Regional-Scale Ecological Risk Assessment of Canada Thistle Establishment in the Black

Hills National Forest

April J. Markiewicz a, Meagan J. Harrisb, Wayne G. Landisc,1

a. April J. Markiewicz

Western Washington University

Institute of Environmental Toxicology

516 High Street, MS 9180

Bellingham WA 98225

USA

April.Markiewicz@wwu.edu

b. Meagan J. Harris

Western Washington University

Institute of Environmental Toxicology

516 High Street, MS 9180

Bellingham WA 98225

USA

meaganjharris@gmail.com

c. Wayne G. Landis

Western Washington University

Institute of Environmental Toxicology

516 High Street, MS 9180

Bellingham WA 98225

USA

Wayne.Landis@wwu.edu

1. Corresponding Author:

Wayne G. Landis

Western Washington University

Institute of Environmental Toxicology

516 High Street, MS 9180

Bellingham WA 98225

USA

Wayne.Landis@wwu.edu

Abstract

Ecological risk assessments specific to invasive species provide a tool to quantitatively assess the

risk of their introduction, establishment, and impacts on the landscape. It entails using a spatially

explicit, probabilistic approach that addresses the temporal, spatial, and stochastic attributes of

invasive species population dynamics. In this study, the Bayesian network- relative risk model

(BN-RRM) framework was applied to assess risk of Canada thistle establishment and spread in

the Black Hills National Forest (BHNF) as a result of forestry activities by the U.S. Forest

Service. Bayesian networks were developed for four timber sale areas in the BHNF for pre- and

post-harvest conditions and used to calculate the relative risk of Canada thistle establishment

over time in those areas. There were three findings of this research: 1) the risk of Canada thistle

establishment was present even before logging activities occurred, 2) the greatest risk occurred in

the first and third year after logging, depending on initial site (soils and vegetative cover)

conditions pre-harvest, and 3) risk differed between sale areas and was strongly influenced by

both logging management practices and proximity to other disturbed areas in the landscape. As

management actions are considered or implemented to mitigate the risk of Canada thistle

establishment and spread in the BHNF, the BN-RRM models for each timber sale area can be

updated in the adaptive management process to assess the potential or actual changes in risk.

The BN-RRM framework can also be applied to assess risk of invasion by other noxious weeds

or non-indigenous species at other U.S. Forest Service-managed sites.

Keywords: Ecological risk assessment, Black Hills National Forest, invasive species, Canada

thistle, Bayesian network relative risk model

1

INTRODUCTION 1

2

Invasive Species Risk Assessment 3

Ecological risk assessment (ERA) can be used to support invasive species research and 4

management across local and regional scales (Andersen et al. 2004, Landis 2004, Bossenbroek et 5

al. 2005, Kerns and Ager 2007, Linder and Little 2010). The traditional, contaminant-based 6

ERA framework (USEPA 1998) can be readily adapted for assessments that focus on the impact 7

of the invasive species on human or biological endpoints. Those assessments, focusing only on 8

invasive species entry, establishment, and spread, require a modified framework that can 9

incorporate the temporal, spatial, and stochastic attributes of invasive species population 10

dynamics. The framework therefore needs to be spatially explicit and use a probabilistic 11

approach (Andersen et al. 2004; Landis 2004; Landis et al. 2010; Linder and Little 2010; 12

Stohlgren et al. 2010) as developed by Landis and Wiegers (2005) and Venette et al. (2010). 13

In conducting an invasive species risk assessment, there are three specific characteristics that 14

need to be considered. First, the study of invasive species is inherently on a landscape-scale, 15

requiring spatial analysis tools and spatially explicit data. An invasive species risk assessment 16

therefore requires robust environmental data for the study area to reduce uncertainty and increase 17

robustness in the risk estimates (Andersen et al. 2004; Landis 2004; Deines et al. 2005; Linder 18

and Little 2010; Sikder et al 2006; Stohlgren et al. 2010). As such, Geographical Information 19

System (GIS) models are essential tools in the assessment process, enabling multiple habitat 20

variables (or data layers) to be overlaid for visualization, data analysis, and mapping (Stohlgren 21

et al. 2010). 22

2

Second, the spread of invasive species across the landscape is inherently probabilistic (Kerns 1

and Ager 2007; Landis 2004; Landis et al. 2010; Linder and Little 2010; Seebach et al. 2010; 2

Sikder et al 2006). Heterogeneous landscapes and stochastic processes are not easily or 3

accurately accommodated by traditional contaminant-based deterministic risk assessment 4

frameworks. As such, predicting the spread of invasive species requires a probabilistic approach 5

that considers sources of uncertainty, as well as variability in environmental responses to 6

biological and contaminant stressors (Landis et al. 2010; Linder and Little 2010; Seebach et al. 7

2010). 8

Third, an invasive species risk assessment requires a multiple stressor approach (Andersen et 9

al. 2004; Landis 2004; Landis et al. 2010; Linder and Little 2010; Seebach et al. 2010). The 10

introduction, establishment and spread of an invasive species is influenced by many factors that 11

are both directly and indirectly related to the species in question. Characteristics such as 12

dispersal mechanisms, propagation/transport distance, habitat requirements, competitive 13

advantages, and specialist/generalist tendencies alter the species’ ability to spread and become 14

established in new patches (Andersen et al. 2004; Deines et al. 2005; Kerns and Ager 2007; 15

Landis 2004; Landis et al. 2010; Seebach et al. 2010; Skarpaas and Shea 20074). Additionally, 16

characteristics of the receiving landscape will determine whether the invasive species is 17

successful. These characteristics may include the community structure of native and non-native 18

species, disturbance regimes, connectivity and patchiness in the landscape, habitat type, and 19

environmental conditions, such as elevation and precipitation (Andersen et al. 2004; Deines et al. 20

2005; Landis 2004; Landis et al. 2010; Sikder et al 2006; Linder and Little 2010; Seebach et al. 21

2010; Stohlgren et al. 2010). 22

23

3

Regional Risk Assessment and the Relative Risk Model 1

The relative risk model (RRM) was developed in the late 1990s as an alternative approach to the 2

traditional contaminant-based ERA framework for conducting a multiple-stressor, spatially-3

explicit ecological risk assessment (Landis and Wiegers 2005; Wiegers et al. 1998). The 4

framework of the RRM consists of a conceptual model that identifies sources of stressors, the 5

individual stressors generated by those sources, the linkages of stressors to ecological receptors, 6

and the resulting direct and indirect impacts on those receptors (endpoints) at the landscape 7

scale. The RRM method has been applied to assess a variety of stressors and combinations of 8

stressors including contaminants, disease, environmental parameters, and non-indigenous species 9

in a number of studies since its development (Ayre and Landis 2012; Ayre et al. 2014; Colnar 10

and Landis 2007, Hayes and Landis 2004; Hines and Landis 2014). 11

As subsequent studies were conducted using the RRM framework, modifications to the 12

invasive species risk assessment model were made. Ayre et al. (2014) adapted the RRM to 13

incorporate the use of Bayesian networks (BNs). Bayesian networks, simply defined, are 14

probabilistic graphical models comprised of nodes and links. The nodes represent random 15

variables and the linkages between them represent probabilistic dependences among the random 16

variables (Ben Gal 2007). When incorporated into the RRM, the ERA conceptual model is 17

converted into a Bayesian network with sources, stressors, receptors and impacts as BN nodes. 18

The links connecting the nodes are the probabilistic relationships or causal pathways between 19

them (Tighe et al. 2013). 20

Ayre et al. (2014) used the first BN-RRM framework to assess the risk of whirling disease in 21

wild trout populations of the western USA caused by the parasite Myxobolus cerebralis. Herring 22

et al. (2015) adapted the BN-RRM to evaluate the risk of non-indigenous species in the Padilla 23

4

Bay National Estuarine Research Reserve in Washington state. More recently the incorporation 1

of BNs in the RRM framework was used by Harris and Landis (2017), Johns et al. (2017), and 2

Landis et al. (2017) to assess the human and ecological risks of mercury contamination in the 3

South River, Virginia. The current BN-RRM model used to conduct this ecological risk 4

assessment of the Black Hills National Forest was therefore the culmination of years spent 5

developing, applying, and improving the RRM models and spatial analysis tools. 6

7

Study Area 8

The Black Hills National Forest (BHNF) is located in Western South Dakota and Northwestern 9

