Year End Summary Report for the 2014 & 2015 Botanical

YEAR END SUMMARY REPORT FOR THE 2014 & 2015 BOTANICAL SEASON

Cardamine angulata, CRPR 2B.1

Prepared By: Bianca Hayashi Staff Botanist

TABLE OF CONTENTS

2014 & 2015 Summary: Summary of 2014 survey season ▪ Page 3 Summary of 2015 survey season ▪ Page 4 Sensitive species detected on GDRCo in 2014 ▪ Page 5 Sensitive species detected on GDRCo in 2015 ▪ Page 6 Uncommon species detected on GDRCo 2014 ▪ Page 7 Uncommon species detected on GDRCo 2015 ▪ Page 8 Coastal Lagoons and Little River BMP, 2014 & 2015 Status Report ▪ Page 9 Montia howellii monitoring in Salmon Creek ▪ Page 11 Centaurea stoebe monitoring/removal Mad River ▪ Page 14 2014 surveys for southern operations ▪ Page 15 2014 surveys for northern operations ▪ Page 17 2015 surveys for southern operations ▪ Page 18 2015 surveys for northern operations ▪ Page 20 Supplemental Information: Vascular plant species list for all surveys 2001-2015 ▪ Page 22 WestInc. Power Analysis for Montia howellii ▪ Page 44

2014 GDRCo Botanical Technicians 2015 GDRCo Botanical Technicians

Lead Technician Permanent Technician

Bianca Hayashi Gabe Cashman

Seasonal Technicians Seasonal Technicians

Gabe Cashman Adrienne Simmons

Jenny Hutchinson Fey Egan

Alex Powell Kellie Eldridge

Jonathan Lee Katelyn Detweiler

Robert Child Jonathan Lucas

Dalynn Dykstra

Evan Mahoney-Moyer

SUMMARY OF 2014 SURVEY SEASON WORK PERFORMED

Field Season Survey Dates: 3/26/14 – 8/14/14 Total Number of Field Days: Approximately 100 field days Summary of Work Conducted: 45 Projects Reviewed

Full Floristic Surveys: 28 THPs received full floristic surveys ▪ 4 were resurveyed in spring 2015 due to the timing of the initial surveys in 2014

Master Agreement for Timber Operations (MATO) ▪ 72 road points under the 2014 Annual Work Plan were surveyed

Exempt From Full Floristic Surveys: 19 THPs in Coastal Lagoons and Little River BMA ▪ All were reviewed; 8 received focused surveys

Follow-up Visits: 42 follow-up CNDDB forms submitted for 12 species

Monitoring Activities: ▪ Salmon Creek Montia howellii monitoring ▪ Removal and monitoring of Centaurea stoebe along the Mad River

Total Acreage Assessed in 2014: 6,210 acres assessed Project area coverage: Approximately 4,745 acres surveyed *total acres surveyed is more since some areas were surveyed more than once 857 acres (6 THPs) exempt from surveying

Number of THP units surveyed: 94 THP units were surveyed Total Number of Surveyors Per Day: Typically 5 surveyors per day Total Number of Field Survey Hours: Approximately 1,646 field survey hours spent surveying THPs

Projects With Sensitive (CRPR 1or 2) Species: 14 THPs with CRPR 1 or 2 taxa

Total Number Sensitive (CRPR 1 or 2) Species 12 Sensitive species detected in 2014 Detected: *only 10 if the unconfirmed Kopsiopsis hookeri and the Piperia sp. are not the sensitive species

Projects With Uncommon (CRPR 3 or 4) 34 THPs with CRPR 3 or 4 taxa Species: Total Number Uncommon (CRPR 3 or 4) 12 Uncommon species detected in 2014 Species Detected:

SUMMARY OF 2015 SURVEY SEASON WORK PERFORMED

Field Season Survey Dates: 3/16/15 – 8/31/15 Total Number of Field Days: Approximately 118 field days Summary of Work Conducted: 56 Projects Reviewed

Full Floristic Surveys: 41 THPs received full floristic surveys ▪ 4 will be resurveyed in spring 2016 due to the timing of initial surveys in 2015

Master Agreement for Timber Operations (MATO) ▪ 441 road points under the 2015 Annual Work Plan were surveyed

Exempt From Full Floristic Surveys: 15 THPs in Coastal Lagoons and Little River BMA ▪ All were reviewed; 7 received focused surveys

Follow-up Visits: 40 follow-up CNDDB forms submitted for 8 species Monitoring Activities: ▪ Salmon Creek Montia howellii monitoring ▪ Removal and monitoring of Centaurea stoebe along the Mad River

Total Acreage Assessed in 2015: 11,141 acres assessed Project area coverage: Approximately 7,739 acres surveyed *total acres surveyed is more since some areas were surveyed more than once 1,904 acres (9 THPs) exempt from surveying

Number of THP units surveyed: 231 THP units were surveyed Total Number of Surveyors Per Day: Typically 8 surveyors per day Total Number of Field Survey Hours: Approximately 2,987 field survey hours spent surveying THPs

Projects With Sensitive (CRPR 1or 2) Species: 22 THPs with CRPR 1 or 2 taxa *3 of these had no new CRPR 1 or 2 detections, Howell’s montia was known from the appurtenant roads of the THPs

Total Number Sensitive (CRPR 1 or 2) Species 7 Sensitive species detected in 2015 Detected:

Projects With Uncommon (CRPR 3 or 4) 45 THPs with CRPR 3 or 4 taxa Species: Total Number Uncommon (CRPR 3 or 4) 11 Uncommon species detected in 2015 Species Detected:

Sensitive Species (CRPR 1 and 2) Detected in 2014

GDRCo Scientific Name Common Name Projects Total # of BotIDs code with Projects in Documented Detection GDRCo on GDRCo in 2014 Database through with 2015 Detection 1 ASUM Astragalus umbraticus Bald Mountain 1 16 26 milk-vetch 2 BOHOu Kopsiopsis hookeri small groundcone 1 5 6 (unconfirmed) 3 CAAR Carex arcta northern clustered 1 3 4 sedge 4 CALP Carex leptalea bristle-stalked sedge 1 3 3 5 COCAN Cornus canadensis bunchberry 1 4 14 6 ERRE Erythronium coast fawn lily 5* 53 209 revolutum 7 MOHO Montia howellii Howell’s montia 2 25 74 8 MOUN Monotropa uniflora ghost-pipe 6 67 470 9 PISP Piperia sp. rein orchid 3** 8 14 10 SAOF Sanguisorba officinalis great burnet 1 3 3 11 THRO Thermopsis robusta robust false lupine 1 14 26

*4 THPs have Erythronium revolutum that were detected at a time when the populations could be identified to species. 1 THP was found to have Erythronium sp. but the population was no longer in flower to be able to be identified to the species level.

**3 THPs have populations of Piperia sp. that could be populations of Piperia candida or a more common species. The total project and number of BotID#s reported in the subsequent columns reflect data of confirmed Piperia candida populations.

Sensitive Species (CRPR 1 and 2) Detected in 2015

GDRCo Scientific Name Common Name Projects Total # of BotIDs code with Projects in Documented Detection GDRCo on GDRCo in 2015 Database through with 2015 Detection 1 ASUM Astragalus umbraticus Bald Mountain 1 16 26 milk-vetch 2 CAAN Cardamine angulata seaside bittercress 1 1 2 3 ERRE Erythronium revolutum coast fawn lily 4* 53 209 4 MOUN Monotropa uniflora ghost-pipe 8 67 470 5 PABO Packera bolanderi var. seacoast ragwort 1 6 13 bolanderi 6 PICA Piperia candida white-flowered rein 2** 8 14 orchid

*2 projects have confirmed populations of Erythronium revolutum. 2 projects have Erythronium sp. that was detected at a time when floral features were not present to make an identification to the species level. The subsequent columns report data that is specifically for Erythronium revolutum and does not include data for Erythronium sp. that could, or could not, be populations of E. revolutum.

**1 project had a confirmed population of Piperia candida and 1 project had a population of Piperia sp. which could, or could not, be P. candida.

Uncommon Species (CRPR 3 and 4) Detected in 2014

GDRCo Scientific Name Common Name Projects Total # of BotIDs code with Projects in Documented Detection GDRCo on GDRCo in 2014 Database through with 2015 Detection 1 COLA Coptis laciniata Oregon goldthread 2 37 118 2 LICO Listera cordata heart-leaved 18 299 584 twayblade 3 LIWA Lilium washingtonianum purple-flowered 1 1 1 ssp. purpurascens Washington lily 4 LYCL Lycopodium clavatum running-pine 18 276 895 5 MICAU Mitellastra caulescens leafy-stemmed 8 112 234 mitrewort 6 OXSU Oxalis suksdorfii Suksdorf’s wood- 1 14 17 sorrel 7 PICAL Pityopus californicus California pinefoot 13 162 269 8 PLRE Pleuropogon refractus nodding semaphore 4 67 104 grass 9 RILA Ribes laxiflorum trailing black currant 8 114 192 10 SIMA Sidalcea malachroides maple-leaved 1 48 87 checkerbloom 11 TITRTR Tiarella trifoliata var. trifoliate laceflower 2 19 20 trifoliata 12 USLO Usnea longissima Methuselah’s beard 4 36 49 lichen

Uncommon Species (CRPR 3 and 4) Detected in 2015

GDRCo Scientific Name Common Name Projects Total # of BotIDs code with Projects in Documented Detection GDRCo on GDRCo in 2015 Database through 2015 with Detection 1 COLA Coptis laciniata Oregon goldthread 4 36 118 2 DARCAL Darlingtonia California pitcherplant 1 3 4 californica 3 LICO Listera cordata heart-leaved 20 299 584 twayblade 4 LYCL Lycopodium clavatum running-pine 9 275 895 5 MICAU Mitellastra caulescens leafy-stemmed 10 112 234 mitrewort 6 PICAL Pityopus californicus California pinefoot 12 162 269 7 PLRE Pleuropogon refractus nodding semaphore 3 67 104 grass 8 RILA Ribes laxiflorum trailing black currant 7 114 192 9 SIMA Sidalcea malachroides maple-leaved 1 47 87 checkerbloom 10 THGR Thermopsis gracilis slender false lupine 2 13 21 11 USLO Usnea longissima Methuselah’s beard 6 36 49 lichen

Coastal Lagoons and Little River Botanical Management Plan (CL&LR BMP) 2014 and 2015 Status Report

GDRCo and CDFW agreed that the long-term survey protocol for THPs within the Coastal Lagoons and Little River BMA, effective 2009, is as follows:

1. RPFs shall conduct focused surveys for all THPs within the Coastal Lagoons and Little River BMA. RPFs shall be responsible for reporting the presence of any unique, high quality, sensitive plant habitat within their project area, e.g. bogs, well developed lakes or ponds, coastal prairie or large mossy boulders or rock outcrops. When Lycopodium clavatum is encountered within THP areas voluntary, non-enforceable PPMs will be applied. These PPMs include establishing ELZs for select populations and retaining non-merchantable trees. If other sensitive species are observed, the RPF will consult with GDRCo botany staff. 2. Botanical technicians shall survey unique, high quality sensitive plant habitats within THPs as identified by RPFs. If sensitive species are discovered appropriate PPMs shall be applied. 3. Botanical technicians shall monitor a subset of L. clavatum populations on a yearly basis. Initially, monitoring activities will focus on pre and post-harvest monitoring of populations protected with voluntary, internal PPMs that were implemented for plans submitted after July 8, 2008. Revisions to internal PPMs may be made based on monitoring results. 4. Botanical technicians will survey unique or high quality habitats outside of THPs when they are identified. The intent is to find and survey areas within the BMA that have the greatest likelihood of supporting sensitive species, regardless of whether or not the habitat would ever be impacted by timber harvest operations.

