine
38FORHubbard
1591WMAMarshall
136WMADakota
1085CountyCarlton
2509CountyWashington
2768WMACottonwood
1192WMABlue Earth
2FORAitkin
352PrivateMurray
898WMAItasca
1246CountyBeltrami
427snaKittson
79BWCALake
1544PATSteele
646CountyItasca
111PATBeltrami
1479nwrOtter Tail
1747WPAStevens
7565cityHennepin
1070CountyClearwater
773CountySt. Louis
1250CountyBeltrami
3252FORFillmore
110WPAPolk
2812PrivatePipestone
122PrivatePolk
1434WPAOtter Tail
536WMARice
262meriwetherKoochiching
921WMAKanabec
456PrivateGoodhue
978CountyHubbard
1786snaNorman
685CountyItasca
210FORCass
57WMAWright
1133WMAAitkin
22FORSt. Louis
416PrivateRedwood
6176snaJackson
546CNFCass
1514WMAClay
637FORSt. Louis
226CountyBeltrami
358CountyCass
302PrivatePolk
568WMAMartin
706CNFItasca
169public watersMeeker
609SNFSt. Louis
877CNFCass
481SNFLake
139WMAMarshall
1581PrivateChisago
929SNFCook
109FORAitkin
1457SNFCook
212CountyOlmsted
69FORSt. Louis
63FORKoochiching
934CountyCrow Wing
1706nwrClay
5004PrivateBlue Earth
2010snaClay
2057CountyAitkin
2282WPABecker
955SNFSt. Louis
763SNFSt. Louis
470CNFCass
450CNFItasca
406WMACass
4211WMALac qui Parle
2567snaBig Stone
400PrivateJackson
486FORCass
1488PrivateJackson
2015FORKoochiching
3379WPAOtter Tail
55PrivateMarshall
3301FORCrow Wing
833SNFLake
284PrivateBlue Earth
1820WMALe Sueur
1140PrivateFillmore
1417PATMille Lacs
636PrivateLincoln
693snaBenton
664PrivateCarver
85CountyPine
4061PATChisago
458CountyBecker
4210PrivateWaseca
1271WMAMarshall
941CountyAitkin
5224WMAFaribault
17CountySt. Louis
413PrivateIsanti
33PATSt. Louis
133FORSt. Louis
495CountyKoochiching
2355WPAOtter Tail
246WMABecker
3584nwrRock
11FORRoseau
338FORItasca
1091WPAPope
113PrivateLake
66WMAAitkin
257PATCook
548PrivateWabasha
1325universityIsanti
423FORBeltrami
765CountySt. Louis
1805PrivateHennepin
242WMAItasca
1242TNCWilkin
1044PrivateHouston
709WMAPine
545CountySt. Louis
2399FORLake of the Woods
726FORCass
1121SNFLake
46FORClearwater
1442CNFBeltrami
3128WMAMartin
5252FORLake of the Woods
434FORItasca
159FORLake of the Woods
478CountyHubbard
1856PrivateRenville
701FORAitkin
466FORHubbard
1062FORHubbard
494PrivateYellow Medicine
731PrivatePennington
3587PrivateKandiyohi
2371WMASwift
983WMARoseau
947nwrLac qui Parle
1167FORKoochiching
2144WMALyon
591SNFSt. Louis