Wyoming (Fig. 1), and encompasses approximately 21,000 km2 of forested hills and mountains 10

dominated by ponderosa pine. It is one of sixteen National Forests in the Rocky Mountain 11

region managed by the U.S. Forest Service agency (USFS) of the U.S. Department of 12

Agriculture (USDA). The USFS uses an ecosystem management approach that integrates and 13

balances resource extraction with resource protection and public access through a system of 14

permits and contracts (USFS 2016). Activities within the BHNF include commercial timber 15

production, mining, and grazing, as well as public hiking, camping, and other recreational 16

activities. 17

Logging in the BHNF started during the gold rush in about 1874 and the first sale of 18

government timber occurred in 1899 (J. Butler, personal communication, 2017). Although exact 19

records are scarce, all of the plots in the study area were logged at least once, and several have 20

likely been logged multiple times (Jack Butler, personal communication, 2017). Identifying and 21

quantifying the impacts of historical, as well as current logging practices have been challenging. 22

5

In recent years there has been mounting evidence, however, that these forestry activities 1

related to timber production and timber sales permitted by the USFS have made the region more 2

susceptible to the establishment and proliferation of non-indigenous invasive species (NIS) and 3

noxious weeds (Keeley 2006; Wacker and Butler 2012). Specifically, those activities included 4

road building and maintenance, as well as logging practices i.e., tree and understory plant 5

removal, slash pile burning, and soil disturbance and compaction. As part of its mission to 6

restore, protect, and maintain species diversity and ecological productivity in the nation’s forests 7

and grasslands, the USFS recognized the need to conduct an invasive species risk assessment of 8

the BHNF. Specifically, to assess the short, as well as long-term risks of NIS and noxious weed 9

introduction and spread that may be caused or exacerbated by management activities they 10

authorize. 11

The area selected for study is located in western South Dakota of the BHNF and 12

encompassed four timber sale areas: Dark Canyon, Thrall, Mercedes, and Power Pole (Fig. 1). 13

They were selected due to their differences in location, size, and how they are managed. Dark 14

Canyon, Thrall, and Mercedes are located in the eastern Mystic Ranger District, whereas Power 15

Pole is located further northwest in the Northern Hills Ranger District. Dark Canyon and Thrall 16

are situated on the eastern border of the BHNF and share a sale boundary. Mercedes is located 17

approximately 21 km northwest of Thrall and Dark Canyon, and Power Pole is located 18

approximately 8.5 km northwest of Mercedes (Jack Butler, personal communication, 2015). 19

Unlike the other three timber sale areas, Power Pole is comprised of four smaller 20

noncontiguous subareas in close proximity (less than two km) to each other. In size, Thrall is the 21

largest of the four timber sale areas at 29.76 km2. Power Pole and Mercedes are smaller than 22

Thrall at 20.28 and 19.74 km2, respectively, with Dark Canyon the smallest at 12.56 km2 (Jack 23

6

Butler, personal communication, 2015). Each timber sale area is comprised of smaller cutting 1

unit areas that are each harvested on a contractual basis. 2

Dark Canyon, Thrall, and Power Pole are managed similarly by the USFS as whole-tree 3

harvest sites (Jack Butler, personal communication, 2015) in which the entire above-ground 4

portion of trees and plants are cut and transported to the landing area where they are de-limbed, 5

topped, cut into logs, and loaded into trucks. This process leaves behind large swaths of bare soil 6

where the harvesting occurred, as well as substantial amounts of limbs, vegetation, and woody 7

debris (slash) at the landing site. After harvesting is complete, the slash is collected into 8

numerous large piles near the landing areas and roadways where they are burned onsite between 9

Year 3 and Year 7 post-harvest. This harvesting process removes most of the overstory and 10

understory vegetation, thereby removing nutrients and canopy cover from the area as well. Soil 11

compaction from heavy machinery, as well as from the slash piles also results. At the time of 12

this study, the Power Pole sale area was the most disturbed and had the lowest abundance of 13

understory vegetative cover compared to Dark Canyon and Thrall (Nancy Grulke, personal 14

communication, 2017). 15

Conversely, Mercedes is a conventional harvest site (Jack Butler, personal communication, 16

2015) in which the above-ground portion of selected mature trees are cut, de-limbed on the 17

ground, and the trunks then transported to landing areas where they are loaded on a truck. The 18

branches, twigs, and other woody debris are left behind on the forest floor to decompose. If 19

slash is collected onsite, the piles are smaller and fewer in number than on whole-tree harvest 20

sites. The benefits of this approach are that it is selective, leaving behind intact vegetation and 21

some canopy cover, it ensures regrowth from the remaining trees and the nutrients from the slash 22

are left at the harvest area. Many times, however, the process of accessing the selected trees with 23

7

heavy logging equipment can damage the surrounding trees and vegetation, compact soils, and 1

create large holes in the canopy cover that fragments the habitat. 2

The magnitude of soil disturbance, vegetation loss, soil compaction, and loss of species 3

abundance and diversity varies at each site depending on the forest practices implemented. 4

Whole-tree removal has been linked in numerous studies to reductions in soil nutrients and 5

moisture (Vanguelova et al. 2010), changes in seed bank composition, soil biota (Bird and 6

Chatapaul 1986), and declines in local songbird populations (Lohr et al. 2002). It has also been 7

linked to decreased forest productivity and timber regeneration rates compared to nearby 8

conventional harvest sites (Roxby et al. 2015). Whole-tree removal sites have also been found to 9

have higher incidences of NIS and noxious weed invasions than conventionally harvested sites 10

(Buckley et al. 2003; Harrington and Schoenholtz 2010; Slesak et al 2011). 11

12

Invasive Species in the Black Hills National Forest 13

Noxious weeds grow aggressively and reproduce quickly without natural controls on their 14

proliferation, causing adverse effects on other species, as well as changes to the physical and 15

chemical environment. Their invasion is characterized by four distinct stages that occur over 16

temporal and spatial scales: entry, establishment, spread, and impacts (Andersen et al. 2004). 17

Establishment and spread follow classic metapopulation and patch dynamic models for invasive 18

species in which patches of established or in the process of being established NIS and noxious 19

weeds are sources from which nearby patches are colonized (Andersen et al. 2004; Deines et al. 20

2005; Landis 2004; Lenda et al. 2010). These processes can also co-occur, and have been 21

modeled in a number of contexts (Deines et al. 2005, Gallien et al. 2010, Lenda et al. 2010). 22

8

Impacts to the receiving environment depend on the rate of biological invasion, the total 1

vegetative cover of the established invasive species, its competitiveness for resources (water, 2

nutrients) and space relative to native and non-native plant communities, and site-specific 3

environmental conditions (Andersen et al. 2004; Deines et al. 2005; Kerns and Ager 2007; 4

Landis 2004; Sikder et al 2006; Stohlgren et al. 2010). They therefore pose significant threats to 5

forests and rangelands managed by the USFS. 6

7

Canada Thistle Assessment Endpoint 8

In this invasive species risk assessment, Cirsium arvense, Canada thistle, was selected as the 9

indicator species representing all deleterious invasive weeds in the BHNF due to its classification 10

as both an NIS and noxious weed, as well as its ubiquitous presence throughout the BHNF 11

(USDA 2016). It is a perennial broadleaf weed that originated in Europe and has since spread 12

throughout most of the United States and Canada (Becker et al. 2008; Moore 1975; Zouhar 13

2001). It is commonly found in disturbed areas, primarily from human activities, along roads 14

and road buffers, streambanks, and ditches, at clearcut and timber harvest sites, and recreational 15

areas (Becker et al. 2008; Heimann and Cussans 1996, Zouhar 2001). Once established, it has 16

been shown to significantly decrease native plant cover and plant species diversity (Zouhar 17

2001). 18

New Canada thistle plants develop from vegetative buds in the parent plant’s root system or 19

from seeds. Locally, it propagates rapidly through an extensive root system, enabling its prolific 20

spread from established patches to adjacent disturbed areas (Donald 1994, Zouhar 2001). Each 21

plant can colonize an area from 1 to 2 m yr-1 and, once fully established, its horizontal roots can 22

extend more than 4.5 m out from the plant with its vertical roots extending as deep. It also 23

9

produces seeds that can be dispersed by wind, water, attached to animals, humans, vehicles, and 1

via contaminated crop seed to colonize more distant areas (Beck 2013; Becker et al. 2008; 2