Summary of THP activity and survey coverage in the Coastal Lagoons and Little River BMA since adoption of the Botanical Management Plan (BMP) in 2008.

Year THP acres in BMA BMA acres BMA acres exempt surveyed from survey 2008 3,029 1,219 1,810 2009 670 76 594 2010 3,813 109 3,704 2011 1,975 52 1,923 2012 893 1 892 2013 1,811 52 1,759 2014 2,185 137* 1,620 2015 2,625 148* 2,374

Totals 17,001 1,794 14,676 *The value of BMA acres surveyed in years 2014 and 2015 includes acreage from plans that were generated in the prior year.

In 2014, nine new populations (BotID#s) of Lycopodium clavatum were detected within the Coastal Lagoons and Little River BMA in 7 THPs. Four populations were protected with programmatic plant protections measures (25 ft. Equipment Limitation Zone placed around the population with merchantable timber harvested and sub-merchantable timber retained) and two were protected through avoidance. Three have no protection measures since they are associated with roads that will need to be utilized in timber harvest operations. Internal protection measures were applied to 5 of the 12 THPs located within the Coastal Lagoons and Little River BMA in 2014. Four documented populations of Lycopodium clavatum within the Coastal Lagoons and Little River BMA were revisited and all four were observed to be extant.

In 2015, two new populations (BotID#s) of Lycopodium clavatum were detected within the Coastal Lagoons and Little River BMA in two THPs. One population was protected with programmatic plant protection measures (25 ft. Equipment Limitation Zone placed around the population with merchantable timber harvested and sub-merchantable timber retained). The other population was located in a region where impacts from timber harvest operations could not be avoided. Internal protection measures were applied in 1 of the 14 THPs located within the Coastal Lagoons and Little River BMA in 2015. Three documented populations of Lycopodium clavatum within the Coastal Lagoons and Little River BMA were revisited and all three were observed to be extant. Seven other documented populations of Lycopodium clavatum outside the Coastal Lagoons and Little River BMA were revisited and all, except for one, were found to be extant as well.

Montia howellii monitoring in Salmon Creek

Based upon the results of the power analysis that West Inc. conducted on the 2011 through 2013 monitoring data for Montia howellii in Salmon Creek, the monitoring protocol was revised to focus on detecting trends of occupancy in 2015. This has subsequently resulted in a change to the sampling protocol and much of the data collection has been simplified. Revisions to the monitoring protocol are as follows:

Objectives: 1. Determine if the trend in occupancy of Montia howellii (MOHO) growing along the mainline Salmon Creek road system is increasing, decreasing or stable over time.

2. Determine if MOHO occupancy is correlated with covariates that can be altered through management. 1. Road surface type as percent cover of dirt and percent cover of rock (cumulatively equal to 100%). 2. Percent cover of competing vegetation – we will measure cover of plants at ground level other than MOHO, including straw mulch or logging slash when they are at densities that clearly prevent plants from growing

3. If the population (percent occupied segments) shows a trend of significant decline, as evidenced by either a significant abrupt change in one or two years, or a significant long-term trend over several years, consult with DFG and attempt to coordinate road management activities with activities designed to maintain MOHO (e.g. if percent occupancy is highly correlated with specific habitat variables then manage for more of those).

Sampling Plan: 1. The sampling universe includes the GDRCo Salmon Creek tract mainline road system on the north side of Salmon Creek, with the exception of one road that crosses to the south side of Salmon Creek. That road is included until it reaches the GDRCo property line. The roads that are classified as mainline roads for the purpose of this study are the main roads that are projected to be used and maintained over the next ten years. For the most part the roads included provide an access loop through the extent of the property on the north side of Salmon Creek. The significance of this continuity is that it should allow us to account for plants anywhere they may have migrated along the mainline. The majority of the mature timber in this tract has already been harvested and the main area left to harvest is at the northeastern portion of the sampling universe. The mainline road accessing this area has been reconstructed and newly constructed in recent years and while MOHO has not been found on this road yet, it does contain potential habitat. There are other roads included that to date have not supported MOHO, as well as roads that have supported large numbers of MOHO until they were rocked a few years ago. Some of the roads included may get more or less use than others over the course of the study. The sampling universe contains road segments with both natural and rocked surfaces. Additional rocking may occur in the years to come. We anticipate that the habitat quality along roads or portions thereof will fluctuate throughout the course of the study.

2. The roads in the sampling universe were designated as routes with beginning and end points and then routes were ordered in space and divided into 50 foot segments that will serve as

sample plots. The width of the road varies and plants are often found at the margins and in the middle, so the plots will not have a fixed dimension across the width of the road. Each 50 foot segment was given a permanent route ID and segment ID with fixed start/stop UTM coordinates that can be loaded into GPS units. *There are three road segments that are shorter than 50 feet, but longer than 25 feet so we will keep them. There are a total of 1551 road segments.

3. Stage 1 consists of determining presence or absence of MOHO in the sample plots or road segments (SID). Sample plots selected by a generalized tessellation stratified (GRTS) sample of 50-foot road segments to ensure sample plots are spatially balanced (interspersed) throughout the population with few if any adjacent to one another. The GRTS sample will consist of a specially randomized list of road segments that preserves spatial balance in adjacent sets of segments. The first 110 segments on this list were formerly placed in a group called Panel 1. All of Panel 1 was sampled on an annual basis for the first 4 years of the study. The subsequent groups of 20 segments were assigned to panels numbered 2 through 73. Road segments in panels 2 through 73 were to be sampled twice every 5 years on a rotating basis. During the first 5 years of the study, a total of n = 130 sites were being sampled (Panel 1 plus one of Panel 2 through 6). After year 5 of the study, a total of n = 150 segments were to be sampled (Panel 1 plus a previously unvisited panel plus a previously visited panel). After 4 years of conducting the study under this sampling regime, it was determined that the effort required to complete the number of sites was too great to remain sustainable for the long term. The proposed revisions to the sampling protocol are outlined below.

Revision to Sampling: The goal for revising the sampling protocol is to have 2 groups of surveyors complete the work in 2 weeks, or 10 sample days. To achieve this goal, we propose that the sample number be reduced and that the survey protocol be simplified. By estimating that each group will be able to complete 5 sites per day, this makes 100 SIDs over the 10 sampling days. This seems reasonable and achievable. There is some concern regarding destructive sampling to the 110 permanent sites. It would be ideal to be able to give some of these sites a rest period. In addition to this, having faster replication of the 80 (panels 2- 5) that have been sampled already could also be beneficial. There will still be sampling of new sites as well under the following scheme: a) Panel 1, which consists of 110 SIDs, gets divided into three panels: Panel 1, Panel 2 and Panel 3. Two of these will have 37 SIDs and one will have 36. In order to give some of these permanent sites a resting period, a two year on and one year off strategy will allow us to do this without losing how a prior year at a SID directly impacts the following year (since they are annual plants). This strategy would look like this: Year 1: Panel 1, 3 Year 2: Panel 1, 2 Year 3: Panel 2, 3 Year 4: Panel 3, 1 Year 5: Panel 1, 2 Etc. b) Panels 2-6, which each consist of 20 SIDs, but collectively make 100 SIDs, will remain the same. Their panel numbers will change since panel 1 has been divided into three new panels. Their new panel numbers will be 4-8. These panels of 20 will cycle and repeat every 5 years e. g. 4, 5, 6, 7, 8, 4, ,5 ,6 ,7 ,8, etc. c) If we take two of the panels from the first group (Panels 1-3), this will make 74 SIDs. If we add one of the other panels from the second group (Panels 4-8), this will make 94 SIDs. We

will always sample 8 new sites, and since the number of permanent SIDs we sample can vary slightly year to year, some years we will survey 101 total SIDs and some years we will survey 102 (36+37+20+8=101; 37+37+20+8=102).

4. Stage 2 consists of determining patch size in the selected sample plots. Each sampled plot will be divided into 5 10-foot quadrats that extend across the entire width of the road. Presence or absence of MOHO will be recorded for each of the 5 quadrats in the sampled plot. To estimate probability of detecting MOHO assuming it is there, two surveyors will make independent presence/absence observations and record their data separately and discretely. When complete the two surveyors will compare their P/A to make the P/A union. When there are discrepancies the two surveyors will visually check the quadrat to see if there was a true miss by one surveyor, a miss-ID or if the plant could not be relocated. If it was a miss-ID or could not be relocated, a note is made on the datasheet, since this leads to cases where their union is not a simple addition of P/A1 and P/A2. Boundaries of the quadrats will be delineated using stakes/spikes at five foot intervals at both edges of the plot (outside road margins) and then using string (hip chain) to create a grid. See the diagram in the field methods section. 5. Each surveyor will make visual estimates of the percent cover of rock to the nearest whole percent (0-100) in each quadrat while they are doing the P/A survey. Each surveyor will also make a visual estimate of the percent cover to the nearest whole percent (0-100) of competing vegetation in each quadrat. Once the surveyors have completed the segment, they will come to agreement on their independent assessments to determine the union value for the environmental variables.

The 2014 monitoring effort for Montia howellii was conducted in the same manner as it was for the 2011-2013 years. The new monitoring protocol was implemented starting in 2015. West Inc. will integrate the data from past years to be able to continue with detecting the trend of occupancy within the Salmon Creek road system. Following this integration, we will be able to further investigate the trends of occupancy as indicated by the 2011-2015 datasets.