3513TNCMcLeod
480snaBrown
401SNFSt. Louis
443SNFSt. Louis
542PrivateMahnomen
1352PATRice
847FORKoochiching
318PATChippewa
1562PATOtter Tail
3652WMALake of the Woods
145SNFSt. Louis
3888PrivateBrown
255FORKoochiching
1161FORMille Lacs
2752PrivatePipestone
50201nwrBecker
267WMALake of the Woods
5132PATFreeborn
498CNFItasca
733WMAAnoka
383FORKoochiching
94WMAMahnomen
397CountyCarver
1626PATOtter Tail
862CountyBecker
390CountySt. Louis
273CountySt. Louis
202CountyBecker
786CNFCass
523WMARoseau
1229CountyRamsey
108WMABrown
550FORWadena
497CountySt. Louis
1411WMASwift
578WMAAitkin
177SNFCook
1988PrivateWinona
3411PrivateOtter Tail
753CountySt. Louis
518CNFItasca
804FORWabasha
50202nwrBecker
820PrivateFillmore
2074FORBecker
7689WMAKanabec
2951PrivateTraverse
321CountyLake
1137CountyLake
514CountyAitkin
146CNFCass
141cityHennepin
612PrivateGoodhue
3220snaWabasha
2306CountyAitkin
3293CountyAnoka
3357CountyDakota
418CNFBeltrami
1210nwrPolk
678FORCass
511FORKoochiching
939WMAKittson
2269PATWashington
3688WMAFaribault
5405PrivateDakota
515WPAKandiyohi
2598CountyHubbard
541nwrSherburne
565WMAMorrison
572wmdYellow Medicine
1146snaPolk
2841WMAIsanti
689SNFCook
1249SNFCook
1026FORAitkin
521PrivateKanabec
2004WMAWinona
65WMACook
926CountyClearwater
433SNFCook
90PrivateOtter Tail
70FORItasca
18FORKoochiching
558snaPolk
1090FORAitkin
2191FORKoochiching
214CNFCass
897FORLake
4950FORWadena
3123PrivateOtter Tail
990FORClearwater
1079WMARoseau
957CountyAitkin
45PATChisago
31WMALake of the Woods
2425PrivateStearns
186WMAPolk
513FORCook
850FORCass
5917FORSherburne
487WMAMarshall
769SNFCook
2163WPALac qui Parle
1186CNFBeltrami
1253PrivateMorrison
867WPAPope
30WPAMahnomen
683WMAKittson
986WPAWilkin
172PrivateSibley
235TNCKittson
2748PrivatePipestone
895FORSt. Louis
225SNFCook
789CountyPine
801SNFLake
381CountyCarlton
852PrivateWabasha
661FORTodd
614CNFCass
1314CountyHubbard
976PrivateJackson
1111PrivateWilkin
50CountyItasca
5512WMADakota
48001PrivateRedwood
584PrivateGoodhue
1698CountyBeltrami
405Camp RipelyMorrison
4556CountyNicollet
1624WMAWaseca
341PrivatePine
1666FORItasca
747WMAKittson
5604PATFillmore
913SNFSt. Louis
2423WMAMarshall
48002PrivateRedwood
260MeriwetherKoochiching
419PrivatePope
3498PrivateClay
7958FORWadena
99000WMAFreeborn
99001WMAPope