Heimann and Cussans 1996; Zouhar 2001). The seeds tend to grow slowly and are sensitive to 3

competition, but can remain viable in the soil for up to 22 years (Beck 2013). Effective control 4

of Canada thistle is therefore a multifaceted and frequently unsuccessful process (Beck 2013). 5

Given its potential for establishment and proliferation, the probability of Canada thistle 6

establishment changes over spatial, as well as temporal scales. In forested areas, the potential for 7

its establishment and spread starts with the disturbance event (e.g., timber harvesting) and 8

changes over the span of decades through the process of overstory and understory vegetative 9

growth, and secondary succession (Zouhar 2001). Therefore, an invasive species risk assessment 10

must consider the risk of NIS and noxious weed establishment or spread across the landscape on 11

both short, as well as long-term time scales. 12

13

Study Objectives 14

The objectives of this study were to evaluate the short and long-term effects of timber 15

harvest, as well as different types of timber harvest methods on understory vegetation 16

responses in the Black Hills National Forest in relation to: 17

1. Canada thistle establishment and, by association other NIS and noxious weed species, 18

within the harvested sale units in a timber sale area, and 19

2. The probability that Canada thistle will spread to other sale units from those harvested 20

units. 21

The goal was to provide the USFS the information they need to prioritize management actions in 22

the BHNF that are effective both spatially and temporally in controlling the establishment and 23

10

spread of Canada thistle. The use of the RRM-BN approach also provided the USFS with an 1

adaptive management tool that enables them to track changes in Canada thistle risk as 2

management actions are implemented (USFS 2010). 3

4

METHODS 5

6

In this study, the Bayesian network - Relative Risk Model (BN-RRM) framework developed by 7

Landis et al. (2010) was used to conduct the invasive species risk assessment. The first step in 8

the process involved consulting with the USFS Western Wildland Environmental Threats 9

Assessment Center and USFS Rocky Mountain Research Station site managers for the BHNF 10

regarding available data. 11

The USFS began establishing permanent sample plots within sale units in timber sale areas 12

starting in 2007 and continuing in 2008, 2010, and 2011. The understory vegetation in all plots 13

were evaluated for species richness, percent cover, and frequency prior to timber harvest (Time 14

0), and then re-evaluated 1-year post-harvest (Time 1), 2-years post-harvest (Time 2), 3-years 15

post-harvest (Time 3), and 7-years post-harvest (Time 7). An additional series of plots were 16

established in undisturbed areas adjacent to the timber sale (Undisturbed). Species richness 17

(native and NIS) was determined in 1,000 m2 macroplots, whereas species foliar cover and 18

frequency were measured in twelve 1 m2 plots randomly placed within each macroplot (Jack 19

Butler, personal communication, 2015). 20

The understory vegetation data were the most complete for sample plots in Dark Canyon, 21

Thrall, Mercedes, and Power Pole for Year 0, Year 1, and Year 3, and thereby defined the spatial 22

and temporal parameters used in this invasive species risk assessment. Year 0 pre-harvest data 23

11

provided initial vegetative cover conditions, Year 1 post-harvest data reflected the phase of rapid 1

recolonization, and Year 3 data captured the successional phase as native species were 2

reestablished and began to outcompete early NIS invasions. The quantity and quality of the data 3

for these timber sale areas over the three timeframes also helped to reduce uncertainty in the risk 4

calculation phase of the assessment. 5

6

Conceptual Model 7

The first step in the RRM framework was the development of a conceptual model depicting the 8

pathways by which Canada thistle establishment or spread could occur in the BHNF due to 9

forestry activities (Fig. 2). The model was developed in consultation with the stakeholders to 10

accurately identify and define the relevant direct and indirect factors that affect Canada thistle 11

establishment after timber harvest, as well as known causal interactions and relationships, 12

cumulative effects, and deleterious impacts. The resulting cause-effect structure of the model 13

consisted of four categories: Sources of Stressors, Stressors (generated by one or more of the 14

sources), Effects, and Impacts. 15

The sources of stressors in the conceptual model (Fig. 2) reflect the disturbance events post-16

harvest and were categorized into three separate types of sources: 1) Direct Sources (top three 17

boxes of the model) through the creation of roads, increased percent disturbed area, and slash 18

piles, 2) Indirect Sources (middle three boxes of the model) through the reduction or elimination 19

of native and exotic overstory and understory vegetation, and 3) Locational Sources (bottom 20

box) that affect Canada thistle dispersal and distribution as a function of proximity to other 21

disturbed areas. Each were also defined in terms of quantifiable units based on site-specific data. 22

12

For example, roads were identified as direct sources of stressors and were defined as the “percent 1

cover of road in the timber sale area, including a 25 m buffer on each side of the road” (Table 1). 2

The stressors generated from the three types of sources included Soil Compaction, Exposure 3

of Bare Soil, Burned Slash and Biomass, and Overstory Vegetation/Understory Vegetation 4

Relationship (Fig. 2). These stressors may act independently, overlap and interact partially, or 5

interact completely with each other to generate a range of effects that result in the impact, i.e., 6

Canada thistle establishment in this model. Intermediate Effects boxes in the model describe the 7

effects caused by the stressors, i.e., Changes in Soil pH, Changes in Available Nutrients, 8

Changes in Soil Biota, and Changes in Seed Bank. They summarize the stressor interactions and 9

link to the endpoint as a Chemical Modification, Biological Modification or Ecological Structure 10

Modification. The arrows linking the boxes in the model represent causal pathways and 11

relationships between each component. 12

13

Bayesian Network - Relative Risk Model 14

Once completed, the conceptual model provided the framework to construct the BN-RRM (Fig. 15

3). Each box in the conceptual model became a node in the BN. The sources of stressors 16

became the input (parent) nodes, stressors and effects became intermediate (child) nodes, and the 17

endpoint became the final (child) node. Arrows in the conceptual model, which represented 18

cause and effect relationships, were converted in the BN to probability relationships known to 19

exist between those two nodes. 20

Guidelines developed by Hosack et al. (2008) and Marcot et al. (2006) were used to develop 21

the BNs for each timber sale area for Year 0, Year 1, and Year 3. The methods described by 22

Marcot (2012) were used to evaluate each BN once it was completed. NeticaTM software 23

13

(Norsys Software Corp. 2014) was used to construct the BNs, as well as to calculate the relative 1

risks and evaluate the risk results. Using this approach, a total of twelve Bayesian networks were 2

created. 3

4

Data to Inform the BN-RRM 5

The BN-RRM framework uses site-specific data to parameterize the nodes (variables) and the 6

relationships between them. In this study, the sources of data were obtained from the scientific 7

literature, governmental reports, USFS monitoring data and spatial files, or a combination 8

thereof. Parameterizing the BN nodes was a three-step process that first entailed establishing a 9

ranking scheme for each node, using the site-specific data to set the risk probability distributions 10

of states (ranks) in each node, and then defining and quantifying the relationships between nodes 11

in conditional probability tables (CPTs). The following methodology was used: 12

13

1. Set a ranking scheme for each node. 14

First, each node variable in the BN was discretized into three or four states, or ranks using a zero, 15

low, medium, and high ranking scheme. Each state was then assigned a numerical rank 16

developed by Landis and Wiegers (2005). Three-state nodes had ranks of 0 = Zero, 3 = Medium, 17

and 6 = High, whereas four-state nodes had ranks of 0 = Zero, 2 = Low, 4 = Medium, and 6 = 18

High. These ranks were used later in the risk calculation phase of the risk assessment. For some 19

nodes, three states were preferable to more accurately reflect natural breaks in the data for that 20

variable or management decisions (Table 1). 21

22

2. Set the probability distribution for each input node. 23

14

Each state in the node was then populated with site-specific monitoring data (e.g. vegetation 1

surveys) or spatial data (e.g. road and slash pile locations) for that node. For example, 2

monitoring data from vegetation surveys for each sample plot within a timber sale areas provided 3

the percent canopy cover data for the Overstory Vegetation node in the BN. For example, in 4

Dark Canyon thirteen plots (59.1%) had a canopy cover of less than 25% (Fig. 4). Once all 5

states in the node are populated with data, they define a unique probability distribution curve 6

(Fig. 4) that is different for each timber sale area and year, depending on the data for that site and 7

time period. 8

Site specific monitoring data from vegetation surveys were used to set the probability 9

distributions for the vegetation nodes (Overstory Vegetation, Understory Native Vegetation, and 10