Centaurea stoebe monitoring/removal at Sweet Flat, Mad River

Since 2013, Green Diamond Resource Company’s botany department has been collaborating with the Humboldt County Department of Agriculture monitoring and removing spotted knapweed (Centaurea stoebe) from the gravel bars along the Mad River near the city of Blue Lake. Initially, in 2013, spotted knapweed was detected along the Mad River at three locations south of the Mad River Hatchery. One of these locations is at Sweet Flat, which is best accessed from private GDRCo roads and is the site that the botany department has been monitoring since 2013. In 2014, the site was resurveyed and no plants were detected. The gravel bars along the river were surveyed to the south of the site and no other populations were detected in these regions either. In 2015, the site was resurveyed, and a population was detected and removed. The gravel bars along the river were surveyed to the south of the site again and no additional plants were detected. The botany department plans to continue surveys for, and removal of, spotted knapweed along the Mad River.

2014 Surveys for Southern Operations

# Acres # units In LYCL # Acres in 2014 Field Sensitive plants Uncommon plants count Mitigation THP/Project Name GDRCo# surveyed # of units surveyed state # Field work dates BMA? THP survey hours CRPR 1 or 2 CRPR 3 or 4 2014 2014 MOHO known from 1 programmatic Salmon Creek Left 141301 no 88 88 4 4 1-13-092H 4/24/14, 4/25/14 18 appurtenant roads LICO in all units 4/2/14, 4/3/14, 4/4/14, LYCL in C and D; 1 programmatic OH (2014) 151301 no 203 203 4 4 1-13-110H 4/7/14, 4/8/14 65 RILA and LICO in D 5/16/13, 5/23/13; Piperia sp. on road; 1 other Roddi 1000 171202 no 56 0 (road) 2 0 1-12-120H 6/11/13; 5/2014 3 ERMA on road Other; 50 ft. No Harvest; 5/16/14, 5/19/14, Erysp. In A, Piperia sp. 1 Avoidance Goodman Prairie (2015) 171301 no 170 170 6 6 1-14-041H 5/20/14 99 in C LICO in B Other; 50 ft. No Harvest; Snow Camp Lake COCAN, ERYsp, 1 Avoidance (2015) 171401 no 92 92 4 4 1-14-087H 6/16/14, 7/3/14, 9/3/14 31 CAAR, SAOF Platanthera sp. MICAU outside 1 other FH 820/940 221301 no 71 71 3 3 1-13-116H 4/3/14, 4/4/14 18 ERRE in B units; LYCL in B 1 programmatic MR 6000 (2014) 221302 no 31 31 1 1 1-14-028H 4/1/14, 4/2/14 12 MOHO at rock pit LYCL, LICO

LYCL in B; MICAU in 1 programmatic Fickle Hill 5200 (2014) 221303 no 62 62 3 3 1-14-054H 4/9/14, 4/10/14, 4/17/14 29 A and B; RILA in B Greenwood Heights 1 programmatic (2014) 231301 no 92 92 1 1 1-14-040H 3/31/2014, 4/1/14 48 LYCL, MICAU Other; 50 ft. No Harvest; 5/21/14, 5/22/14, Erysp. In A and E; MICAU and PLRE in 1 Avoidance Hungry Hollow (2014) 261303 no 182 182 6 6 1-14-057H 5/23/14 46 Piperia in F A 4/29/14, 4/30/14, 5/1/14, 5/2/14, 5/5/14, MICAU in A, B, F and 5/6/14, 5/7/14, 5/14/14, I; LICO in A, B, F, G, 1 CB 1160 (2015) 261304 no 328 328 9 9 1-14-082H 5/15/14 105 and I 50 ft. No MICAU in A, B and Harvest; 5/23/14, 5/27/14, Piperia sp. in C; BOHO C; TITRTR in A; 1 Avoidance Fulton North (2015) 261401 no 102 102 5 5 1-14-060H 5/28/14 36 in B PICAL in B PICAL in A, B, C, E 5/28/14, 5/29/14, THRO on apprtnt and F; LICO in D and 1 avoidance Upper Lupton (2014) 271401 no 282 282 6 6 1-14-043H 5/30/14, 6/2/14 51 roads F CAAR and CALP in 1 not developed Fern Prairie (2015) 271403 no 151 20 4 2 1-14-131H 11/4/2014 6 unit B

PLRE in A, B and C; MICAU in B and C; 1 NF 1685 (2014) 381301 no 77 77 3 3 1-14-062H 6/3/14, 6/4/14 37 LICO in C LYCL in B; LICO in A, B; MICAU in B; RILA in B; PICAL and 1 avoidance NF-1000 Bridge (2014) 401301 no 102 102 3 3 1-14-022H 6/5/14, 6/6/14 38 COLA in B, C TITRTR in C 4/25/14, 4/28/14, 1 Upper Basin (2014) 421303 no 118.5 118.5 4 4 1-13-124H 4/29/14 29 LICO in B and C K&K 700/CR 2016 1 (2014) 431303 no 38 38 2 2 1-13-103H 4/21/14, 4/22/14 9 PICAL in B 1 CR 2760/2021 (2015) 431304 yes 89 2 5 1 1-13-131H 3/27/2014 1

1 programmatic CR 2022 Thin 431401 no 266 266 2 2 1-14-107H 8/8/14, 8/11/14-8/14/14 75 LYCL in A LYCL in A; PICAL in 1 programmatic CR 2018 (2014) 451301 no 111 111 5 5 1-13-088H 5/5/14-5/8/14 25 C and D LYCL in B and along road; LICO and 1 programmatic BL 1000 (2014) 471306 yes/no 103 32 3 2 1-14-019H 3/26/14, 3/27/14 16 PICAL in C 1 BL 2230 (2015) 471311 yes 93 4 4 2 1-14-013H 3/27/2014 7 1 BL 1000/1140 (2015) 471312 yes 330 60 2 2 1-14-059H 8/21/2014 4.5

1 CR 1945/BL 2225 (2015) 471402 yes 108 5 3 2 1-14-037H 5/1/2014 2 Programmatic; 1 Avoidance BL 1000/2600 (2015) 471403 yes 204 4 7 3 1-14-085H 7/24/14, 8/13/14 3 LYCL in A and G 1 None BL 2600/2690 (2015) 471407 yes 219 5 6 3 1-14-113H 8/20/2014 5 LYCL by A McDonald Creek 1 Combo 471409 yes 271 25 4 1 1-14-144H 10/16/14, 10/21/14 6 LICO in A, C, D; 1 K&K 959 481302 no 125 125 5 5 1-13-127H 4/22/14, 4/25/14 37 PICAL in A and B 29 4164.5 2697.5 94 861.5 *There are additional THPs from the Coastal Lagoons and Little River Botanical Management Area that were exempt from survey that are not included on this spreadsheet

2014 Surveys for Northern Operations

# Acres # units # Acres in 2014 Field Sensitive plants Uncommon plants count Mitigation THP/Project Name GDRCo# surveyed # of units surveyed state # Field work dates THP survey hours CRPR 1 or 2 CRPR 3 or 4 2014 2014 4/11/14, 4/14/14 - ERRE, ASUM, Piperia, 1 other Bald Hills (2014) 511401 267 267 7 7 1-14-073H 4/17/14 88 GICA, COLA LICO in C, A 7/14/14, 7/15/14, 7/16/14, 7/17/14, 1 N 599 561304 170 170 6 6 1-14-026H 7/18/14 50 LYCL outside of GDRCo property; 4/9/14, 4/10/14, 4/18/14, 4/21/14, LICO in A, B and C; 5/2/14; 7/1/14, RILA in B; PICAL in 1 Holter Ridge (2014) 561401 181 181 4 4 1-14-048D 7/11/14 61 B 5/9/14, 5/12/14, 5/13/14, 5/14/14, 5/15/14; 6/30/14, ERYsp outside D; PICAL in A and B; programmatic; 7/2/14, 7/8/14, 7/9/14, MOUN in A, B, C, D, E LICO in F; RILA in B, 1 other Upper Hunter (2014) 711301 232 232 6 6 1-15-057H 7/10/14 138 and F C and D 6/9/14, 6/25/14, programmatic; 6/26/24, 6/27/14, MOUN in unit B, C 1 other Teepo Ridge (2014) 731401 239 239 8 8 1-14-065D 7/1/14 84 and D PICAL in F and G 6/11/14, 6/12/14, 6/13/14, 6/16/14, programmatic; 6/18/14, 6/19/14, 1 other R1210-R1610 (2014) 931304 231 231 8 8 1-14-052D 6/20/14, 6/23/14 111 MOUN in every unit RILA in unit E 7/22/14, 7/23/14, 7/25/14, 7/28/14, 7/29/14, 7/30/14, OXSU in A; RILA programmatic; 7/31/14, 8/1/14, MOUN in every unit along road; USLO in 1 other Low Divide 1300 (2015) 931401 434 434 7 7 1-14-142D 8/4/14, 8/6/14 171 except B G; SIMA in G

7/2/14, 7/7/14, 7/8/14, 7/9/14, 7/14/14, programmatic; 7/15/14, 7/16/14, MOUN in A, B, C, D, E 1 other Little Mill Creek 941401 293 293 7 7 1-14-146D 7/17/14, 7/21/14 82 and G RILA in D 8 2047 53 785

