National Park Service

Northeast Temperate Network (NETN)

Parks in this Network

NETN Network Map
Click to see Larger
Network Map
Find your park logo

Distribution of Non-Indigenous Invasive Plants in the Vicinity of the Appalachian National Scenic Trail

Appalachian National Scenic Trail showing Non-indigenous species observation points in the HUC10 shell.
Figure 1. Non-indigenous invasive species (NIIS) observation points within the HUC10 shell. Background of map obtained from ESRI ArcGIS Online and data partners including USGS and © 2007 National Geographic Society.
The following review is based on information freely available from the Early Detection and Distribution Mapping System (EDDMapS). For more information about the EDDMapS, visit their web site at: http://www.eddmaps.org/.

Ecoregional Section Abreviations and Names

NMF—Northeastern Mixed Forest (211)

EBF—Eastern Broadleaf Forest (221)

SMF—Southeastern Mixed Forest (231)

ANEMF—Adirondack-New England Mixed Forest--Coniferous Forest--Alpine Meadow (M211)

CABF—Central Appalachian Broadleaf Forest-Coniferous Forest-Meadow (M221)

Introduction

The Appalachian National Scenic Trail (APPA) and the National Park Service (NPS) are entrusted to protect, among other things, the native plant and animal communities that contribute to the unique nature of the Appalachian Trail. These "core mission" resources are being threatened and replaced by non-indigenous (non-native and invasive) aquatic and terrestrial plants and animals. In some cases, the biological richness and integrity of these natural resources may be forever changed.

Non-indigenous Invasive species (NIIS) have been introduced to areas along the APPA and other natural areas by humans, animals, wind, and water. Figure 1 shows the locations of NIIS that have been identified in the immediate vicinity of, and in some instances on the APPA. NPS has a great interest in NIIS management, and has established Exotic Plant Management Teams (EPMT) to help manage problematic plants in selected regions and parks across the nation. While the focus of the EPMT program is on implementing NIIS management programs, there remains a need to identify and track invasions and early detection is frequently cited as the best way to deal with NIIS.

Background and History

An "…invasive species is an alien species whose introduction does or is likely to cause economic or environmental harm or harm to human health…" (USPEO 1999). Presidential Executive Order 13112 further defines an "…alien species, with respect to a particular ecosystem, as any species, including its seeds, eggs, spores, or other biological material capable of propagating that species, that is not native to that ecosystem…" (USPEO 1999). In broad terms, a non-indigenous invasive species is an organism that has been introduced deliberately or unintentionally into an environment in which it did not evolve, is capable of establishing self-sustaining populations in "…untransformed ecosystems…" (MacDonald et al. 1989), has no natural enemies to limit its reproduction and spread, and is likely to cause harm to human health or the environment.

Successful NIIS tend to have broad ecological requirements and tolerances, effective reproductive and dispersal mechanisms (Rejmánek and Richardson 1996), competitive ability superior to that of natives in the original or modified system, and the capability of altering the site by significantly changing resource ability and/or disturbance regimes (Baker 1965). These characteristics make NIIS in the vicinity of the APPA potential invasion threats and underscore the importance of species "watch lists" (Table 1) as well as knowing the location of existing infestations of the most common NIIS (Figures 2 & 3).

Table 1. Species watch list showing the number of locations that NIIS have been found in the vicinity of the APPA. On APPA is defined to be within 500 ft. of the APPA footpath, while off APPA is anywhere within the HUC10 shell but not within 500 ft. of the APPA footpath. List includes species found in at least 100 locations, 2 or more states, and 2 or more ecoregional provinces.

NIIS negatively impact park resources and visitor enjoyment by altering landscapes and fire regimes, reducing native plant and animal habitat, blocking and altering viewsheds, and increasing the need for and cost of additional trail maintenance. Examples from national parks include alteration of geochemical cycling by feral pigs (Sus scrofa) in Great Smoky Mountains National Park, acceleration of soil erosion rates by feral mammals in Channel Islands National Park, alteration of hydrological cycles by salt cedar (Tamarix sp.) in Death Valley National Monument and Big Bend National Park (MacDonald et al., 1989), and obstruction of cultural viewsheds by common mullein (Verbascum thapsus) on Skyline Drive in Shenandoah National Park (James Åkerson, personal communication, March 2, 2010).