Current proportions of EMN monitoring plots

Proportion of plots by system group

 
 Plant Community SystemNumber 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 Wilderness3
Chippewa National Forest17
National Wildlife Refuge9
Superior National Forest32
Wetland Management District1
Waterfowl Production Area16
Local (17%)City3
County (Parks and Tax Forfeit)64
Other (3%)Private Companies, Universities, The Nature Conservancy, and others12
Private (18%)Privately Owned by Individuals69
State (42%)
(DNR Managed)
Aquatic Mangement Area1
State Forest67
Parks and Trails18
Scientific and Natural Area14
Wildlife Management Area61

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.

Plot IDPlot Percent BrowsedLand ManagerNPCSystem Groups
1130.1forFPn63wetland forests
1858.3forMHn44upland forests
222.9forAPn80wetland forests
3338.7patFDn43upland forests
3617.5wmaMHs38upland forests
3833forFDc34upland forests
3833forFDc34upland forests
4513.5patMHc36upland forests
490bwcaFDn43upland forests
5037.6countyMHn44upland forests
5734.7wmaMHc36upland forests
5967.3snfFDn32upland forests
6328.6forMHn44upland forests
6524.8wmaFPn63wetland forests
6942.7forMHn44upland forests
7912.6bwcaFDn22upland forests
8226.5cnfFDc34upland forests
8530.9countyMHc26upland forests
9038.3privateFDs36upland forests
1095.5forAPn81wetland forests
11025wpaMHc36upland forests
11348.7privateWFn53wetland forests
12730.7snfFDn43upland forests
12730.7snfFDn43upland forests
13348.4forMHn44upland forests
13655.9wmaMHs38upland forests
13928.8wmaWFn55wetland forests
14158.5cityMHs38upland forests
1453.3snfAPn81wetland forests
14639.7cnfMHn44upland forests
17227.6privateMHs39upland forests
17742.9snfFDn43upland forests
18031.2privateMHs37upland forests
18716.2snfFDn43upland forests
19820.8forMHn35upland forests
20245.7countyFDc24upland forests
21243.4countyMHs37upland forests
21243.4countyMHs37upland forests
21437.5cnfMHc26upland forests
22548.4snfMHn45upland forests
22652.3countyMHc26upland forests
23077.4countyWFn55wetland forests
24231.2wmaFDn33upland forests
2550forAPn80wetland forests
25739.1patMHn45upland forests
26031.4meriwetherFFn57wetland forests
26127.3MN PowerFDn43upland forests
26244meriwetherFFn57wetland forests
2672.6wmaFPn71wetland forests
27331countyMHn44upland forests
27773.4forMHc36upland forests
2783.3forAPn81wetland forests
28456.3privateMHs38upland forests
3052.7snfAPn80wetland forests
31937.2countyFDn43upland forests
32157.1countyFDn43upland forests
32434.1forMHn44upland forests
33523.6bwcaFDn32upland forests
3380forAPn80wetland forests
34145.5privateMHc26upland forests
3835.7forWFn74wetland forests
40120.3snfFPn63wetland forests
40568.4camp ripelyMHc26upland forests
40639.4wmaMHc26upland forests
41355privateFDs37upland forests
41737.7countyMHn47upland forests
42243.8cnfFDn33upland forests
42243.8cnfMHn44upland forests
42937.7countyFFn57wetland forests
43310.9snfFDn43upland forests
43429.2forFDn43upland forests
44316.3snfFDn43upland forests
45024.1cnfFPn63wetland forests
45659.6privateMHs38upland forests
45848.6countyMHc37upland forests
46637forMHc26upland forests
47051.6cnfMHc26upland forests
47346.5privateMHc36upland forests
47840.4countyMHc26upland forests
48129snfMHn45upland forests
48225.6snfMHn44upland forests
48644.6forMHn35upland forests
48731.4wmaFFn57wetland forests
49523.3countyMHn44upland forests
49740.6countyFDn43upland forests
49845.4cnfMHn35upland forests
5110forAPn80wetland forests
5130forFPn63wetland forests
51441.2countyMHn35upland forests
51843.1cnfWFn64wetland forests
52125.4privateMHn44upland forests
54255.5privateMHc37upland forests
54527countyFDn43upland forests
54643cnfMHc26upland forests
54850.2privateMHc37upland forests
55823.3snaFPw63wetland forests
5618snfFDn32upland forests
5718.3snfFDn43upland forests
5750forAPn80wetland forests
58458.4privateMHs38upland forests
58529.7privateMHc36upland forests
59148.7snfFDn32upland forests
60951.2snfFDn43upland forests
61244.3privateMHs37upland forests
61425.4cnfFDc34upland forests
63770.4forFFn57wetland forests
64668.8countyMHn35upland forests
65052.2countyFDc34upland forests
66129.3forFDc34upland forests
67320.5snfFDn43upland forests