2015 Surveys for Southern Operations

# Acres # units In LYCL # Acres in 2015 Field Sensitive plants Uncommon plants count Mitigation THP/Project Name GDRCo# surveyed # of units surveyed state # Field work dates BMA? THP survey hours CRPR 1 or 2 CRPR 3 or 4 2015 2015 LICO in every unit Stevens Creek Central 6/11/15, 6/12/15, except H; MICAU in 1 not yet developed (2016)* 091501 no 245 245 9 9 1-15-068H 6/15/15 72.5 PABO in G and I A MOHO as known 3/26/15, 3/27/15, from appurtenant 3/30/15, 3/31/15, roads; no new LICO in every unit; 1 Programmatic Middle Salmon (2015) 141401 no 214 214 8 8 1-14-137H 4/1/15 100 detections MICAU in B and C MOHO as known from appurtenant roads; no new 1 Programmatic McCloud Creek #6 141402 no 146 146 6 6 1-14-119H 4/8/15, 4/9/15 39.5 detections LICO in B, E and F MOHO as known from appurtenant LICO in every unit; roads; no new MICAU in G; PLRE in 1 Programmatic Salmon Creek (2016) 141403 no 178 178 7 7 1-15-046H 6/2/15, 6/3/15, 6/4/15 49 detections G LYCL in A, B, D, E, F and G; RILA in E and 5/12/15, 5/13/15, G; LICO in C and G; 1 G-140 151401 no 181 181 7 7 1-15-026H 5/14/15, 5/15/15 75.5 MICAU in A Erysp. In A;ERRE in Other; 50 ft. No- A, B and E; Piperia 1 Harvest Goodman Prairie (2015) 171301 no 170 20 5 5 1-14-041H 4/27/15-4/29/15 33 sp. in C and A LICO in B 6/16/14, 7/3/14, LYCL in C; PICAL in 9/3/14, 6/17/14, A and B; PLRE in A; 6/18/14, 6/24/15, PLST in B; LIWA Other; 50 ft. No- 5/8/15, 5/11/15, COCAN, ERYsp, along appurtenant 1 Harvest Snow Camp Lake (2015) 181401 no 124 30 4 4 1-14-087H 5/21/15 8 ERRE, SAOF, CAAR road 3/16/15, 3/17/15, 3/18/15, 3/20/15, SIMA in D, RILA in 1 McKay R-8 Combo 191401 no 300 300 4 4 1-15-001H 3/31/15, 4/2/15 60 C and D LICO in A; LYCL in 4/3/15, 4/8/15, B; MICAU in A and 1 Programmatic MR5100 Thin (2016) 221401 no 173 173 2 2 1-15-043H 4/9/15, 4/10/15 67 B MICAU in A and D; LICO in A, D and E; Avoidance; 50 ft. No- 5/4/15, 5/5/15, ERRE in D; Piperia LYCL in B; USLO in 1 Harvest Ward Road (2016) 241401 no 293 293 5 5 1-15-044H 5/6/15, 5/7/15 82 sp. in E C and D; PLRE in A LICO in A, B, D and 4/28/15, 4/29/15, E; MICAU in A, C, D 1 Korbel Combo (2016) 241403 no 305 305 6 6 1-15-018H 4/30/15 68 and E

MICAU, LICO and Other; 50 ft. No- 4/9/15, 4/20/15, Erysp. In A and E; PLRE in A; USLO in 1 Harvest Hungry Hollow (2014) 261303 no 182 35 6 6 1-14-057H 4/21/15 30 Piperia in F C and D MICAU in A, B and Other; 50 ft. No- 5/23/14, 5/27/14, Piperia sp. in C; C; TITRTR in A; 1 Harvest Fulton North (2015) 261401 no 99 15 5 5 1-14-060H 5/28/14 25 BOHO in B PICAL in B LICO in A and B and 4/22/15, 4/27/15, appurtenant roads; 1 High Prairie Thin 271402 no 213 213 3 3 1-14-132H 4/28/15, 6/4/15 49 PICAL in C CAAR in and north 4/13/15, 4/14/15, of B; CALP north of 1 Avoidance Fern Prairie (2015) 271403 no 153 153 4 4 1-14-131H 4/15/15, 5/8/15 151 B LICO along road to A LICO in every unit; COLA in B, C, D and 1 North Fork 401501 no 179 179 6 6 1-15-095H 5/29/15, 6/1/15 44 E; USLO in F. 9/10/15-9/12/15, 9/15/15, 9/19/15, LICO in E; LYCL in C; 1 Programmatic Kings Canyon (2015) 421401 no 247 247 7 7 1-14-086H 4/7/15 52 PICAL in A and G 3/24/25, 3/26/15, LYCL in B; LICO in 4/1/15, 4/2/15, B; PICAL in B; COLA 1 Programmatic K&K 800 Junction (2014) 421402 no 228 228 6 6 1-14-133H 4/6/15, 4/7/15 78 in F LICO in A, C, D and E; COLA in E; LYCL 5/18/15, 5/27/15, in C; PICAL in C and 1 Programmatic K&K 622 (2016) 421403 no 200 200 5 5 1-14-143H 5/28/15 67 D The Little River THP LICO in D; LYCL in 1 Programmatic (2015) 431402 yes 155 23 7 1 1-14-139H 5/28/2015 7 unit C

PICAL in A, B, C and 1 Fawn Prairie 300 (2016)* 441501 no 125 125 5 5 1-15-087H 6/17/15, 6/18/15 36 E; LICO in B and D 5/20/15, 5/21/15, PICAL in A and C; 1 Redwood Creek North 451401 no 260 260 3 3 1-14-098H 5/22/15, 5/26/15 70 LICO in B 1 Keystone 1000 451402 no 240 240 1 1 1-14-138H 6/8/15, 6/9/15 72

1 Programmatic Panther Creek (2015) 451502 no 145 145 5 5 1-15-076H 6/4/15, 6/5/15, 6/8/15 46 LYCL in F; LICO in F 1 Tom Creek Thin 471408 yes 258 85 1 1 1-14-116H 2/28/2015 6 6/10/15, 6/16/15, 1 Programmatic CR 1000 West 471411 yes 195 5 7 3 1-15-055H 6/25/15 6 LYCL in B, C, E and F 1 BL 1000/1500 (2016) 471501 yes 138 2 4 1 1-15-079H 6/10/2015 1 1 BL 1380 Thin 471504 yes 174 10 2 2 1-15-078H 10/14/2015 4 1 BL 2000/3000 Thin (2016) 471507 yes 193 23 5 2 1-15-135H 10/29/15, 12/31/15 6 29 5713 4273 129 1404.5 *Those projects with an asterisk and are colored green will be getting a spring survey in 2016 **There are additional THPs within the Coastal Lagoons and Little River Botanical Management Area that were exempt from survey that do not appear on this list

2015 Surveys for Northern Operations

# Acres # units # Acres in 2015 Field Sensitive plants Uncommon plants count Mitigation THP/Project Name GDRCo# surveyed # of units surveyed state # Field work dates THP survey hours CRPR 1 or 2 CRPR 3 or 4 2015 2015 1 50 ft. No-Harvest Bald Hills #2 (2014) 511402 164 164 5 5 1-14-136H 4/13/15-4/16/15 68 ERYsp unit E LICO in C, D, E ERRE in unit E and LICO in A, B and C; the unit that was COLA in B, C and the 4/17/15, 4/20/15, thrown out of the unit that was thrown 1 Avoidance Hancorne (2015) 511501 130 150 5 6 1-15-050H 4/21/15, 4/22/15 28 plan. out of the plan 1 CL1040-A600 561402 160 160 6 6 1-15-052H 6/10, 6/11, 7/20/25 54 PICAL in C PLRE in B and E; RILA 50 ft. No-Harvest 5/14, 5/19, 5/20, 8/5, CAAN in units A in A, D, H and 1 Avoidance CL South/A 400 (2016) 561403 298 298 8 8 1-15-082H 8/6, 8/10/15, 8/11/15 142 and B roadside; PICAL in E 8/13, 8/17, 8/18, not 8/20, 8/23, 8/24, 1 CL 800 (2016) 561502 172 172 5 5 assigned 8/25/15 81 RILA in F 5/4/15, 5/5/15, 50 ft. No-Harvest 6/26/15, 6/29/15, ERYsp unit D, 1 Programmatic S-810 (2015) 611401 145 145 5 5 1-15-039H 7/13/15, 7/14/15 91 MOUN 5/11, 5/12, 5/14, 7/31, 8/4, 8/6, MICAU in A, B, C, D, 1 Programmatic SA 10/300 (2016) 611501 352 352 8 8 1-15-108H 8/15/15 113 MOUN unit E H; PICAL in C, F and G 5/12/15, 5/13/15, 1 H-200 711401 126 126 4 4 1-15-038D 7/10/13, 7/13/15 28 RILA in C 5/6/15, 6/26/15, 1 Programmatic Hunter Creek West 711501 231 231 6 6 1-15-045D 7/24/15, 7/27/15 74 MOUN in A, B and D PICAL in D and F 8/11/15, 8/12/15, 8/14/15, 8/21/15, 1 Programmatic KM850 731501 142 142 7 7 8/31/15 58 MOUN in unit C 4/23/15, 4/24/15, 7/14/15, 7/15/15, 1 U-10 (2015) 851401 330 330 11 11 1-14-152D 7/16/15, 7/17/15 111 PICAL in unit K 5/19/2015, 8/3/2015, 1 B-10-13/18 851501 68 68 3 3 1-15-060D 8/6/2015 27 MICAU in B 1 North Bank 100 Thin 901401 270 270 4 4 1-14-151D 7/21/15,7/22/15 90 MOUN in almost every unit - unit B OXSU in unit A; RILA only one without in unit F; USLO in G; 1 Programmatic ; Other Low Divide 1300 (2015) 931401 441 20* 7 2 1-14-142D 7/1/2015 10 MOUN SIMAL unit G

6/24/15, 6/25/15, 6/29/15, 6/30/15, 7/1/15, 7/8/15, MOUN in every unit MICAU in unit D; 1 Programmatic ; Other D1130 (2015) 931402 167 167 5 5 1-14-155D 7/9/15 236 of the plan PICAL in unit D 6/30/15, 7/6/15, 7/7/15, 7/20/15, MOUN in every unit 1 Programmatic ; Other WI-105 931501 200 200 6 6 1-15-069D 7/28/15 200 of the plan 6/23/15, 6/24/15, 7/8/15, 7/9/15, 1 None D1900 (2016) 931502 268 268 5 5 1-15-048D 7/22/15, 7/23/15 102 MOUN in unit E USLO in units C and E 7/29/15, 7/30/15, MOUN in units D, E 1 Programmatic NB-340 941501 223 223 6 6 1-15-105D 8/27/15 69 and F USLO 18 3466 102 1582