Understory Exotic Vegetation). The data were collected from sampling locations within each of 11

the timber sale areas at Year 0, pre-harvest, Year 1, post-harvest and Year 3, post-harvest. 12

Spatial data extracted from USFS GIS shapefiles were used to set the probability distribution 13

curves for the location and percent cover of Roads, percent Disturbed Area, the locations and 14

sizes of Slash Piles and Scars, and Proximity (of sale areas) to Other Disturbed Areas nodes. 15

Metadata for the GIS shapefiles provided additional information on the sources of the data and 16

years the data were collected. 17

The probability distribution curve for the Disturbed Area node for each timber sale area and 18

year was determined from GIS format aerial images for each year from 2008 to 2013. As 19

vegetative regrowth occurred after logging, changes in percent cover of a disturbed area were 20

compared from Year 0 to Year 1 and Year 3. The images were also compared to the GIS 21

shapefiles to confirm the location of roads, slash piles, and other anthropogenic or natural 22

landmarks within the sale areas. 23

15

1

3. Complete a conditional probability table for each intermediate and endpoint node. 2

Lines or arrows connecting nodes in the BN were based on known cause-effect pathways and 3

were derived directly from the conceptual model. In the BN, each line connecting two or more 4

input nodes to an intermediate node indicated causal relationships between them. The 5

relationships are described probabilistically and can be direct P(BA), indirect P(BA), P(CB), a 6

shared cause P(BA), (P(CA) or shared effect (P(CA,B). Conditional probability tables (CPTs) 7

were then constructed to quantify the causal relationships and calculate the probability 8

distributions in the intermediate node. 9

CPTs can be completed using a variety of methods, depending on the data available. These 10

methods can be divided into four categories: expert judgment, empirical evidence, calculations 11

from mathematical or biological equations, and case file learning (Chen and Pollino 2012; 12

Marcot et al. 2006; Pollino et al. 2007). In this study, three different methods for creating the 13

CPTs were used and were based on the available site-specific data: empirical evidence, case file 14

learning, and a combination of empirical evidence and mathematical calculations (Table 2). 15

16

Risk Calculations 17

Once the CPTs and input probabilities were completed, NeticaTM was used to update the 18

probability of risk of Canada thistle establishment in the intermediate nodes, including the 19

summary nodes, and the final endpoint node using probabilistic inference (Norsys Software 20

Corp. 2014). Probabilistic inference is the process of determining the posterior probability of 21

one or more variables taking a specific value or set of values based on the value of the input 22

variable(s). The final result was a risk distribution for the Canada thistle establishment endpoint 23

16

(Fig. 3), which is also referred to as the posterior probability distribution (PPD). In addition to 1

the PPD, Netica calculated the risk score for each intermediate node and the endpoint node, 2

which is the mean of the risk probability distribution ranks in that node (Fig. 4). Risk scores 3

range from 0 (zero risk) to 6 (high risk), derived from the numerical rank assigned to the states in 4

that node. Risk scores can be similar in value to other risk scores in the BN, however, the scores 5

reflect different risk probability distributions. 6

Risk scores facilitate the communication of general trends in risk, whereas risk probability 7

distributions are useful for conveying specific information about patterns of risk and comparing 8

differences in risk by area or by year. There is no assumption of a normal, bell-shaped 9

distribution of the states within a node; rather, the distribution reflects the actual probability of 10

those states to occur based on the model’s calculations. Differences between the distributions 11

provide information about the probability of risk and the associated uncertainty. 12

In total, risk scores were calculated for the four timber sale areas for Year 0, Year 1, and 13

Year 3. These risk results can be used to compare risk over space (by timber sale risk region) 14

and time (by year). Additionally, risk scores were summed to compare total risk by year (all 15

regions). 16

17

Model Evaluation 18

After completing the BNs and calculating risk, the models were evaluated using two common 19

approaches related to uncertainty analysis in ecological risk assessments: sensitivity analysis and 20

influence analysis (Marcot 2012; Pollino et al. 2007). 21

22

17

Sensitivity Analysis 1

Sensitivity analysis explains the extent to which the endpoint node is influenced by the values of 2

the input nodes (Hines and Landis 2014; Marcot 2012; Pollino et al. 2007). Results of the 3

sensitivity analysis can then be used to evaluate the model structure, interpret the risk results, and 4

provide further information to the risk managers as to the sources of risk to the endpoint (Marcot 5

2012; Pollino et al. 2007). 6

A sensitivity analysis was performed on each of the twelve BNs using the NeticaTM 7

Sensitivity to Findings tool. The tool reports a measurement of mutual information between 8

nodes, meaning how much one random variable tells us about another, i.e., their mutual 9

dependence (Norsys Software Corp. 2014, Pollino et al. 2007, Woodberry et al. 2004). A high 10

value of mutual information for an input indicates a greater degree of influence on the endpoint 11

node (Hosack et al. 2008, Marcot 2012). Mutual information is a function of both the findings in 12

the node (probability distributions) and the relationships described in the CPT (Marcot 2012, 13

Norsys Software Corp. 2014). 14

To facilitate interpretation of the results, sensitivity analysis was divided into two parts: 1) 15

sensitivity to model inputs and 2) sensitivity to stressor-exposure pathways. These analyses were 16

run simultaneously, though single nodes can be analyzed independently without changing the 17

sensitivity results. 18

19

Influence Analysis 20

To further evaluate the models, an influence analysis was conducted on each of the BN-RRMs 21

following the methods described by Marcot (2012). An influence analysis provides information 22

on the possible range of risk. In this approach, input variables were set to their maximum or 23

18

minimum states and resulting changes in the distribution of risk were compared. Influence 1

analysis results were used to understand the minimum and maximum limits of risk relative to the 2

risk results calculated in the model. It also provided information when applied to theoretical 3

scenarios in which input values were set higher or lower than measured values (Marcot 2012). 4

5

RESULTS 6

7

Risk Scores 8

The risk of Canada thistle establishment was low to medium in all harvested areas for all three 9

years (Table 3), with risk scores ranging from 2.48 (Mercedes, Year 0) to 3.21 (Power Pole, Year 10

3) on a scale of 6, with 6 being the maximum risk. In all timber sale areas, the risk was lowest in 11

Year 0 and increased after logging in both Years 1 and 3. The greatest risk occurred in the first 12

or third year after logging, depending on the timber sale area. In Thrall and Mercedes, risk was 13

higher in Year 1 than in Year 3, whereas in Power Pole, risk increased from Year 1 to Year 3 14

(Table 3). 15

The pattern of risk also varied by timber sale area. Thrall had the highest risk of all four sale 16

areas in Years 0 and 1 (Table 3), whereas Power Pole had the highest risk score in Year 3, but 17

the lowest risk score in Year 1. Dark Canyon had the second highest risk score all three years. 18

Mercedes had the lowest risk score in Years 0 and 3 and the second lowest in Year 1. 19

20

Risk Probability Distributions 21

Risk probability distributions were skewed in the Low or Medium states for all timber sale areas 22

and years (Fig. 5). There was a 38.8% probability of Low risk in Dark Canyon in Year 0 and a 23

19

40.8% probability of Medium risk by Year 1 (Table 3). Low risk was the most probable state in 1

Year 0 for all areas except Thrall, where Medium risk was more probable. Medium risk was the 2

most probable state in Year 1 for all areas except Power Pole, where Low risk was more 3

probable. Medium risk was the also the most probable state in Year 3 for all areas except 4

Mercedes, where Low risk was more probable. When combined, Low and Medium risk 5

probability distributions accounted for 71.8- 77.5% of the risk, depending on the timber sale area 6

and year. 7

Though the probability of risk was skewed towards the Low and Medium states, the risk 8

distribution tails provided additional information about the probability of extreme events (Zero 9

or High risk). For example, risk was Low to Medium in Thrall, however there was still an 11.6% 10

to 15.9% probability of High risk in Year 0 and Year 1 (Table 3). During Year 1, there was an 11

8.3 to 15.9% probability of High risk depending on the timber sale area. Similarly, in Year 3 12

there is an 8.1 to 13.8% probability of High risk. 13

14

Sensitivity Analysis 15

Sensitivity to Input Parameters 16

Understory Native Vegetation had the greatest influence on risk of Canada thistle establishment 17

in most timber sale areas and years (Fig. 6). Understory Exotic Vegetation and Overstory 18