68555.2countyMHn35upland forests
68939.7snfFDn43upland forests
69353.8snaFDs37upland forests
70148.3forMHn35upland forests
70653.8cnfMHn35upland forests
71355.6wmaMHc36upland forests
72132.6snfFDn32upland forests
72626.5forMHc26upland forests
73423.8countyMHc26upland forests
75340.7countyMHn44upland forests
76318.1snfFDn32upland forests
76521.4countyWFn55wetland forests
76923.5snfFDn43upland forests
77325.5countyMHn44upland forests
7866.4cnfFPn82wetland forests
78933.1countyMHc26upland forests
7994.2forFPn71wetland forests
80116.7snfMHn45upland forests
80441.2forMHs49upland forests
82052.7privateMHs37upland forests
83317.9snfFDn32upland forests
84710.3forFPn63wetland forests
8501.9forFPn81wetland forests
85231.7privateMHs39upland forests
86217.1countyMHn46upland forests
87048.9countyMHn35upland forests
87744.3cnfMHn35upland forests
8931.2countyAPn80wetland forests
89539.8forFDn33upland forests
8970forWFn53wetland forests
9130snfAPn80wetland forests
92633.6countyMHc26upland forests
92929.6snfFDn43upland forests
94123.3countyMHn44upland forests
94932.8patMHc26upland forests
95519.7snfFDn43upland forests
97544.5forMHn35upland forests
99022.2forMHn35upland forests
99439.2snfMHn44upland forests
100346.1snfFDn43upland forests
10154forFPn81wetland forests
104445.4privateMHs37upland forests
106213.6forMHc26upland forests
107054.8countyMHc37upland forests
107410.8countyFPn63wetland forests
107928.8wmaFPn63wetland forests
108514.8countyMHn47upland forests
109013.3forMHc36upland forests
111128.6privateFFn57wetland forests
11213.8snfFDn32upland forests
112463.3patFFs68wetland forests
113323.9wmaFPn63wetland forests
113726countyFDn43upland forests
114043.9privateMHs38upland forests
116138.5forMHc36upland forests
11679.5forFPn63wetland forests
117254.3privateMHs37upland forests
118619.7cnfWFn53wetland forests
119229wmaFFs59wetland forests
123666.7wmaMHs39upland forests
124940.5snfMHn45upland forests
125026.1countyMHn35upland forests
125361.5privateFDs37upland forests
13140.7countyFPn82wetland forests
132534.8universityFDs37upland forests
135249.4patMHs38upland forests
136553.3countyWFn55wetland forests
141740patMHc36upland forests
144216.7cnfMHn47upland forests
14570.8snfAPn81wetland forests
154455patMHs49upland forests
157420.5countyFDc34upland forests
158136.7privateMHc26upland forests
162649.2patMHc37upland forests
165264.4privateMHs37upland forests
169838.5countyMHn35upland forests
186966countyMHs37upland forests
192551.4patMHn35upland forests
19355.6forFPn82wetland forests
196718.5forWFn55wetland forests
198865.6privateMHs37upland forests
200413.1wmaMHs38upland forests
20157.1forAPn81wetland forests
205148.1privateMHs38upland forests
205730.5countyWFn55wetland forests
207447.6forMHn35upland forests
226944.8patFDs37upland forests
230632.8countyMHn35upland forests
242339.3wmaWFw54wetland forests
242534.9privateFDs37upland forests
250947.1countyFDs38upland forests
25985countyFPn81wetland forests
265330countyMHs38upland forests
284058.6snaMHs39upland forests
284128.2wmaMHs38upland forests
322056.6snaMHs37upland forests
325255.6forMHs38upland forests
330142.7forMHc26upland forests
335751.7countyMHs38upland forests
337920.1wpaFDs37upland forests
341123privateFDs37upland forests
341419.7forMHc26upland forests
351371.3tncFFs59wetland forests
360432.3forMHs37upland forests
392844.1countyMHs38upland forests
406159.2patMHc36upland forests
421070.2privateMHs39upland forests
434062.4privateMHs37upland forests
443651privateMHs38upland forests
455655.6countyMHs38upland forests
496833.8privateFFs68wetland forests
500429.6privateFFs59wetland forests
513252.4patMHs39upland forests
540553.8privateMHs37upland forests
548031.1amaMHs38upland forests
551244.4wmaMHs37upland forests
558432.3countyFFs59wetland forests
56043.8patMHs38upland forests
591750.6forFDs37upland forests
692543.6snaMHs38upland forests
768943.5wmaMHc26upland forests
795848.9forFDc23upland forests
845663.8privateMHs38upland forests
952446.5forMHs39upland forests
952446.5forMHs39upland forests
5020158.3nwrFPn82wetland forests
9900250.4forMHs38upland forests
9900335.2forMHs38upland forests
9900434.2forMHs38upland forests