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count TREES 88% 97 Pseudotsuga menziesii var. menziesii Douglas-fir 76% 84 Sequoia sempervirens coast redwood 76% 84 Alnus rubra red alder 73% 81 Notholithocarpus densiflorus var. tanoak 61% 67 Tsuga heterophylla western hemlock 52% 57 Frangula purshiana cascara 47% 52 Acer macrophyllum big-leaf maple 47% 52 Arbutus menziesii Pacific madrone 41% 45 Umbellularia californica California-bay 35% 38 Abies grandis grand fir 33% 36 Salix sp. willow 29% 31 Thuja plicata western red cedar 28% 31 Picea sitchensis Sitka spruce 26% 28 Chrysolepis chrysophylla var. chrysophylla giant chinquapin 15% 16 Salix sitchensis Sitka willow 9% 10 Cornus nuttallii Pacific dogwood 7% 82 Quercus kelloggii California black oak 6% 72 Quercus garryana Oregon white oak 6% 62 Chamaecyparis lawsoniana Port Orford Cedar 5% 57 Quercus chrysolepis canyon live oak 5% 55 Calocedrus decurrens incense cedar 5% 52 Pinus sp. pine 5% 51 Taxus brevifolia Pacific yew 4% 46 Abies concolor white fir 4% 46 Pinus lambertiana sugar pine 4% 46 Salix lucida ssp. lasiandra Pacific willow 4% 44 Pinus muricata Bishop pine 4% 42 Pinus radiata x P. attenuata Monterey and knobcone cross 3% 34 Salix lasiolepis arroyo willow 3% 33 Pinus attenuata knobcone pine 2% 23 Pinus murtica x Pinus radiata Monterey and Bishop pine cross 2% 18 Abies magnifica var. magnifica California red fir 1% 15 Pinus ponderosa Ponderosa pine 1% 14 Populus balsamifera ssp. trichocarpa black cottonwood 1% 14 Salix scouleriana Scouler’s willow 1% 12 Fraxinus latifolia Oregon ash 1% 11 Salix hookeriana Hooker’s willow 1% 10 Quercus sp. oak 1% 9 Pinus jeffreyi Jeffery pine 1% 9 Pinus radiata Monterey pine 1% 6 Acacia sp. acacia 1% 6 Acer negundo var. californicum box elder 1% 6 Quercus agrifolia coast live oak 0% 5 Malus sylvestris cultivated apple

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count TREES 0% 4 Salix laevigata red willow 0% 3 Malus sp. apple 0% 2 Acacia dealbata silver wattle 0% 2 Aesculus californica California buckeye 0% 2 Pinus contorta ssp. contorta beach pine 0% 2 Prunus virginiana var. demissa western chokecherry 0% 1 Juglans sp. Walnut 0% 1 Malus fusca Oregon crab apple 0% 1 Pinus monticola western white pine 0% 1 Pinus sabiniana gray pine 0% 1 Quercus wislizeni interior live oak

%Occ. Count SHRUBS 77% 85 Gaultheria shallon salal 77% 85 Vaccinium ovatum evergreen huckleberry 72% 79 Vaccinium parvifolium red huckleberry 71% 78 Rubus ursinus Pacific bramble; California blackberry 64% 71 Berberis nervosa dwarf Oregon-grape 62% 68 Rubus parviflorus thimbleberry 61% 67 Baccharis pilularis coyote brush 58% 64 Rubus spectabilis salmonberry 54% 60 Rhododendron macrophyllum California rose-bay 51% 56 Rubus leucodermis white-stemmed raspberry 48% 52 Sambucus racemosa var. racemosa red elderberry 45% 50 Toxicodendron diversilobum poison-oak 42% 46 Ceanothus thyrsiflorus blue blossom 37% 41 Ribes sanguineum var. glutinosum pink flowering currant 35% 38 Rosa sp. rose 33% 36 Rubus discolor Himalayan blackberry 32% 35 Corylus cornuta var. californica California hazelnut 30% 33 Holodiscus discolor oceanspray 30% 32 Ribes bracteosum stink currant 26% 29 Arctostaphylos columbiana hairy manzanita 21% 23 Morella californica wax myrtle 19% 21 Ribes menziesii canyon gooseberry 19% 21 Rosa gymnocarpa wood rose 16% 17 Cytisus scoparius Scotch broom 15% 16 Acer circinatum vine maple 15% 16 Berberis aquifolium tall Oregon-grape 15% 16 Ilex aquifolium English holly 14% 15 Arctostaphylos sp. (not a rare) manzanita 14% 15 Euonymus occidentalis western burning bush 10% 11 Ribes sp. gooseberry 10% 11 Ribes laxiflorum trailing black currant

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count SHRUBS 8% 90 Aralia californica elk clover 8% 89 Symphoricarpos albus var. laevigatus common snowberry 8% 88 Ceanothus velutinus snow brush 7% 82 Oemleria cerasiformis oso berry 7% 79 Cotoneaster pannosa cotoneaster 7% 78 Ceanothus sp. California-lilac 6% 68 Lonicera involucrata var. ledebourii black twinberry 6% 67 Ribes roezlii Sierra gooseberry 6% 65 Genista monspessulana French broom 6% 63 Rubus laciniatus Dissected leaf blackberry 5% 54 Amelanchier alnifolia western service-berry 4% 49 Menziesia ferruginea False azalea 4% 49 Symphoricarpos sp. snowberry 4% 45 Sambucus sp. Elderberry 4% 39 Quercus berberidifolia scrub oak 3% 38 Frangula californica California coffeeberry 3% 38 Ribes lobbii gummy goosebeery 3% 34 Ceanothus foliosus var. foliosus wavyleaf ceanothus 3% 33 Cornus sericea American dogwood 3% 33 Gaultheria ovatifolia oval-leaved salal 3% 33 Ribes sanguineum red flowering current 3% 32 Arctostaphylos nevadensis pinemat manzanita 3% 30 Arctostaphylos manzanita var. elegans common manzanita 3% 30 Ribes sanguineum var. sanguineum red flowering currant 2% 25 Ceanothus cordulatus mountain whitethorn 2% 24 Alnus viridis ssp. sinuata Sitka alder 2% 24 Ceanothus integerrimus deer brush 2% 24 Mimulus aurantiacus orange bush monkey-flower 2% 24 Prunus emarginata bitter cherry 2% 24 Rhododendron occidentale western azalea 2% 22 Vaccinium membranaceum thinleaf huckleberry 2% 19 Ceanothus incanus coast whitethorn 2% 19 Paxistima myrsinites Oregon boxwood 2% 18 Ceanothus velutinus var. velutinus tobacco brush 2% 18 Sambucus mexicana blue elderberry 2% 17 Philadelphus lewisii wild mock-orange 1% 16 Prunus sp. plum or cherry 1% 14 Garrya elliptica coast silk tassel 1% 12 Chrysolepis chrysophylla var. minor dwarf chinquapin 1% 12 Phoradendron villosum oak mistletoe 1% 11 Erica lucitanica weedy heath 1% 11 Rubus sp. bramble 1% 10 Garrya fremontii bearbrush, Fremont's silk tassel 1% 10 Spiraea douglasii Douglas’ spiraea

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count SHRUBS 1% 9 Amelanchier utahensis Utah service-berry 1% 8 Arctostaphylos canescens ssp. canescens hoary manzanita 1% 7 Ceanothus velutinus var. hookeri snow brush 1% 7 Lonicera sp. twinberry 1% 6 Cornus sp. dogwood 1% 6 Ledum glandulosum western Labrador tea 1% 6 Physocarpus capitatus Pacific ninebark 0% 5 Amelanchier sp. service berry 0% 5 Arctostaphylos viscida white-leaved manzanita 0% 5 Rosa eglanteria sweet brier 0% 4 Alnus incana ssp. tenuifolia Mountain alder 0% 4 Arctostaphylos canescens ssp. sonomensis Sonoma manzanita 0% 4 Ribes lacustre Swamp current 0% 4 Rosa nutkana var. nutkana Nootka rose 0% 3 Garrya buxifolia boxleaf silk tassel 0% 3 Heteromeles arbutifolia toyon 0% 3 Quercus vaccinifolia huckleberry oak 0% 2 Arctostaphylos nortensis Del Norte manzanita 0% 2 Aruncus dioicus var. pubescens goat’s beard 0% 2 Berberis sp. Oregon grape 0% 2 Ceanothus cuneatus var. cuneatus buck brush 0% 2 Ceanothus oliganthus var. sorediatus jim brush 0% 2 Fuschia sp. fushia 0% 2 Salix exigua narrow-leaved willow 0% 2 Sorbus scopulina var. scopulina mountain ash 0% 2 Vaccinium caespitosum dwarf bilberry 0% 1 Arctostaphylos glandulosa Eastwood’s manzanita 0% 1 Arctostaphylos nortensis (unconfirmed) Del Norte manzanita 0% 1 Buddleja davidii butterfly bush; summer lilac 0% 1 Chrysolepis sempervirens bush chinquapin 0% 1 Empetrum nigrum ssp. hermaphroditum black crowberry 0% 1 Gaultheria sp. salal 0% 1 Holodiscus dumosus rock spirea 0% 1 Quercus garryana var. breweri Brewer's oak

%Occ. Count LICHEN/BRYOPHYTES 3% 29 Usnea longissima long-beard lichen 0% 1 Lobaria pulmonaria Lungwort

%Occ. Count HERBACEOUS 79% 87 Polystichum munitum sword fern 72% 79 Viola sempervirens evergreen violet 72% 79 Claytonia sibirica candy flower 71% 78 Pteridium aquilinum var. pubescens western bracken fern

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 70% 77 Oxalis oregana redwood sorrel 68% 75 Trillium ovatum western trillium 68% 75 Blechnum spicant deer fern 67% 74 Whipplea modesta yerba de selva 67% 74 Trientalis latifolia Pacific star flower 66% 73 Athyrium filix-femina var. cyclosorum lady fern 65% 72 Asarum caudatum wild ginger 63% 69 Galium sp. bedstraw 57% 63 Hypochaeris radicata hairy cat’s-ear 57% 63 Prunella vulgaris self-heal 56% 62 Holcus lanatus common velvet grass 54% 60 Cardamine californica California toothwort; milk maids 54% 60 Juncus effusus common rush 52% 57 Petasites frigidus var. palmatus western coltsfoot 52% 57 Anaphalis margaritacea pearly everlasting 52% 57 Iris sp. iris 48% 52 Plantago lanceolata English plantain 45% 49 Tolmiea diplomenziesii youth-on-age; pig-a-back plant 44% 49 Anthoxanthum odoratum sweet vernal grass 44% 48 Adiantum aleuticum five-fingered fern 42% 47 Hierochloe occidentalis vanilla grass 42% 46 Lonicera hispidula var. vacillans hairy honeysuckle 42% 46 Cirsium sp. thistle 41% 46 Leucanthemum vulgare ox-eye daisy 41% 45 Hieracium albiflorum white hawkweed 40% 44 Luzula parviflora small-flowered wood rush 40% 44 Cortaderia jubata weedy pampas grass 40% 44 Osmorhiza berteroi mountain sweet-cicely 39% 43 Fragaria vesca wood strawberry 38% 42 Cirsium vulgare bull thistle 38% 42 Erechtites minima toothed coast fireweed 38% 41 Digitalis purpurea foxglove 38% 41 Equisetum arvense common horsetail 38% 41 Sanicula crassicaulis Pacific snakeroot 37% 41 Dryopteris expansa wood fern 37% 40 Goodyera oblongifolia rattlesnake plantain 36% 39 Achillea millefolium common yarrow 35% 39 Stachys ajugoides hedge nettle 35% 38 Disporum sp. fairy bells 35% 38 Lilium sp. lily 34% 37 Vancouveria hexandra northern inside-out flower 33% 37 Luzula comosa common wood rush 33% 37 Rumex crispus curly dock 33% 36 Cynosurus echinatus hedgehog dogtail