Vegetation were other contributors of risk primarily in Year 0. In Years 1 and 3, Understory 19

Native Vegetation and Disturbed Area were the primary contributors of risk. Slash Piles and 20

Roads were not influential variables in any of the models. 21

In the specific timber sale areas, Understory Native Vegetation had the greatest influence on 22

risk in Thrall, Mercedes, and Power Pole all three years. Overstory Vegetation had the greatest 23

20

influence on risk in Dark Canyon in Year 0 and Year 3, and was the second most influential 1

variable in Mercedes in Year 3. In Power Pole, mutual information values were lower overall 2

than for the other timber sale areas, indicating that vegetative cover did not influence Canada 3

thistle establishment as it did for the other areas. 4

5

Sensitivity to Stressor-Effect Pathways. 6

The Canada thistle endpoint node was most sensitive to input from the Ecological Structure 7

Modification effect node in all timber sale areas for all years, but especially in Year 1 (Fig. 7). 8

The specific stressors that contributed the most to the Ecological node in Year 1 were logging 9

disturbance (Exposure to Bare Soils node) and changes in vegetation post-logging 10

(Overstory/Understory Vegetation Relationship node) (Fig. 7). The Canada thistle endpoint was 11

less sensitive to inputs from the Chemical Modification and Biological Modification effect 12

nodes. By Year 3, this sensitivity decreased further due to the burning of slash piles on the 13

timber sale areas. The exception to this trend was in Power Pole, where the greatest sensitivity 14

to Biological Modification occurred in Year 3. 15

The Proximity to Other Disturbed Areas node had an equal or greater influence on the 16

Canada thistle endpoint than the Biological Modification or Chemical Modification nodes in 17

many timber sale areas (Fig. 7). This is an important result of the sensitivity analysis because the 18

Proximity node is a single, source of stressor input variable, whereas the Biological and 19

Chemical nodes summarized the effects of multiple input variables. As such, a much greater 20

sensitivity to the Biological and Chemical Modification nodes would be expected if all of the 21

inputs had an equal effect on the endpoint. The Proximity node, however, had mutual 22

information values ranging from approximately 0.05 to 1.2, which were larger than the mutual 23

21

information values for any other single source of stressor input node. The Canada thistle 1

establishment endpoint is therefore highly sensitive to the location of the timber sale area relative 2

to other disturbed i.e., primarily logged areas. 3

4

Influence Analysis 5

The influence analysis compared the calculated (Most Likely) risk results to hypothetical 6

scenarios for Minimum and Maximum risk, based on changes to the input nodes. To conduct the 7

analysis, input node probability distributions were changed to 100% probability in the Zero state 8

for the Minimum risk scenario and to 100% probability in the High state for the Maximum 9

scenario (Fig. 8). The resulting risk probability distribution in the endpoint under the initial 10

conditions of the Minimum risk scenario was skewed, as expected towards Zero risk, and 11

similarly skewed towards High risk in the Maximum risk scenario. The calculated Most Likely 12

risk results were approximately in the middle of the risk range bracketed by the results from the 13

Minimum and Maximum scenarios. Regardless of initial input conditions, however, the 14

influence analysis indicated very little difference between Minimum and Maximum risk scenario 15

results for all timber sale areas. There were slight differences between Years 0, 1 and Year 3, 16

however they were due to differences in the CPTs that represented changing stressor effects or 17

interactions. For example, a soil erosion event in Year 1 will alter soil nutrients in both Year 1 18

and Year 3, though to different extents. 19

20

21

22

22

DISCUSSION 1

Risk Estimates by Year 2

Risk of Canada thistle establishment changes over time, with greatest risk occurring in either the 3

first or third year after logging. Risk was present even before logging activities occur in Year 0, 4

but increased after logging in Year 1. 5

In Year 0, the risk of Canada thistle establishment was dependent on three sources of 6

stressors: Roads (and associated human activity), Overstory and Understory Vegetation, and 7

Proximity to Other Disturbed Areas. Roads exist across the BHNF landscape due to current and 8

historical logging activities throughout the region. The roads have been maintained to some 9

extent over time, resulting in periodic disturbances of the soil and surrounding vegetation to keep 10

these corridors open (Jack Butler, personal communication, 2015). The BHNF aerial images and 11

road layers showed that the percent cover of roads changed very little in the 3 to 5-year 12

timeframe evaluated in this study. These corridors and associated human activities provided 13

opportunities for facilitated Canada thistle seed dispersal and root propagation (Forman and 14

Alexander 1998, Fowler et al. 2008). 15

Secondly, the presence or absence of Overstory and Understory Vegetation in Year 0 prior to 16

or after logging inhibited or promoted, respectively, the risk of Canada thistle establishment. 17

Vegetation presence or absence was a function of a number of factors including previous logging 18

history and site conditions (e.g. soil type, soil disturbance, other natural disturbances - 19

landslides). 20

Finally, the Proximity to Other Disturbed Areas increased the risk of Canada thistle 21

introduction and establishment through seed dispersal and root propagation. This risk was 22

present even before logging occurred in a timber sale area. Canada thistle seeds can be 23

23

transported by wind, animals, and human activity (Heimann and Cussans 1996). Areas in closer 1

proximity to established Canada thistle patches, therefore, will be at a higher risk for the 2

introduction and spread of Canada thistle not only by seed dispersal, but by root propagation as 3

well. These pre-harvest results indicated that to mitigate the risk of Canada thistle establishment, 4

actions must be implemented even before harvest occurs. 5

In Year 1, the increased risk of Canada thistle establishment could be attributed to the direct 6

and indirect effects of logging and harvest practices. Logging and harvest practices directly 7

altered ecological structure by increasing the percent area of disturbed land and removing the 8

percent cover of native and exotic understory vegetation. As overstory trees and understory 9

vegetation were removed, canopy cover was eliminated, creating large swaths of bare soil that, 10

coupled with areas of increased sunlight and little understory competition, provided ideal 11

conditions for Canada thistle establishment (Parendes and Jones 2000; Wilson Jr. 1979; Zimdahl 12

et al. 1991). As a result, the risk of thistle establishment due to Proximity to Other Disturbed 13

Areas persisted from Year 0 through Year 1 as large areas of exposed soils were created. 14

Risks posed by the roads persisted from Year 0 through Year 1 as well. Logging activities 15

resulted in increased utilization of roads to transport harvesting machinery to the timber sale site 16

and harvested logs offsite, as well as personnel to and from the site each day. The increased 17

traffic, as well as reopened and maintained roads and buffers provided vectors by which Canada 18

thistle seed transport would be facilitated into deforested areas. In terms of total area within the 19

timber sale areas, however, roads comprised a much smaller percentage of disturbed areas 20

compared to harvested areas, and therefore posed an overall lower risk. 21

The indirect effects of logging included changes to the chemical and biological composition 22

of the soil. Soil compaction from forestry equipment and log transport can lead to soil nitrogen 23

24

losses and decreases in soil nitrification due to suppressed microbial activity (Gomez et al. 2002; 1

Neve and Hofman 2000). Bare soils are prone to erosion, which can also lead to losses in soil 2

moisture, organic matter, nitrogen, and phosphorus (Gyssels et al. 2005; Munn 1973). 3

In Year 3, changes to the landscape both increased and decreased risk compared to Year 1. 4

First, most slash piles in the BHNF are burned three years post-logging. The effects on bare soil, 5

soil pH, soil biota, and soil nutrients increased the risk of Canada thistle establishment. Burning 6

sterilizes the soil and kills all soil biota beneath the slash piles, as well as up to several meters 7

from the burn site, depending on the size and composition of the slash pile. It also destroys or 8

makes inviable any native plant overstory or understory plant seeds in or near the burn site 9

(Abella et al. 2007; Creech et al. 2012; Halpern et al. 2014; Haskins and Gehring 2004; Korb et 10

al. 2004; Thompson et al. 1996). Farther away from the slash pile, the effects of burning may 11

include short-term releases of carbon and nitrogen into the soil, though these effects do not 12

persist 3-5 years after the burn (Esquilin et al. 2007; Monleon et al. 1997). The more slash piles 13

on a site, like those found on whole-tree harvest sites (i.e., Dark Canyon, Thrall, and Power 14