20 Most Frequently Reported NIIS


(Click on the species name to change the observation map)
Figure 2.
Figure 3. Distribution of species observations across the three ecoregional sections that intersect the APPA as well as the remaining two (other) that intersect the HUC10 shell but not the APPA itself.

Along the 2,180 mile length of the APPA, there are over 900 road crossings, hundreds of side trails and access points, innumerable parking areas, thousands of miles of boundary, and thousands of adjoining land owners. Each of these may be an invasion vector, and that makes the APPA highly vulnerable to invasions.

Why Detect Non-indigenous Species?

Early detection followed by rapid response may be the most effective way to eradicate NIIS before they become widely established, thus eliminating the need for costly and resource-intensive control programs (Ashton and Mitchell 1989, OTA 1993, Atkinson 1997, Myers et al. 2000, Harris et al. 2001, Timmins and Braithwaite 2001, Rejmánek and Pitcairn 2002, FICMNEW 2003). Conversely, delayed action reduces the chance of successful eradication (Rozenfelds et al. 1999, NISC 2008). Early response can save money and minimize ecological damage by preventing habitat fragmentation and ecosystem degradation associated with large or widespread NIIS populations and related management activities (Smith et al. 1999, Timmins and Braithwaite 2001).

One of the most vital steps in confronting new NIIS problems is to know they exist (FICMNEW 2003). "Early Detection and Rapid Response" is one of five long-term strategic goals of the National Invasive Species Council's (NISC) Management Plan (NISC 2008). It is also a main element of the Federal Interagency Committee for the Management of Noxious and Exotic Weed's (FICMNEW) "National Early Detection and Rapid Response System for Invasive Plants" (FICMNEW 2003). Next to prevention, "…early detection, rapid assessment and rapid response (EDRR) is a critical second defense against the establishment of invasive populations…" (NISC 2008).

The costs and benefits of early detection are hard to estimate, but the cost to control NIIS once established are known. Pimental et al. (2005) estimates that approximately $120 billion/year is spent controlling NIIS; the total cost of destruction by introduced rats on U.S. farms, for example, is more than $19 billion per year, while non-indigenous weeds, pest insects, and plant pathogens cause several billion dollars' worth of losses to crops, pastures, and forests annually in the United States. The chestnut blight fungus (Cryphonectria parasitica) and the virtual elimination of the American chestnut (Castanea dentata) in the early 1900's (von Broembsen 1989) is another well known example of the potentially devastating economic and ecological consequences of invading species.

Eradication of established NIIS is difficult, if not impossible in many cases, but early detection and associated management responses have proven effective in reducing, if not eliminating, the associated costs and consequences (MacDonald et al. 1989, Braithwaite 2000). Early detection and rapid response success stories include restharrow (Ononis alopecuroides) in San Luis Obispo County, California (Tu 2002a), Egeria (Egeria densa) in the Connecticut River (Tu 2002b), and water hyacinth (Eichhornia crassipes) and parrot-feather watermilfoil (Myriophyllum aquaticum) in the Shawnee National Forest, Mississippi (Corey 2008).

Methods

We began by downloading tabular data for 1,369 species from the EDDMapS website (http://eddmaps.org/tools/dataaccess.cfm) and by restricting our review to terrestrial species. Species available from EDDMapS are reported to be invasive somewhere througout their range, but that range may not intersect the APPA region. To remove inconsequential species, the data were "clipped" to the HUC10 shell to establish a geographic scope for NIIS species relative to the APPA. The HUC10 shell, or the general frame of reference used to establish an area of interest around the APPA, is the "outer" boundary of all HUC10 hydrologic units that are within 5 miles of the APPA land base. The hydrologic unit system was developed by the USGS and subsequently modified by the Natural Resource Conservation Service (NRCS). HUC10 units (watersheds) are defined at the fifth level of the Hydrologic Unit Code system, with each being given a discrete 10-digit code. There are 177 individual HUC10 hydrologic units within this shell. Though they are termed watersheds, Omernik (2003) explains that hydrologic units are not always true watersheds and that some hydrologic elements contained within the HUC10 shell may not include all upstream components of a true watershed. We use the HUC10 because it incorporates all areas of immediate interest to APPA resource managers as well as areas that are more distant but potentially of great ecological similarity. While the HUC10 shell does not guarantee that projects or data within it will be of interest, or that data and activities beyond it are not of interest, it does provide a starting point and some degree of guidance when attempting to determine if data or activities are worthy of further consideration. Of particular relevance is the fact that NIIS species identified within the HUC10 shell are already within a watershed that leads directly to the APPA, and because many NIIS are believed to spread quickly within watersheds the proximity of these NIIS to the APPA is a potential concern, particularly for species that have yet to be identified along the APPA.