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 33% 36 Bellis perennis English daisy 32% 35 Lupinus sp. lupine 32% 35 Carex sp. (not a rare) sedge 32% 35 Ranunculus repens creeping buttercup 32% 35 Carex deweyana ssp. leptopoda short-scaled sedge 32% 35 Trifolium sp. clover 31% 34 Adenocaulon bicolor trail plant 31% 34 Boykinia occidentalis coast boykinia 31% 34 Dicentra formosa Pacific bleeding heart 31% 33 Rumex acetosella sheep sorrel 30% 33 Madia sp. tarweed 30% 33 Cardamine oligosperma western bittercress 30% 33 Scoliopus bigelovii slink-pod 30% 33 Aira caryophyllea silver European hairgrass 29% 32 Ranunculus sp. buttercup 28% 31 Oenanthe sarmentosa Pacific water-parsley 28% 31 Mimulus dentatus toothed monkey flower 28% 31 Dactylis glomerata orchard grass 28% 31 Listera cordata heart-leaved twayblade 27% 30 Carex obnupta slough sedge 27% 30 Lotus aboriginus rose-flowered lotus 27% 30 Maianthemum racemosum branched Solomon's seal 27% 30 Stachys sp. hedge nettle 27% 29 Stellaria crispa crisp chickweed 27% 29 Tellima grandiflora fringe cups 27% 29 Veronica americana American brooklime 26% 29 Clinopodium (Satureja) douglasii yerba buena 26% 29 Clintonia andrewsiana bead lily 25% 28 Mentha pulegium pennyroyal 25% 27 Pectiantia ovalis coastal mitrewort 25% 27 Taraxacum officinale dandelion 24% 27 Lycopodium clavatum running-pine 24% 26 Galium aparine goose grass 24% 26 Pyrola picta white-veined wintergreen 24% 26 Gnaphalium sp. cudweed 23% 25 Iris douglasiana Douglas iris 23% 25 Scirpus microcarpus small-flowered bulrush 23% 25 Poa annua annual bluegrass 23% 25 Viola glabella smooth violet 22% 24 Corallorhiza maculata spotted coralroot 22% 24 Lotus sp. lotus 22% 24 Calypso bulbosa calypso orchid; fairy slipper orchid 22% 24 Hypericum perforatum Klamath weed or common St. John’s- 22% 24 Agrostis sp. bent grass

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 22% 24 Epilobium sp. fireweed; willow herb 22% 24 Lotus micranthus rose-flowered lotus 20% 22 Campanula prenanthoides California harebell 20% 21 Urtica dioica ssp. holosericea stinging nettle 20% 21 Gnaphalium purpureum purple cudweed 20% 21 Vicia sp. vetch 19% 21 Hypochaeris glabra smooth cat's-ear 19% 21 Juncus sp. rush 19% 21 Circaea alpina ssp. pacifica enchanter’s nightshade 19% 20 Epilobium ciliatum northern willow herb 19% 20 Pentagramma triangularis ssp. triangularis goldback fern 19% 20 Scrophularia californica coast figwort 18% 20 Bromus sp. brome 18% 20 Achlys triphylla ssp. triphylla vanilla leaf 18% 19 Lysichiton americanum skunk cabbage 18% 19 Maianthemum stellatum star Solomon's seal 18% 19 Marah sp. wild cucumber 18% 19 Linum bienne western blue flax 18% 19 Plantago major common plantain 18% 19 Lathyrus sp. pea 18% 19 Poa sp. bluegrass 17% 19 Disporum smithii (new Prosartes smithii) Smith’s fairy bells 17% 19 Kopsiopsis (Boschniakia) strobilacea California ground-cone 17% 19 Vancouveria sp. inside-out flower 17% 18 Cynoglossum grande hound’s-tongue 17% 18 Heuchera micrantha small-flowered alumroot 17% 18 Trifolium repens white clover 17% 18 Woodwardia fimbriata giant chain fern 16% 18 Polypodium sp. polypody 16% 18 Collomia heterophylla varied-leaf collomia 16% 17 Corallorhiza sp. coralroot 16% 17 Festuca sp. fescue 16% 17 Juncus patens spreading rush 15% 17 Carex hendersonii Henderson’s sedge 15% 16 Achlys californica California deer foot; vanilla leaf 15% 16 Equisetum telmateia ssp. braunii giant horsetail 15% 16 Disporum hookeri (new Prosartes hookeri) Hooker’s fairy bells 15% 16 Nemophila sp. nemophila 14% 15 Juncus bufonius common toad rush 14% 15 Elymus glaucus blue wildrye 14% 15 Lotus corniculatus birdfoot trefoil 14% 15 Trifolium dubium little hop clover; shamrock clover 14% 15 Veronica sp. speedwell 14% 15 Lathyrus vestitus wood pea

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 14% 15 Nemophila parviflora small-flowered nemophila 14% 15 Pityopus californicus California pinefoot 13% 14 Aquilegia formosa crimson columbine 13% 14 Hydrophyllum tenuipes Pacific waterleaf 13% 14 Xerophyllum tenax bear-grass 13% 14 Anemone deltoidea Columbia windflower 13% 14 Polypodium glycyrrhiza licorice fern 13% 14 Chimaphila umbellata prince’s pine 13% 13 Actaea rubra baneberry 12% 13 Parentucellia viscosa yellow parentucellia 12% 13 Lilium columbianum Columbia lily 12% 13 Spergularia rubra purple sand spurry 12% 13 Luzula sp. wood rush 12% 13 Pyrola picta forma aphylla leafless wintergreen 12% 13 Lolium multiflorum Italian ryegrass 12% 13 Claytonia perfoliata miner’s lettuce 12% 13 Lolium perenne perennial ryegrass 11% 12 Geranium dissectum cut-leaved geranium 11% 12 Glyceria elata tall mannagrass 11% 12 Vancouveria planipetala redwood inside-out flower 11% 12 Synthyris reniformis snow queen 11% 12 Vicia sativa common vetch 10% 11 Montia fontana water montia 10% 11 Festuca arundinacea tall fescue 10% 11 Gnaphalium japonicum Japanese cudweed 10% 11 Smilacina sp. false Solomon's seal 10% 11 Dryopteris arguta coastal wood fern 10% 11 Maianthemum dilatatum false lily-of-the-valley 10% 10 Bromus carinatus California brome 10% 10 Cynosurus sp. dogtail grass 10% 10 Danthonia californica California oatgrass 10% 10 Lupinus rivularis riverbank lupine 9% 10 Daucus carota wild carrot or Queen Anne’s lace 9% 10 Navarretia squarrosa skunkweed 9% 10 Galium triflorum sweet-scented bedstraw 9% 10 Bromus vulgaris narrow-flowered brome 9% 10 Phacelia bolanderi Bolander’s phacelia 9% 99 Mitellastra caulescens leafy-stemmed mitrewort 9% 98 Cerastium sp. chickweed 9% 98 Sonchus sp. sow thistle 9% 97 Tiarella trifoliata var. unifoliata sugar scoop; lace flower 9% 95 Bromus hordeaceus soft chess 8% 92 Elymus sp. wildrye 8% 91 Briza minor small quaking grass; rattlesnake grass

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 8% 91 Cerastium glomeratum mouse ear chickweed 8% 91 Leontodon taraxacoides hawkbit 8% 91 Scirpus sp. bulrush 8% 89 Listera caurina northwest twayblade 8% 89 Ranunculus californicus California buttercup 8% 88 Anagallis arvensis scarlet pimpernel 8% 88 Lathyrus torreyi redwood pea; Torrey’s pea 8% 84 Senecio sylvaticus wood groundsel 7% 83 Cyperus eragrostis nut-grass; tall flat-sedge 7% 81 Mimulus sp. monkey flower 7% 80 Briza maxima large quaking grass; rattlesnake grass 7% 80 Centaurium muhlenbergii Monterey centaury 7% 79 Deschampsia elongata slender hairgrass 7% 79 Vulpia bromoides six week fescue 7% 78 Cephalanthera austiniae phantom orchid 7% 78 Lolium sp ryegrass 7% 77 Chrysosplenium glechomifolium golden saxifrage 7% 77 Juncus ensifolius dagger-leaf rush 7% 75 Cirsium arvense Canada thistle 7% 75 Hedera helix English ivy 7% 74 Corallorhiza mertensiana western coralroot 7% 74 Marah oreganus coast man-root 7% 73 Monotropa hypopitys pine sap 7% 73 Stellaria media common chickweed 6% 71 Vulpia sp. annual fescue 6% 70 Juncus bolanderi Bolander’s rush 6% 69 Callitriche sp. water starwort 6% 69 Chimaphila menziesii Little Prince's pine 6% 69 Polygala californica California milkwort 6% 68 Senecio sp. groundsel; ragwort; butterweed 6% 67 Phacelia sp. phacelia 6% 67 Pleuropogon refractus nodding semaphore grass 6% 67 Ranunculus occidentalis western buttercup 6% 66 Monotropa uniflora Indian-pipe 6% 65 Equisetum hyemale ssp. affine common scouring rush 6% 63 Picris echioides bristly ox-tongue 6% 62 Heracleum lanatum cow parsnip 6% 62 Ranunculus uncinatus little buttercup 5% 61 Anemone sp. anemone 5% 61 Geranium sp. geranium 5% 60 Achlys sp. deer foot 5% 59 Cerastium arvense field chickweed 5% 59 Conyza canadensis horseweed 5% 59 Epilobium angustifolium (new Chamerion red fireweed