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.

 
 
SpeciesMinimumFirst quartileMedianThird quartileMaximum
Big-toothed aspen
Populus grandidentata
1.11.71.823.1
Blue beech
Carpinus caroliniana
0.31.01.31.752.5
Quaking aspen
Populus tremuloides
00.91.21.73.8
Red elm
Ulmus rubra
00.81.11.52.9
Sugar maple
Acer saccharum
00.61.11.42.4
Black ash
Fraxinus nigra
00.611.42
Green ash
Fraxinus pennsylvanica
00.711.32.6
Hackberry
Celtis occidentalis
00.811.41.9
Box elder
Acer negundo
00.50.91.23.5
Bur oak
Quercus macrocarpa
00.60.91.31.7
Paper birch
Betula papyrifera
00.40.91.21.5
Ironwood
Ostrya virginiana
00.40.91.22.6
White ash
Fraxinus americana
0.60.60.91.11.5
Basswood
Tilia americana
00.50.81.31.9
American elm
Ulmus americana
00.50.71.12.8
Bitternut hickory
Carya cordiformis
00.20.611.8
Northern red oak
Quercus rubra
00.10.61.02.2
Red maple
Acer rubrum
00.40.61.13
White pine
Pinus strobus
0.30.50.60.60.7
Balsam fir
Abies balsamea
000.20.61.2

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

 
 
SpeciesMinimumFirst quartileMedianThird quartileMaximum
Round-leaved dogwood
Cornus rugosa
1.71.722.22.7
Downy arrowwood
Viburnum rafinesquianum
00.91.51.72.3
Chokecherry
Prunus virginiana
00.91.51.73
Gray dogwood
Cornus racemosa
0.81.31.522.5
Missouri gooseberry
Ribes missouriense
01.21.51.82.9
Mountain maple
Acer spicatum
0.51.11.51.63.9
American hazelnut
Corylus americana
0.41.01.41.62.2
Common buckthorn
Rhamnus cathartica
01.31.41.72.3
Fly honeysuckle
Lonicera canadensis
00.91.41.83
Beaked hazelnut
Corylus cornuta
011.41.72.8
Red-berried elder
Sambucus racemosa
0.40.71.42.03.8
Nannyberry
Viburnum lentago
0.81.11.31.93.1
Pagoda dogwood
Cornus alternifolia
0.60.81.21.62.2
Prickly gooseberry
Ribes cynosbati
00.91.21.63.2
Juneberry
Amelanchier sanguinea/spicata
00.81.11.72.5
Juneberry
Amelanchier laevis/interior
00.61.01.42.6
Bush Honeysuckle
Diervilla lonicera
00.41.01.32.1
Black cherry
Prunus serotina
00.60.91.42.7
Morrow's honeysuckle
Lonicera morrowii
00.40.91.41.5
Prickly rose
Rosa acicularis
00.70.91.51.6
Thimbleberry
Rubus parviflorus
0.50.60.71.051.8
Velvet-leaved blueberry
Vaccinium myrtilloides
00.40.70.92.1
Canada moonseed
Menispermum canadense
0.40.70.81.01.3
Lowbush blueberry
Vaccinium angustifolium
00.20.50.82.3
Prickly ash
Zanthoxylum americanum
00.30.51.11.6
Wild grape
Vitis riparia
00.30.50.61.7
Tall blackberry
Rubus (Blackberry)
0.30.40.50.70.8
Wild red raspberry
Rubus idaeus
000.40.91.6
Greenbrier
Smilax tamnoides
00.20.30.91.8
Woodbine
Parthenocissus vitacea
000.30.41.1
Black raspberry
Rubus occidentalis
000.20.30.9
Eastern poison ivy
Toxicodendron radicans
0000.10.3
Leatherwood
Dirca palustris
00001.8
Snowberry
Symphoricarpos albus
0000.31.8
Western poison ivy
Toxicodendron rydbergii
00000.4