After identifying a dataset specific to the APPA HUC10 shell, each point was subsequently associated with an ecoregional province as well as whether the observation was made within 500 feet of the APPA footpath itself (Table 1, Figure 2). Establishing the ecoregional province and the current proximity to the APPA is useful for determining whether a NIIS poses a potential invasion threat. For example, a species that is present in the surrounding HUC10 shell but not yet along the APPA would be considered a potential future threat, whereas a species that is already present along the APPA would no longer be considered to be a potential threat because it has already arrived.

Discussion

The information presented on this web page is based on data obtained from EDDMapS, a program that both collects new NIIS data and aggregates data from a number of other sources. While each species on the list is considered to be invasive, how problematic a species may be is not factored into the information contained on this web page, nor is the recency of a species arrival or its prominence on the landscape. Rather, each of these factors are presented equally, leaving the decision to include or exclude a species from a watch list up to the viewer of this web page.

How invasive a species may be, the recency of its arrival, and it's prominence contribute to the general perception of the various NIIS. Some of species on the top 20 list (Figures 2 & 3) may be obscure, or may have been present on the landscape for a long enough time that they are overlooked as NIIS. Coltsfoot, for example, has been present long enough that many people may not realize it is not native to North America, whereas species like Japanese knotweed that become established and overwhelm areas quickly are immediately recognized. Consequently, there may be instances where a species like coltsfoot might be a bigger management problem than Japanese knotweed, but if the coltsfoot infestation is not recognized as an invasion the management urgency may be overlooked.

Figure 4. Distribution of land area and observations, as a percent, across five ecoregional provinces.

There are 243 NIIS species known to occur in the HUC10 shell, with a median of 71.0 observations each. Japanese honeysuckle is the most frequently observed NIIS (1,839 records; Table 1, Figures 2 & 3) and 67 species were observed only once. In total, there are 20,189 NIIS observations in the HUC10 shell, excluding aquatic species (Figure 1). While these data have great value for the detection of NIIS, they are "presence only," meaning that there are no data on overall search effort or search locations. This is an important point because without data telling us where NIIS do exist as well as where NIIS do not exist, our ability to make broader inferences about the existence of NIIS throughout a particular area, in this case the APPA and the surrounding region, are limited.

Appalachian National Scenic Trail showing Non-indigenous species observation points in the HUC10 shell.
Figure 5. "Hypothetical" series of 1,480 NIIS sampling points that are spatially balanced.

Data do not appear to be evenly distributed throughout the area of interest – there are places where the data are more concentrated than others (Figures 2 & 3). Notably, observation counts for 15 of the 20 most frequently observed NIIS were greatest in ecoregional province CABF, with observations for 9 species exceeding 90% of their respective total observations. Province CABF extends from northeastern Pennsylvania to Georgia, includes Great Smoky Mountains National Park, and accounts for the greatest land area as well as the most NIIS observations of all ecoregional provinces that intersect the APPA (14,785 observations, or 73% of all NIIS observations; Figures 1 & 2). So, while it would be reasonable to expect that CABF would have the most observations if the number of observations and land area are correlated, Figure 4 indicates that the number of observations in CABF are disproportionately greater than any of the other provinces.