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 5% 58 Pleuricospora fimbriolata fringed pine-sap 5% 56 Erythronium revolutum coast fawn lily 5% 56 Ranunculus parviflorus small-flowered buttercup 5% 55 Mimulus guttatus seep-spring monkey flower 5% 54 Erechtites sp. fireweed 5% 54 Senecio jacobaea tansy ragwort 5% 53 Galium trifidum trifid bedstraw 5% 53 Hierochloe sp. vanilla grass 5% 53 Nemophila menziesii baby blue-eyes 5% 53 Vicia tetrasperma slender vetch 5% 52 Chlorogalum pomeridianum var. soap plant 5% 52 Hypericum anagalloides bog St. John’s-wort or tinker’s-penny 5% 51 Festuca occidentalis western fescue 5% 51 Melilotus alba white sweetclover 5% 50 Sidalcea malachroides maple-leaved checkerbloom 5% 50 Stellaria sp. chickweed 4% 49 Triphysaria pusilla dwarf orthocarpus 4% 48 Carex gynodynama Olney’s hairy sedge 4% 48 Melica sp. oniongrass 4% 47 Glyceria sp. mannagrass 4% 47 Polystichum imbricans imbricated sword fern 4% 47 Spergularia sp. sand spurry 4% 46 Conyza sp. horseweed 4% 46 Cynosurus cristatus crested dogtail 4% 46 Rorippa nasturtium-aquaticum water cress 4% 45 Sanicula sp. sanicle 4% 42 Delphinium sp. larkspur 4% 42 Sisyrinchium bellum blue-eyed-grass 4% 41 Corallorhiza striata striped coralroot 4% 40 Hypericum sp. St. John’s-wort 4% 40 Polypodium scouleri leather-leaf fern 4% 40 Vicia americana var. americana American vetch 4% 40 Viola sp. violet 4% 39 Equisetum sp. 4% 39 Erodium sp. stork's-bill 4% 39 Mentha sp. field mint 3% 38 Boykinia major Mountain boykinia 3% 38 Sedum sp. stonecrop 3% 37 Allotropa virgata sugar-stick 3% 37 Hemitomes congestum gnome plant 3% 37 Linnaea borealis var. longiflora twin flower 3% 37 Madia madioides woodland madia 3% 37 Symphoricarpos mollis creeping snowberry 3% 36 Apocynum androsaemifolium bitter dogbane

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 3% 36 Aster sp. aster 3% 36 Carex bolanderi Bolander’s sedge 3% 36 Coptis laciniata Oregon goldthread 3% 36 Daucus sp. wild carrot 3% 36 Trisetum canescens (old T. cernuum) Nodding oat grass 3% 35 Avena sp. Oatgrass 3% 35 Melica subulata Alaska oniongrass 3% 35 Mimulus moschatus musk monkey flower 3% 35 Potentilla glandulosa sticky cinquefoil 3% 35 Stipa occidentalis var. pubescens western needlegrass 3% 35 Streptopus amplexifolius clasping twisted-stalk 3% 34 Dichelostemma ida-maia firecracker flower 3% 33 Clintonia uniflora single-flowered clintonia 3% 33 Deschampsia sp. tufted hair grass 3% 33 Montia howellii Howell’s montia 3% 32 Torreyochloa pallida var. pauciflora weak mannagrass 3% 31 Melica bulbosa western melica; oniongrass 3% 31 Rumex salicfolius willow dock 3% 31 Sonchus asper ssp. asper prickly sow thistle 3% 30 Deschampsia cespitosa tufted hairgrass 3% 30 Moehringia macrophylla large-leaved sandwort 3% 29 Chamomilla suaveolens (new Matricaria pineapple weed 3% 29 Plantago subnuda Plantago 3% 29 Sonchus oleraceus common sow thistle 3% 28 Callitriche marginata California water-starwort 3% 28 Gnaphalium collinum creeping cudweed 3% 28 Heuchera sp. Alum root 2% 27 Festuca californica California fescue 2% 27 Polypodium californicum California polypody 2% 27 Selaginella wallacei Wallace's spike-moss 2% 26 Lithophragma affine woodland star 2% 26 Polypogon monspeliensis rabbitfoot grass; annual beard grass 2% 26 Polypogon sp. beard grass 2% 26 Silene californica Indian pink 2% 25 Centaurium davyi Davy’s centaury 2% 25 Claytonia parviflora ssp. parviflora small-leaved claytonia 2% 25 Iris purdyi Purdy’s iris 2% 25 Tiarella trifoliata var. trifoliata sugar scoop; lace flower 2% 25 Trillium chloropetalum giant trillium 2% 24 Anemone oregana windflower 2% 24 Festuca subuliflora crinkle-awn fescue 2% 24 Iris tenuissima ssp. tenuissima slender-tubed iris 2% 24 Lotus purshianus spanish lotus 2% 24 Viola adunca western dog violet

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 2% 23 Baccharis douglasii marsh baccharis 2% 23 Dichelostemma capitatum blue dicks 2% 23 Fritillaria affinis var. affinis checker lily 2% 23 Lomatium sp. lomatium 2% 23 Piperia transversa green striped piperia 2% 22 Bromus diandrus ripgut grass 2% 22 Campanula scouleri Scouler's harebell 2% 22 Erodium botrys long-beaked stork's-bill 2% 22 Geum macrophyllum large-leaved avens 2% 22 Marah fabaceus California man-root 2% 22 Penstemon sp. beardtongue 2% 22 Potentilla sp. cinquefoil 2% 22 Rosa californica California rose 2% 22 Saxifraga mertensiana Merten’s saxifrage 2% 22 Torilis arvensis field hedge-parsley; rattlesnake weed 2% 21 Poa pratensis Kentucky bluegrass 2% 21 Trillium sp. 2% 21 Veronica peregrina ssp. xalapensis purslane speedwell 2% 20 Brodiaea terrestris ssp. terrestris dwarf brodiaea 2% 20 Bromus anomalis nodding brome 2% 20 Dipsacus fullonum wild teasel 2% 20 Dodecatheon hendersonii Henderson’s shooting star 2% 20 Eschscholzia californica California poppy 2% 20 Lapsana communis nipplewort 2% 20 Piperia sp. piperia 2% 19 Aira sp. Hairgrass 2% 19 Allium sp. wild onion 2% 19 Calochortus tolmiei pussy ears 2% 19 Phlox adsurgens woodland phlox 2% 19 Trifolium albopurpureum common Indian clover 2% 19 Veronica persica Persian speedwell 2% 19 Veronica serpyllifolia ssp. humifusa thyme-leaved speedwell 2% 18 Boykinia sp. 2% 18 Brodiaea sp. brodiaea 2% 18 Cerastium fontanum ssp. vulgare large mouse-ear chickweed 2% 18 Conium maculatum poison hemlock 2% 18 Phalaris arundinacea reed canary grass 2% 18 Veratrum sp. corn lily 2% 17 Botrychium multifidum leather grape-fern 2% 17 Lupinus bicolor miniature lupine 2% 17 Poa kelloggii Kellogg’s bluegrass 2% 17 Sherardia arvensis field madder 2% 17 Typha latifolia broadleaf cattail 1% 16 Prunella vulgaris var. vulgaris self-heal (exotic)

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 1% 16 Pyrola sp. wintergreen 1% 16 Thermopsis robusta robust false-lupine 1% 16 Tragopogon sp. goat’s beard; salsify 1% 15 Avena barbata slender wild oat 1% 15 Crepis capillaris hawksbeard 1% 15 Erythronium californicum California fawn lily 1% 15 Listera convallarioides broad-leaved twayblade 1% 15 Senecio triangularis 1% 15 Trisetum sp. 1% 14 Artemesia douglasiana mugwort 1% 14 Astragalus umbraticus Bald Mountain milk-vetch 1% 14 Avena fatua wild oat 1% 14 Brassica rapa field mustard 1% 14 Eriogonum sp. wild buckwheat 1% 14 Hemizonia corymbosa coast tarweed 1% 14 Montia parvifolia streambank spring beauty 1% 14 Oxalis suksdorfii Suksdorf’s wood-sorrel 1% 14 Sarcodes sanguinea Snow plant 1% 13 Aconitum columbianum monkshood 1% 13 Adiantum jordanii California maiden-hair fern 1% 13 Delphinium trolliifolium cow poison 1% 13 Deschampsia cespitosa ssp. cespitosa tufted hair-grass 1% 13 Erodium cicutarium red-stemmed filaree; common stork's- 1% 13 Gastridium ventricosum nit grass 1% 13 Gilia sp. gilia 1% 13 Hordeum sp. wild barley 1% 13 Lemna sp. duckweed 1% 13 Senecio vulgaris common butterweed 1% 13 Thermopsis gracilis var. gracilis slender false lupine 1% 13 Vicia sativa ssp. nigra narrow-leaved vetch 1% 12 Caltha leptosepala var. biflora marsh marigold 1% 12 Cirsium occidentale western thistle 1% 12 Lactuca sp. wild lettuce 1% 12 Lathyrus polyphyllus leafy pea 1% 12 Lupinus elmeri South Fork Mtn lupine 1% 12 Madia gracilis slender tarweed 1% 12 Myosotis latifolia forget-me-not 1% 12 Phalaris sp. canary grass 1% 12 Platanthera sp. bog orchid 1% 12 Poa bulbosa bulbous bluegrass 1% 12 Pyrola asarifolia bog wintergreen 1% 11 Agrostis exarata western bent grass 1% 11 Aira praecox narrow European hairgrass 1% 11 Camassia quamash ssp. quamash common camas

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 1% 11 Carex globosa round-fruited sedge 1% 11 Carex hartfordii Hartford’s sedge 1% 11 Clarkia sp. clarkia 1% 11 Collomia sp. collomia 1% 11 Festuca subulata bearded fescue 1% 11 Galium californicum California bedstraw 1% 11 Glyceria occidentalis western mannagrass 1% 11 Lilium pardalinum ssp. pardalinum leopard lily 1% 11 Lupinus nanus sky lupine 1% 11 Madia minima hemizonella 1% 11 Madia sativa coast tarweed 1% 11 Piperia candida white-flowered piperia 1% 11 Plagiobothrys sp. popcorn flower 1% 11 Platanthera stricta Bog orchid 1% 11 Silybum marianum milk thistle 1% 11 Trifolium pratense red clover 1% 11 Vicia sativa ssp. sativa common vetch; spring vetch 1% 10 Brassica sp. 1% 10 Brodiaea elegans 1% 10 Calandrinia cilata red maids 1% 10 Delphinium nudicaule canyon delphinium 1% 10 Deschampsia danthoides Annual Hairgrass 1% 10 Eriophyllum lanatum woolly sunflower 1% 10 Melissa officinalis lemon balm 1% 10 Oxalis sp. sorrel 1% 10 Ranunculus sardous hairy buttercup 1% 10 Raphanus sativus wild radish 1% 10 Scirpus cernuus low club-rush 1% 10 Spiranthes romanzoffiana lady’s tresses 1% 10 Vicia hirsuta hairy vetch 1% 9 Aspidotis densa Indian's dream 1% 9 Avena sativa cultivated oat 1% 9 Campanula sp. campanula 1% 9 Carex aquatilis water sedge 1% 9 Carex multicostata many-ribbed sedge 1% 9 Danthonia sp. oat grass 1% 9 Erythronium sp. Fawn lily 1% 9 Lathyrus/Vicia sp. 1% 9 Sanicula bipinnata poison sanicle 1% 9 Solanum sp. nightshade 1% 9 Stellaria borealis ssp. sitchana northern starwort 1% 9 Thelypteris nevadensis Sierra marsh fern 1% 9 Trillium albidum sessile, green-stamened trillium 1% 9 Ulex europaea gorse