Pole), the greater the deleterious ecological effects on the site that, in turn, facilitates Canada 15

thistle establishment. 16

Second, the first successional stages of native understory and overstory regrowth after the 17

timber harvest alleviated some of the risk of Canada thistle establishment. As the understory 18

regrew after harvest, Canada thistle competed with other plants for sunlight and nutrients, 19

thereby inhibiting some of its prolific spread. In fact, understory vegetation is often denser after 20

the canopy cover is removed due to the greater amount of sunlight reaching the forest floor 21

(Zouhar 2001). Risks from the Proximity to Other Disturbed Areas and, to a lesser extent, the 22

Roads variables persisted into Year 3, even as revegetation occurred in the logged areas. 23

25

1

Risk Estimates by Timber Sale Area 2

The risk of Canada thistle establishment differed between timber sale areas and was influenced 3

primarily by three factors: 1) logging management practices used on the site, 2) site vegetation 4

characteristics, and 3) proximity to other disturbed areas. The location of the timber sale in the 5

study area was of secondary importance. Dark Canyon and Thrall, located adjacent to each 6

other, did have some similarities in their patterns of risk, whereas Power Pole, located farthest 7

away (approximately 35 km to the northwest), and Mercedes, located between Thrall and Power 8

Pole did not. 9

10

Logging Management Practices 11

Mercedes had the lowest risk of the four timber sale areas and was the only site on which 12

conventional logging practices were used. Few if any slash piles are made using this method and 13

at the time of this study, Mercedes had one slash pile that was burned in Year 3, which greatly 14

reduced the probability of changes in soil biota and chemistry on the site. 15

Conversely, Dark Canyon, Power Pole and Thrall timber sale areas were harvested using the 16

whole-tree method. The slash left behind after harvesting was stacked in large piles at landing 17

sites in each area and burned in Year 3. At the time of this study, there were nine to twenty-one 18

slash piles associated with the Dark Canyon, Thrall, and Power Pole timber sale areas. The 19

probability of deleterious effects to ecological structure on these sites was therefore substantially 20

higher due to the extent of deforestation and impact on the sites once all the large slash piles 21

were burned. These impacts set up conditions favorable for Canada thistle colonization, 22

increasing the probability of its establishment at these sites. 23

26

1

Site Vegetation Characteristics 2

Understory Native Vegetation, Understory Exotic Vegetation, and Overstory Vegetation 3

accounted for many of the differences in the model inputs for the four timber sale areas. These 4

variables were influenced by a number of factors. First, logging removed understory and 5

overstory vegetation in each of the sale areas. Second, the percent that was logged, as well as 6

regrowth rates of the vegetation after logging activities differed between them. In these timber 7

sale areas, the relative percent cover of understory exotic vegetation increased after logging in 8

Years 1 and 3, whereas the relative percent cover of understory native vegetation increased only 9

the first year after logging and decreased by Year 3. This decrease was likely due to competition 10

with exotics, as well as shading effects from the regrowth of overstory vegetation by Year 3. 11

Unfortunately, data were lacking for overstory vegetation in Year 3, which increased the 12

uncertainty in the risk estimates for that timeframe. 13

In the Dark Canyon timber sale area, the understory native vegetation cover decreased from 14

Year 1 to Year 3 in most sample plots. The exotic understory vegetation, however, remained 15

relatively constant at less than 30% cover (Jack Butler, personal communication, 2015). In both 16

Power Pole and Thrall, the understory native vegetation percent cover varied greatly between 17

sample plots spatially, but remained relatively the same in each temporally between Years 1 and 18

3. The understory exotic vegetation increased in some sample plots by Year 3, with percent 19

cover reaching nearly 60 to 80% in some. 20

In Mercedes, the understory native and exotic vegetation regrowth by Year 1 was less rapid 21

with percent cover similar to, but not more than the pre-harvest percent cover. By Year 3, 22

however, percent cover of understory native species had decreased in all sample plots, whereas 23

27

understory exotic species increased by Year 3, but did not exceed 50% cover in the timber sale 1

area. 2

3

Proximity to Other Disturbed Areas 4

Disturbed areas in each of the timber sale areas were considered to have established patches of 5

Canada thistle that served as sources from which newly harvested sale units would be invaded. 6

As such, risk of Canada thistle introduction and establishment was present in each timber sale 7

area even before logging occurred. The distance of the harvested site from the disturbed area(s) 8

determined the dominant dispersal mechanism of Canada thistle, thereby affecting its risk of 9

introduction and establishment. Harvested areas (sale units) within 10 m of disturbed areas 10

would most likely be colonized rapidly by direct root propagation, and therefore be at highest 11

risk. 12

At distances between 10 and 250 m, introduction of Canada thistle was considered more 13

likely to be a result of seed dispersal by wind, animal and/or human activity (Becker et al. 2008; 14

Donald 1994; Zouhar 2001,). Seeds, however, are not the most effective or fastest means by 15

which Canada thistle reproduce, requiring favorable physical and environmental conditions the 16

following season to germinate and grow. The risk of establishment via this dispersal mechanism 17

was therefore, considered medium. At distances greater than several hundred meters (250 - 1000 18

m), wind dispersal of seeds was considered minimal due to the increased frequency of pappus 19

detachment from the seed with increasing distance from the plant. As such, chance transport of 20

the seed by animals and/or humans becomes the primary mechanism of dispersal (Zouhar 2001), 21

resulting in low risk of Canada thistle introduction and establishment. 22

28

All four timber sale areas in the study area had harvested sale units within the 50 to 250 m 1

distance to other disturbed areas and were therefore considered at medium risk. Power Pole and 2

Thrall, however, had a greater percentage of sale units within 10 m of other disturbed areas, 17 3

and 15.5 %, respectively compared to Dark Canyon and Mercedes, putting them at medium to 4

high risk. Dark Canyon had 30% of its sale units located at least 250 m from a disturbed area, 5

putting it at medium to low risk. Interestingly, Mercedes had the lowest percentage of sale units 6

(1%) located at least 250 m from a disturbed area and therefore should have been at higher risk. 7

These results indicated that, although proximity is an important contributor of risk, other factors 8

may be more important in determining risk or lack thereof. In the case of Mercedes, 9

conventional logging practices reduced exposed barren soils and number of slash piles that 10

potentially hindered Canada thistle establishment and growth. Conversely, of the three whole-11

tree harvested timber sale areas, Power Pole had the highest percentage of disturbed areas and 12

the least amount of understory vegetation in Year 1, contributing to it having greater Canada 13

thistle invasion in Year 3 than Dark Canyon and Thrall. 14

15

Model Evaluation 16

Sensitivity analysis. Sensitivity analysis was used to evaluate the extent to which the Canada 17

thistle establishment was influenced by forest management practices and environmental 18

variables. The sensitivity analysis results explained which of these variables contributed to or 19

alleviated the risk. 20

Sensitivity analysis indicated that a primary driver of risk in the Canada Thistle 21

Establishment node was the Proximity to Other Disturbed Areas node. As the distance between 22

a timber sale and other disturbed areas decreased, the risk of Canada thistle introduction and 23

29

establishment via seed dispersal through wind, wildlife, and human activity increased. 1

Moreover, the mechanism of dispersal influenced the dispersal distance (Heimann and Cussans 2

1996). The ability of Canada thistle to disperse viable seeds decreases with distance from the 3

source population, resulting in an inverse relationship between the likelihood of Canada thistle 4

colonization and distance from the source patch. 5

Influence analysis. The results of influence analysis were used to bracket the range of 6

possible risks of Canada thistle establishment in the timber sale areas of the BHNF. More useful 7

however, is using the model in an adaptive management process to assess whether risk is 8

increased or reduced with the implementation of a management action by resource managers. 9

For example, the model could be used to determine whether risk would increase if an area near 10

one of the timber sale areas was logged, i.e., changing the Proximity to Other Disturbed Areas 11

node, or whether changing the number and size of slash piles (Slash Pile and Scars node) in a 12

timber sale area would alter the risk. The model could also be used to determine whether risk 13

would decrease if rapidly growing non-invasive species were planted post-harvest to expedite 14

understory/overstory vegetative regrowth rates. 15

16

Patterns of Risk over Time 17

The risk of Canada thistle establishment changed over time. Logging activities and harvest 18

practices, including road building and slash pile burning, altered the landscape and increased the 19

risk of Canada thistle establishment in the four timber sale areas. Post-harvest, successional 20

changes occurred in the landscape that at first promoted Canada thistle establishment, but over 21

time decreased the risk of further establishment and spread. The regrowth of understory and 22

overstory vegetation changed soil physical and chemical properties, as well as reduced habitat 23