The apparent high density of observations in the vicinity of Great Smoky Mountains National Park (Figure 2) is likely a result of greater sampling effort in a relatively small area than the presence of more NIIS in the Park than other geographic areas along the APPA. A similar phenomenon occurs further north where a higher density of observations are generally found throughout New England (except for Maine) than adjacent New York. It would be easy to conclude, based on the current dataset, that NIIS are more abundant throughout New England than in adjacent New York, however, it turns out that a large volume of data in New England originate from a very prolific program called the Invasive Plant Atlas of New England (IPANE). IPANE contributes all their observations to EDDMapS, but does not operate in New York State despite its close proximity. So, are there really more NIIS in New England than adjacent New York, or was the sampling effort more intense in New England? Unfortunately, it isn't possible to definitively answer that question using the current dataset, but sampling intensity and spatial balance certainly play a role. Figure 5 provides a graphical contrast of points that are "spatially balanced" versus the existing, likely concentrated sample (Figure1). In this "hypothetical" layout, just 1,480 points (~10 % of the Top 20 sum) are used to illustrate how fewer points may actually achieve better coverage, and how "clumped" the actual points are in comparison to a balanced distribution. Figures 6 and 7, that indicate that a large percentage of species were identified with relatively few observations, helps support the notion that reduced effort (fewer observations) but a different sampling strategy (spatially balanced vs. concentrated) might be a more efficient way to detect NIIS. In the end, however, it is unrealistic to think that it would be possible to achieve a balanced NIIS detection effort using a dataset that is a compilation of data from many NIIS detection projects.

For these and other reasons, it is important to understand that maps based on the current dataset do not provide an accurate depiction of the full distribution of NIIS on or around the APPA. This limitation aside, maps based on these data are still valuable because knowing where NIIS have been observed in the past gives resource managers the ability to "fine tune" their early detection species watch lists (Table 1) in their quest to preserve and protect key natural resources on and around the APPA.

Figure 6. Percent of species detected vs. percent total observations.

These data are well suited to serve as an early warning tool – particularly for species that have not yet been detected on the APPA (Table 1). Species listed in table 1 are those that have more than the median number of observations (71.0) and are present in at least two states and two ecoregional provinces. These criteria attempt to identify species that are both sufficiently widespread and sufficiently abundant to pose a potential threat to the APPA region. Table 1 also identifies the proportion of the NIIS that exist on the APPA, with a low % indicating that relatively few of the observations were made on the APPA. Only 1 of the species (wine raspberry) exceeds 50% of observations in the HUC10 shell, while 20 are below 10%, and 6 have not been identified on the APPA (0%). Species that have many observations in the HUC10 shell but have comparatively few observations on the APPA may represent the greatest threats. For example, Japanese honeysuckle is the most observed species in the HUC10 shell but only a little over 10% (10.33%, 190 observations) of all observations are on the APPA. Determining whether a species like Japanese honeysuckle is a real threat requires more than a cursory review of existing observations, and must consider whether a disproportionate number of observations come from a particular area, are the result of a concentrated effort to identify NIIS, or both. The information presented in Figure 3 compares the number of observations, as a percent, for each of the 20 most frequently observed species. For Japanese honeysuckle, 98.74% of all observations within the HUC10 shell come from the "CABF" ecoregional section, but it is not possible to determine if they are the result of a concentrated NIIS detection effort. Figure 2 provides a graphical depiction of the Japanese honeysuckle observations, showing that a large number are within Great Smoky Mountains National Park.

Figure 7. Number of observations of a species and the number of species that have been observed that many times.

Despite the quantity of data, none of the information currently available can be used to predict the exact location that a NIIS might appear along the APPA. However, when an aggressive NIIS is known to be in close proximity to the APPA, we can produce watch lists that observers use to search for signs of new infestations. Generating such lists proactively addresses problems plaguing previous projects by focusing the search to a few species that are of higher concern.

Sources Cited

Ashton, P. J., and D. S. Mitchell. 1989. Aquatic plants: Patterns and modes of invasion, attributes of invading species and assessment of control programmes. In J. A. Drake, H. A. Mooney, F. di Castri, R. H. Groves, F. J. Kruger, M. Rejmanek, and M. Williamson (Eds.). Biological invasions: A global perspective. Pp. 111-154. Chichester, England. John Wiley & Sons, Ltd.

Atkinson, I. A. E. 1997. Problem weeds on New Zealand islands. Science Conservation 45. Wellington, Department of Conservation.

Baker, H. G. 1965. Characteristics and modes of origin of weeds. In Baker, H. G., and G. L. Stebbins (eds.). The genetics of colonizing species. Pp.147-168. New York, NY. Academic Press.