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 Systemsmedianmaxminq25q75
Northern Fire Dependent Forest (FDn)0.000.460.000.000.00
Southern Fire Dependent Forest (SDn)3.6126.890.002.006.98
Northern Mesic Hardwood Forest (MHn)0.001.540.000.000.03
Southern Mesic Hardwood Forest (SHn)1.3564.580.000.2213.37
Northern Upland Prairie (Upn)16.8643.445.1412.3329.33
Southern Upland Prairie (Ups)29.4085.540.0013.5253.11
Northern Wet Prairie (WPn)8.8828.961.773.9313.70
Southern Wet Prairie (WPs)11.9642.301.604.1731.20

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.

Course woody debris volume in Minnesota forests. Larger dot size represents higher volume of CWD in a full hectare.

Plot IDLand Managerm3/ha
18Forestry21.87
33Parks and Trails64.16
36Wildlife Management Area62.58
38Forestry21.66
45Parks and Trails46.8
49BWCA170.04
50County31.53
57Wildlife Management Area92.15
59SNF61.23
63Forestry6.07
69Forestry9.89
79BWCA14.36
82CNF23.58
85County44.7
90Private16.33
110WPA42.51
127SNF25.06
133Forestry53.42
136Wildlife Mangement Area62.89
141City11
146CNF62.44
172Private9.61
177SNF59.84
180Private49.53
187SNF206.8
198Forestry40.84
202County16.85
212County33.61
214CNF32.87
225SNF44.17
226County148.88
242Wildlife Mangement Area32.42
257Parks and Trails24.45
261MN Power82.26
273County64.59
277Forestry42.22
284Private60.92
319County117.07
321County37.04
324Forestry61.04
335BWCA24.84
341Private61.56
405Camp Ripely81.24
406WMA98.16
413Private71.92
417County34.47
422CNF353.43
433SNF35.74
434Forestry6.13
443SNF22.69
456Private19.96
458County62.49
466Forestry33.13
470CNF46.21
473Private27.92
478County56.29
481SNF59.47
482SNF28.72
486Forestry53.93
495County30.77
497County29.67
498CNF197.2
514County23.45
521Private50.03
542Private14.95
545County55.25
546CNF48.35
548Private39.52
561SNF87.36
571SNF48.06
584Private65.84
585Private6.43
591SNF27.27
609SNF53.07
612Private62.82
614CNF24.14
646County37.25
650County143.38
661Forestry39.73
673SNF86.85
685County73.55
689SNF69.44
693SNA74.08
701Forestry39.58
706CNF58.15
713WMA44.16
721SNF96.77
726Forestry38.68
734County117.39
753County61.94
763SNF179.38
769SNF153.33
773County47.76
789County49.41
801SNF17.55
804Forestry46.52
820Private32.57
833SNF49.93
852Private57.66
862County189.12
870County68.85
877CNF68.38
895Forestry24.24
926County10.48
929SNF78.77
941County30.06
949Parks and Trails52.55
955SNF58.03
975Forestry54.6
990Forestry132.8
994SNF65.67
1003SNF46.83
1044Private33.21
1062Forestry10.34
1070County28.28
1085County16.87
1090Forestry262.55
1121SNF24.91
1137County78.24
1140Private41.77
1161Forestry10.46
1172Private17.07
1236WMA34.46
1249SNF47.81
1250County15.04
1253Private52.31
1325University126.5
1352Parks and Trails24.72
1417Parks and Trails47.5
1442CNF40.01
1544Parks and Trails118.76
1574County2.63
1581Private27.45
1626Parks and Trails66.58
1652Private50.3
1698County56.58
1869County24.25
1925Parks and Trails35.36
1988Private24.39
2004WMA40.18
2051Private134.96
2074Forestry138.46
2269Parks and Trails20.62
2306County30.95
2425Private93.94
2509County11.32
2653County56.35
2840SNA69.67
2841Wildlife Management Area32.6
3220SNA52.88
3252Forestry48.9
3301Forestry24.54
3357County13.9
3379WPA96.78
3411Private36.12
3414Forestry15.78
3604Forestry9.58
3928County53.13
4061Parks and Trails48.08
4210Private77.32
4340Private10.26
4436Private49.01
4556County102.74
5132Parks and Trails45.38
5405Private35.42
5480AMA19.47
5512Wildlife Management Area131.04
5604Parks and Trails22.83
5917Forestry19.5
6925SNA47.18
7689Wildlife Management Area39.29
7958Forestry13.59
8456Private53.24
9524Forestry84.53
99002Forestry63.53
99003Forestry44.76
99004Forestry85.38

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.