30

fragmentation and increased competitive exclusion of Canada thistle. These results are not 1

unique. The risk of biological invasion is expected to change with time, however, the models 2

used in this study identified how variables were changing over time and how these variables 3

influenced risk each year. Moreover, data collected during the post-harvest monitoring phase 4

could be used as inputs in the model to reevaluate whether one or more invasive species 5

management actions reduced or increased risk. 6

In conclusion, there were three key findings of this research: 1) the risk of Canada thistle 7

establishment was present even before logging activities occurred, 2) the greatest risk occurred in 8

the first and third year after logging, depending on initial site (soils and vegetative cover) 9

conditions pre-harvest, and 3) risk differed between sale areas and was strongly influenced by 10

both logging management practices and proximity to other disturbed areas in the landscape. As 11

management actions are considered or implemented to mitigate the risk of Canada thistle 12

establishment and spread in the BHNF, the BN-RRM models for each timber sale area can be 13

updated in the adaptive management process to assess the potential or actual changes in risk. 14

The BN-RRM framework can also be applied to assess risk of invasion by other noxious weeds 15

or non-indigenous species at other U.S. Forest Service-managed sites. 16

17

31

Acknowledgements 1

This research had been a joint partnership between the U.S. Forest Service (USFS) and the 2

Institute of Environmental Toxicology at Western Washington University. Special thanks to 3

Nancy Grulke, Director of the USFS Pacific Northwest Western Wildland Environmental 4

Threat Assessment Center for her support and funding, and to Jack Butler at the Rocky 5

Mountain Research Station Forest and Grassland Research Laboratory for providing us with 6

timber sale area monitoring data, spatial data in the form of GIS shapefiles, and aerial images in 7

the Black Hills National Forest study area. The modeling and risk analyses would not have been 8

possible without these data. 9

10

Funding 11

Funding was provided by the United States Forest Service’s Pacific Northwest Research 12

Station’s, Western Wildland Threat Assessment Center [Joint Venture Agreement No. 12-JV-13

11261904-024]. 14

15

32

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37

Table 1. Parameterization of model inputs for all sources of stressors.

Input

Variable Definition Ranking Justification References

Roads

Percent cover of

roads in timber sale

area (including 25 m

road buffer)

0 - 1% Roads, road buffers are vectors of

exotic spp. transport; road buffers are

susceptible to establishment by C.

arvense and other exotics. Ranking

based on Tinker et al 1998,

McGarigal et al. 2001, Wemple et al.

2001.

Buckley et al.

2003; Fowler et al

2008; Forman and

Alexander 1998

1 - 3%

3 - 10%

Disturbed

Areas

Percent of timber

sale area disturbed

through logging

activities

0 - 25% Disturbance a function of total area

impacted by logging activities.

Disturbance includes skid trails,

logged areas, landing/loading decks.

Disturbance estimated from aerial

images. Ranking set at equal

increments.

Williamson and

Nielsen 2000;

Buckley et al.

2003

25 - 50%

50 - 75%

75 - 100%

Slash Piles

and Scars

Ratio of Area of

slash piles to Area

of timber sale unit

0 - <0.1%

Area of slash piles relative to total

area of timber sale unit. Ranking

based on natural breaks in data.

Haskins and

Gehring 2004;

Korb et al. 2004;

Creech et al. 2012;

Halpern et al 2014

Low - <1%

High - >1%

Overstory

Vegetation

Percent canopy

cover,

predominantly

Ponderosa Pine

(Pinus ponderosa)

0 - 25% Canopy cover inversely proportional

to understory/overstory vegetation.

Shading decreased likelihood of

establishment of C. arvense and other

exotics. Ranking set at equal

increments.

Abella and

Covington 2004

25 - 50%

50 - 75%

75 - 100%

Understory

Native

Vegetation

Percent cover of

native understory

vegetation

0 - 5% Greater percent cover of native

species decreases likelihood of

establishment by C. arvense and other

exotics. Ranking based on ranges

defined by monitoring data collected

for that parameter.

Abella and

Covington 2004

5 - 50%

50 - 100%

> 100%

Understory

Exotic

Vegetation

Percent cover of

exotic understory

vegetation

0 - 1 % Greater percent cover of exotics

increases likelihood of Canada thistle

establishment throughout the rest of

the timber sale unit. Ranking based on

ranges defined by monitoring data

collected for that parameter.

Abella and

Covington 2004

1 - 25%

25 - 50%

> 50%

Proximity

to Other

Disturbed

Areas

Proximity to nearest

disturbance (road or

sale unit) in meters

0 – 10 Proximity to a source patch increases

likelihood of C. arvense introduction

and spread. Numerical ranges for

ranking scheme based on C. arvense

dispersal mechanisms and distances.

Skarpaas and Shea

2007

10 – 50

50 – 250

250 - 500

1

Table 2. Description of CPTs. (categories correspond to those in the conceptual model (Fig. 2)

and BN (Fig. 3).

Parameter Inputs Node Type CPT Derivation

Soil Disturbance and Compaction

Roads Stressor Node

Math. Calculations Disturbed Area Empirical Evidence

Exposure of Bare Soil

Roads Stressor Node Case File Learning

Disturbed Area

Burned Slash and Biomass

Disturbed Area Stressor Node Empirical Evidence Slash Piles

Overstory/Understory Relationship

Understory Native Vegetation

Stressor Node

Math. Calculations Understory Exotic Vegetation Empirical Evidence Overstory Vegetation

Changes in Soil pH

Burned Slash and Biomass Effect Node Empirical Evidence Overstory/Understory

Changes in Available Nutrients

Soil Disturbance and Compaction

Effect Node Empirical Evidence Burned Slash and Biomass Exposure of Bare Soil Overstory/Understory

Changes in Biota

Overstory/Understory

Effect Node Empirical Evidence Burned Slash and Biomass Soil Disturbance and Compaction

Changes in Seed Bank

Overstory/Understory Effect Node Empirical Evidence Burned Slash and Biomass

Chemical Modification

Changes in Soil pH Summary Node

Math. Calculations Changes in Available Nutrients Empirical Evidence

Biological Modification

Changes in Soil Biota Summary Node

Math. Calculations Changes in Seed Bank Empirical Evidence

Ecological Structure Changes

Overstory/Understory Summary Node

Math. Calculations Exposure of Bare Soil Empirical Evidence

Establishment of Canada Thistle

Chemical Modification

Endpoint Node Empirical Evidence Biological Modification Ecological Structure Proximity to Other Disturbed Areas

1

Table 3. Risk probability distributions and overall risk scores for all risk regions and years.

The most likely risk probability distribution is denoted in BOLD.

Timber

Sale Descriptor

Risk

States Year 0 Year 1 Year 3

Dark

Canyon

Risk

Probability

Distributions

Zero 18.9 13.4 12.5

Low 38.8 33.8 34.5

Med 35.4 40.8 41.6

High 6.9 11.9 11.3 Risk Score 2.61 3.03 3.04

Thrall

Risk

Probability

Distributions

Zero 14.9 12.3 13.1

Low 34.8 31.4 34.5

Med 38.7 40.4 40.7

High 11.6 15.9 11.7 Risk Score 2.94 3.2 3.02

Mercedes

Risk

Probability

Distributions

Zero 18.2 13.7 14.5

Low 44 38.2 39.7

Med 33.2 38.7 37.8

High 4.6 9.5 8.1 Risk Score 2.48 2.88 2.79

Power

Pole

Risk

Probability

Distributions

Zero 18.1 15.6 10.1

Low 41.4 38.9 33

Med 34.4 37.1 43.1

High 6.2 8.3 13.8 Risk Score 2.57 2.76 3.21

Total Risk by Year 10.6 11.87 12.06

2

Figure Captions

Figure 1. Map of the Black Hills National Forest with the four timber sales: Dark Canyon,

Thrall, Mercedes, and Power Pole.

Figure 2. Conceptual model of Cirsium arvense (Canada thistle) risk of establishment due to

forestry activities in the BHNF. There are three pathways from sources and stressors:

(1) the pink nodes represent direct effects of timber harvest, (2) the green nodes

represent indirect effects of harvest through vegetation cover and species, and (3) the

purple node represents locational factors that affects the dispersal and distribution of

Canada thistle.