Braithwaite, H. 2000. Weed surveillance plan for the Department of Conservation. Wellington, Department of Conservation.

Corey, S. 2008. Early detection and rapid response: Shawnee National Forest early response at work. http://www.fs.fed.us/r9/ssrs/story?id=4224. United States Forest Service, Eastern Region. Accessed February 24, 2009.

Federal Interagency Committee for the Management of Noxious and Exotic Weeds (FICMNEW). 2003. National early detection and rapid response system for invasive plants in the United States. Washington, DC. http://www.fws.gov/ficmnew/FICMNEW_EDRR_FINAL.pdf.

Harris, S., J. Brown, and S. Timmins. 2001. Weed surveillance–how often to search? Science for Conservation 175.

MacDonald, I. A.W., L. L. Loope, M. B. Usher, and O. Harmann. 1989. Wildlife conservation and the invasion of nature reserves by exotic species: a global perspective. In Drake, J., F. diCastri, R. Groves, F. Kruger, H. A. Mooney, M. Rejmanek, and M. Williamson, (eds.). Biological invasions: a global perspective. Wiley and Sons.

Myers, J. H., D. Simberloff, A. M. Kuris, and J. R. Carey. 2000. Eradication revisited: Dealing with exotic species. Trends in Ecology and Evolution. 15(8):316-320.

National Invasive Species Council (NISC). 2008. 2008-2012 National Invasive Species Management Plan. http://www.invasivespeciesinfo.gov/council/mp2008.pdf.

Omernik, J.M. 2003. The Misuse of Hydrologic Unit Maps for Extrapolation, Reporting, and Ecosystem management. Journal of the American Water Resources Association. (JAWRA) 39(3):563-573.

Pimentel, D., R. Zuniga, and D. Morrison. 2005. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecological Economics. 52(3):273-288.

Rejmánek, M., and M. J. Pitcairn . 2002. When is eradication of exotic plant pests a realistic goal? Pp. 169-176. in Veitch C. R, Clout, M. N., eds. Turning the tide: The eradication of invasive species. Gland (Switzerland): IUCN.

Rejmánek, M., and D. M. Richardson. 1996. What attributes make some plants more invasive? Ecology 77(6):1655-1661.

Rozenfelds, A. C. F., L. Cave, D. I. Morris, and A. M. Buchanan. 1999. The weed invasion in Tasmania since 1970. Australian Journal of Botany 47:23-48.

Smith, H. A., W. S. Johnson, J. S. Shonkwiler, and S. R. Swanson. 1999. The implications of variable or constant expansion rates in invasive weed infestations. Weed Science 47(1):62-66.

Timmins, S. M., and H. Braithwaite. 2001. Early detection of invasive weeds on islands. Pp. 311-318 In Veitch, C. R., and Clout, M. N. (eds.). Turning the tide: the eradication of invasive species. IUCN SSC Invasive Specialist Group. IUCN, Gland, Switzerland and Cambridge, UK.

Tu, M. 2002a. A new invader to North America, rapidly controlled in San Luis Obispo County, California. The Nature Conservancy. Press Release. http://tncinvasives.ucdavis.edu/stories/ca004.html. Accessed February 12, 2009.

Tu, M. 2002b. Early detection and cooperation prevents the establishment and spread of a severe invasive plant pest into the Connecticut River. The Nature Conservancy. Press Release. http://tncinvasives.ucdavis.edu/stories/ct001.html. Accessed February 12, 2009.

U.S. Congress Office of Technology Assessment (OTA). 1993. Harmful invasive species in the United States. U.S. Government Printing Office. Washington, DC.

U.S. Presidential Executive Order (USPEO). 1999. Executive Order 13112 of February 3, 1999. Federal Register: February 8, 1999. Volume 64, Number 25.

von Broembsen, S. L. 1989. Invasions of natural ecosystems by plant pathogens. In J. A. Drake, H. A. Mooney, F. di Castri, R. H. Groves, F. J. Kruger, M. Rejmanek, and M. Williamson (Eds.). Biological invasions: A global perspective. Pp.77-83. Chichester, England. John Wiley & Sons, Ltd.

⇑ To Top of Page

Last Updated: December 30, 2016 Contact Webmaster