Earthworm levels in Minnesota forests. Larger dot size represents higher earthworm effects on soil.

Plot IDLand ManagerIERAT Stage
18Forestry4
33Parks and Trails2
36WMA5
38Forestry1.25
38Forestry3
45Parks and Trails3.5
49BWCA1
50County4
57WMA2
59SNF1
63Forestry3
69Forestry4.5
79BWCA1
82CNF2
85County3
90Private3.5
110WPA3
113Private2
127SNF3
127SNF3
133Forestry4
136WMA5
139WMA2
141City5
146CNF3
172Private5
177SNF1
180Private5
187SNF1
198Forestry5
202County3
212County5
212County5
214CNF1
225SNF1
226County1.75
242WMA4
257Parks and Trails3
260Meriwether4
261MN Power1
262Meriwether4
273County4
277Forestry1
284Private4
321County1.25
324Forestry4
335BWCA1
341Private2.5
405Camp Ripely1
406WMA2.5
413Private2
417County4
422CNF2
429County5
433SNF1
434Forestry3
443SNF2
456Private5
458County1
466Forestry3
470CNF1.666666667
473Private5
478County1
481SNF3
482SNF3
486Forestry2
487WMA4
495County2.5
497County4
498CNF5
514County1
518CNF1
521Private3
542Private5
545County3.333333333
546CNF1
548Private4
561SNF1
571SNF1
584Private4
585Private3
591SNF1
609SNF1.5
612Private5
614CNF1
637Forestry5
646County5
650County1
661Forestry1
673SNF1
685County4
689SNF1
693SNA3
701Forestry2
706CNF4
713WMA5
721SNF1
726Forestry1
734County1.5
753County4
763SNF1
765County2.25
769SNF1
773County3
789County2
801SNF1
804Forestry3.5
820Private5
833snf1
852Private5
862County2.5
870County5
877CNF1
895Forestry2
926County1
929SNF1
941County1.5
949Parks and Trails5
955SNF1
975Forestry4.666666667
990Forestry1
994SNF3
1003SNF1
1044Private4
1062Forestry1
1070County2
1085County1
1090Forestry3
1121SNF1
1124Parks and Trails2
1137County4
1140Private5
1161Forestry3
1172Private4
1192WMA5
1236WMA5
1249SNF3
1250County5
1253Private5
1325University2
1352Parks and Trails5
1365County2
1417Parks and Trails4
1442CNF3.666666667
1544Parks and Trails5
1574County1
1581Private3.5
1626Parks and Trails4.666666667
1652Private3
1698County3
1869County5
1925Parks and Trails5
1967Forestry3
1988Private5
2004WMA5
2051Private5
2074Forestry1
2269Parks and Trails5
2306County2.5
2423WMA4
2425Private5
2509County4
2653County5
2840SNA5
2841WMA4
3220SNA5
3252Forestry4.5
3301Forestry2
3357County5
3379WPA5
3411Private3
3414Forestry1
3513TNC5
3604Forestry4
3928County4.5
4061Parks and Trails3
4210Private5
4340Private4
4436Private5
4556County5
4968Private4
5004Private4
5132Parks and Trails4.5
5405Private5
5480AMA4.5
5512WMA4
5584County5
5604Parks and Trails5
5917Forestry2.5
6925SNA4.5
7689WMA4.666666667
7958Forestry1
8456Private5
9524Forestry5
9524Forestry5
99002Forestry5
99003Forestry5
99004Forestry3

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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