Figure 3. Example of Bayesian network for Dark Canyon Year 3. The BN maintains the

structure of the conceptual model and the three colored pathways (pink, green, and

purple) are consistent with the conceptual model (Fig. 2).

Figure 4. BN node for Dark Canyon timber sale area in Year 1 with four states populated with

site-specific data.

Figure 5. Risk probability distributions for Dark Canyon, Thrall, Mercedes, and Power Pole

timber sale areas for Year 0 pre-harvest and Years 1 and 3 post-harvest.

Figure 6. Sensitivity analysis of the input (parent) node variables.

Figure 7. Results of the sensitivity analysis of the stressor-effect intermediate node pathways.

Figure 8. Influence analysis results for Mercedes Year 1.

3

BHNF Manuscript Figures and Tables

Figure 1. Map of the Black Hills National Forest with the four timber sales: Dark Canyon,

Thrall, Mercedes, and Power Pole.

4

Figure 2. Conceptual model of Cirsium arvense (Canada thistle) risk of establishment due to forestry activities in the BHNF. There

are three pathways from sources and stressors: (1) the pink nodes represent direct effects of timber harvest, (2) the green

nodes represent indirect effects of harvest through vegetation cover and species, and (3) the purple node represents

locational factors that affects the dispersal and distribution of Canada thistle.

5

Figure 3. Example of Bayesian network for Dark Canyon Year 3. The BN maintains the structure of the conceptual model and

the three colored pathways (pink, green, and purple) are consistent with the conceptual model (Figure 2).

6

Figure 4. BN node for Dark Canyon timber sale area in Year 1 with four states populated

with site-specific data.

Overstory (% canopy cover)

0 to 25

25 to 50

50 to 75

75 to 100

59.1

22.7

13.7

4.55

28.4 ± 23

States

Unique probability

distribution curve

formed by populated

states.

Risk Score

Number of Plots Percent

0 to 25 percent cover 13 59.1

25 to 50 percent cover 5 22.7

50 to 75 percent cover 3 13.7

75 to 100 percent cover 1 4.55

Overstory (Percent Canopy Cover)

7

Figure 5. Risk probability distributions for Dark Canyon, Thrall, Mercedes, and Power Pole

timber sale areas for Year 0 pre-harvest and Years 1 and 3 post-harvest.

8

Figure 6. Sensitivity analysis of the input (parent) node variables.

9

Figure 7. Results of the sensitivity analysis of the stressor-effect intermediate node pathways.

10

Figure 8. Influence analysis results for Mercedes Year 1.

11

Table 1. Parameterization of model inputs for all sources of stressors.

Input

Variable Definition Ranking Justification References

Roads

Percent cover of roads

in timber sale area

(including 25 m road

buffer)

0 - 1%

1 - 3%

3 - 10%

Roads and road buffers are vectors of

exotic spp. transport; road buffers are

susceptible to establishment by C.

arvense and other exotics.

Ranking based on Tinker et al 1998,

McGarigal et al. 2001, and Wemple et

al. 2001

Buckley et al. 2003

Fowler et al 2008

Forman and

Alexander 1998

Disturbed

Area

Percent of timber sale

area disturbed through

logging activities

0 - 25%

25 - 50%

50 - 75%

75 - 100%

Disturbance is a function of total area

impacted by logging activities.

Disturbance includes skid trails, logged

areas, and landing/loading decks.

Disturbance was estimated from aerial

images.

Ranking is set as equal intervals.

Williamson and

Nielsen 2000

Buckley et al. 2003

Slash Piles

and Scars

Ratio of Area of slash

piles to Area of timber

sale unit

0 - < 0.1%

Low - < 1%

High - > 1%

Area of slash piles relative to total are of

the timber sale unit.

Ranking is based on natural breaks in the

data.

Haskins and

Gehring 2004,

Korb et al. 2004,

Creech et al. 2012,

Halpern et al 2014

Overstory

Vegetation

Percent canopy cover,

predominantly

Ponderosa Pine (Pinus

ponderosa)

0 - 25%

25 - 50%

50 - 75%

75 - 100%

Canopy Cover is inversely proportional

to understory/overstory vegetation.

Shading decreased the likelihood of

establishment by Canada thistle and

other exotics.

Ranking is set as equal intervals.

Abella and

Covington 2004

Understory

Native

Vegetation

Percent cover of native

understory vegetation

0 - 5%

5 - 50%

50 - 100%

> 100%

Greater percent cover of native species

decreases likelihood of establishment by

Canada thistle and other exotics.

Ranking is based on ranges

corresponding to those of the monitoring

data collected for that parameter.

Abella and

Covington 2004

Understory

Exotic

Vegetation

Percent cover of exotic

understory vegetation

0 - 1 %

1 - 25%

25 - 50%

> 50%

Greater percent cover of exotics

increases likelihood of Canada thistle

establishment throughout the remainder

of the timber sale unit.

Ranking is based on ranges

corresponding to those of the monitoring

data collected for that parameter.

Abella and

Covington 2004

Proximity

to Other

Disturbed

Areas

Proximity to nearest

disturbance (road or

sale unit) in meters

0 – 10

10 – 50

50 – 250

250 - 500

Proximity to a source patch increases

likelihood of C. arvense introduction

and spread.

Numerical ranges for ranking scheme

based on C. arvense dispersal

mechanisms and distances.

Skarpaas and Shea

2007

1

Table 2. Description of CPTs. (Color corresponds to those in the conceptual model (Figure 2)

and BN (Figure 3).

Parameter Inputs Node Type CPT Derivation

Soil

Disturbance

And

Compaction

Roads

Stressor Node

Math. calculations

Disturbed area Empirical evidence

Exposure of

Bare Soil

Roads Stressor Node Case file learning

Disturbed area

Burned Slash

and Biomass

Disturbed area Stressor Node Empirical evidence

Slashing Piles

Overstory/

Understory

Relationship

Understory Native Vegetation

Stressor Node

Math. calculations

Understory Exotic Vegetation Empirical evidence

Overstory Vegetation

Changes in

Soil pH

Burned Slash and Biomass Effect Node Empirical evidence

Overstory/Understory

Changes in

Available

Nutrients

Soil Disturbance and Compaction

Effect Node Empirical evidence Burned Slash and Biomass

Exposure of Bare Soil

Overstory/Understory

Changes in

Soil Biota

Overstory/Understory

Effect Node Empirical evidence Burned Slash and Biomass

Soil Disturbance and Compaction

Changes in

Seed Bank

Overstory/Understory Effect Node Empirical evidence

Burned Slash and Biomass

Chemical

Modification

Changes in Soil pH Summary Node

Math. calculations

Changes in Available Nutrients Empirical evidence

Biological

Modification

Changes in Soil Biota Summary Node

Math. calculations

Changes in Seed Bank Empirical evidence

Ecological

Structure

Overstory/Understory Summary Node

Math. calculations

Exposure of Bare Soil Empirical evidence

Establishment

of Canada

Thistle

Chemical Modification

Endpoint Node Empirical evidence Biological Modification

Ecological Structure

Proximity to Other Disturbed Areas

2

Table 3. Risk probability distributions and overall risk scores for all risk regions and years.

The most likely risk probability distribution is denoted in BOLD.

Timber

Sale Descriptor

Risk

States Year 0 Year 1 Year 3

Dark

Canyon

Risk Probability

Distributions

Zero 18.9 13.4 12.5

Low 38.8 33.8 34.5

Med 35.4 40.8 41.6

High 6.9 11.9 11.3

Risk Score 2.61 3.03 3.04

Thrall

Risk Probability

Distributions

Zero 14.9 12.3 13.1

Low 34.8 31.4 34.5

Med 38.7 40.4 40.7

High 11.6 15.9 11.7

Risk Score 2.94 3.2 3.02

Mercedes

Risk Probability

Distributions

Zero 18.2 13.7 14.5

Low 44 38.2 39.7

Med 33.2 38.7 37.8

High 4.6 9.5 8.1

Risk Score 2.48 2.88 2.79

Power

Pole

Risk Probability

Distributions

Zero 18.1 15.6 10.1

Low 41.4 38.9 33

Med 34.4 37.1 43.1

High 6.2 8.3 13.8

Risk Score 2.57 2.76 3.21

Total Risk by Year 10.6 11.87 12.06