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 1% 9 Veratrum californicum var. californicum corn lily 1% 9 Verbascum thapsus woolly mullein 1% 8 Achnatherum lemonii lemon needlegrass 1% 8 Alopecurus geniculatus water foxtail 1% 8 Bensoniella oregona Benson’s saxifrage; bensoniella 1% 8 Carex tumulicola foothill sedge 1% 8 Cornus canadensis bunchberry 1% 8 Foeniculum vulgare fennel 1% 8 Hordeum jubatum foxtail barley 1% 8 Kopsiopsis (Boschniakia) hookeri small groundcone 1% 8 Listera sp. twayblade 1% 8 Lupinus albifrons silver lupine 1% 8 Myosotis sp forget-me-not 1% 8 Penstemon rattanii var. rattanii gray beardtongue 1% 8 Plectritis brachystemon pink plectritis 1% 8 Silene sp. catchfly; campion 1% 8 Viola sheltonii Shelton's violet 1% 7 Achnatherum sp. needlegrass 1% 7 Agrostis stolonifera creeping bent 1% 7 Anthoxanthum aristatum annual sweet vernal grass 1% 7 Bromus tectorum cheat grass 1% 7 Carex subfusca rusty sedge 1% 7 Centaurium sp. Centaury 1% 7 Cheilanthes gracillima lip fern 1% 7 Claytonia rubra redstem spring beauty 1% 7 Collinsia sp. collinsia 1% 7 Convolvulus arvensis field bindweed 1% 7 Cryptantha/Plagiobothyrs sp. 1% 7 Epilobium brachycarpum parched fireweed 1% 7 Festuca rubra red fescue 1% 7 Fragaria sp. strawberry 1% 7 Geranium molle dovefoot geranium 1% 7 Lathyrus latifolius everlasting pea 1% 7 Linanthus bicolor (new Leptoshiphon baby stars 1% 7 Lupinus latifolius broad-leaved lupine 1% 7 Medicago polymorpha bur clover 1% 7 Orthilia secunda one-sided wintergreen 1% 7 Packera bolanderi var. bolanderi seacoast ragwort 1% 7 Prunella vulgaris var. lanceolata self-heal (native) 1% 7 Scirpus setaceous annual bulrush 1% 7 Senecio integerrimus var. major butterweed 1% 7 Sidalcea sp. checkerbloom 1% 7 Stachys chamissonis Chamisso’s hedge nettle 1% 7 Triticum sp. wheat grass

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 1% 7 Viola purpurea ssp. quercetorum mountain violet 1% 6 Anthemis cotula mayweed 1% 6 Arrhenatherum elatius tall oatgrass 1% 6 Carex echinata star sedge 1% 6 Carex rossii Ross’ sedge 1% 6 Convolvulus sp. morning-glory 1% 6 Erysimum sp. wallflower 1% 6 Phleum pratense cultivated Timothy 1% 6 Polygonum sp. knotweed 1% 6 Sagina procumbens pearlwort 1% 6 Sidalcea malviflora ssp. asprella harsh sidalcea 1% 6 Trifolium subterraneum subterranean clover 1% 6 Triticum aestivum wheat 1% 6 Vicia gigantea giant vetch 1% 6 Vinca major greater periwinkle 0% 5 Carex vesicaria blister sedge 0% 5 Dipsacus sativus Fuller's teasel 0% 5 Eleocharis sp. spike-rush 0% 5 Elymus glaucus ssp. glaucus blue wildrye 0% 5 Epilobium minutum minute willow herb 0% 5 Fragaria chiloensis beach strawberry 0% 5 Gnaphalium luteo-album weedy cudweed 0% 5 Lamium purpureum red henbit 0% 5 Lilium kelloggii Kellogg’s lily 0% 5 Medicago sp. bur clover 0% 5 Mimulus alsinoides chickweed monkey flower 0% 5 Montia diffusa diffuse montia 0% 5 Phleum alpinum Mountain phleum 0% 5 Plectritis sp. plectritis 0% 5 Spergula arvensis ssp. arvensis stickwort 0% 5 Triantha occidentalis supsp. Occidentalis western tofieldia 0% 5 Trillium rivale brook wake robin 0% 4 Angelica genuflexa kneeling angelica 0% 4 Anthemis arvensis field chamomile 0% 4 Aquilegia sp. columbine 0% 4 Artemesia sp. mugwort/tarragon/wormwood 0% 4 Botrychium sp. grape fern 0% 4 Calochortus elegans cat's ear 0% 4 Calyptridium umbellatum pussy paws 0% 4 Carex arcta northern clustered sedge 0% 4 Castilleja sp. Indian paintbrush 0% 4 Clarkia purpurea ssp. quadrivulnera four-spot 0% 4 Eriodictyon californicum yerba santa 0% 4 Eriogonum nudum naked-stemmed buckwheat

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 0% 4 Geranium robertianum Robert’s geranium 0% 4 Gilia capitata ssp. pacifica Pacific gilia 0% 4 Iris innominata Del Norte County iris 0% 4 Lonicera ciliosa honeysuckle 0% 4 Melilotus sp. sweetclover 0% 4 Mitella sp. mitrewort 0% 4 Narcissus sp. domestic daffodil 0% 4 Nemophila pedunculata meadow nemophila 0% 4 Oxalis pes-carpae Bermuda buttercup 0% 4 Penstemon anguineus Siskiyou penstemon 0% 4 Perideridia sp. yampa 0% 4 Phalaris californica California canary grass 0% 4 Phleum sp. 0% 4 Polypogon interruptus ditch rabbitfood grass 0% 4 Romanzoffia californica Romanzoffia 0% 4 Silene gallica windmill pink or common catchfly 0% 4 Trifolium willdenovii tomcat clover 0% 4 Triteleia bridgesii Tritelia 0% 4 Triteleia hyacinthina white hyacinth 0% 4 Triteleia laxa Ithuriel’s spear 0% 4 Vicia benghalensis purple vetch 0% 4 Zigadenus fremontii var fremontii (new fremont’s death camas 0% 3 Acaena novae-zelandiae biddy-biddy 0% 3 Alopecurus pratensis meadow foxtail 0% 3 Alopecurus saccatus Pacific foxtail 0% 3 Arnica discoidea rayless arnica 0% 3 Asarum hartweggii Hartwegg's ginger 0% 3 Calamagrostis nutkaensis Pacific reed grass 0% 3 Calochortus amabilis Diogenes' lantern 0% 3 Carex amplifolia bigleaf sedge 0% 3 Carex leptalea bristle-stalked sedge 0% 3 Cheilanthes sp. Lip fern 0% 3 Clarkia amoena farewell-to-spring 0% 3 Clintonia sp. Clintonia 0% 3 Crocosmia xcrocosmiiflora crocosmia 0% 3 Darlingtonia californica California pitcher plant 0% 3 Dichelostemma congestum ookow 0% 3 Dryopteris sp. wood fern 0% 3 Erigeron sp. fleabane daisy 0% 3 Erythronium oregonum Oregon fawn lily 0% 3 Euphorbia sp. spurge 0% 3 Gilia capitata blue field gilia 0% 3 Gnaphalium californicum California cudweed 0% 3 Gnaphalium canescens ssp. beneolens white cudweed

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 0% 3 Gnaphalium ramosissimum pink everlasting 0% 3 Juncus xiphioides iris leaf rush 0% 3 Kopsiopsis (Boschniakia) hookeri small groundcone 0% 3 Lilium rubescens redwood lily 0% 3 Linanthus sp. linanthus 0% 3 Lomatium howellii Howell's lomatium 0% 3 Lupinus albicaulis sickle-keeled lupine 0% 3 Melilotus officinalis yellow sweet clover 0% 3 Najas flexilis slender water-nymph 0% 3 Pedicularis densiflora Indian warrior 0% 3 Phacelia californica California phacelia 0% 3 Poa trivialis rough bluegrass 0% 3 Ranunculus flammula flamulated buttercup; creeping 0% 3 Ribes divaricatum struggly gooseberry 0% 3 Rorippa sp. cress 0% 3 Rumex sp. 0% 3 Rupertia physodes forest scurf pea or California tea 0% 3 Sanguisorba officinalis great burnet 0% 3 Sanicula arctopoides footsteps of spring 0% 3 Sanicula bipinnatifida purple sanicle 0% 3 Sidalcea malviflora ssp. patula Siskiyou checkerbloom 0% 3 Sisyrinchium douglasii Douglas’ yellow-eyed-grass 0% 3 Thermopsis sp. false lupine 0% 3 Trifolium arvense Rabbitfoot clover 0% 3 Trillium angustipetalum narrowpetal wakerobin 0% 3 Viola praemorsa canary violet 0% 2 Allium triquetrum ornamental onion 0% 2 Arnica sp. 0% 2 Asarum marmoratum marbled wild-ginger 0% 2 Brodiaea coronaria ssp. coronaria harvest brodiaea 0% 2 Calochortus sp. cat's ear 0% 2 Calyptridium monospermum Pussypaws 0% 2 Calyptridium sp. pussy paws 0% 2 Cardamine hirsuta bitter-cress 0% 2 Cardamine sp. bitter-cress 0% 2 Ceanothus pumilus Siskiyou mat 0% 2 Cryptantha intermedia common cryptantha 0% 2 Cuscuta sp. dodder 0% 2 Cypripedium fasciculatum lady slipper 0% 2 Darmera peltata Indian rhubarb 0% 2 Daucus pusillus rattlesnake weed 0% 2 Eleocharis pachycarpa black sand spike-rush 0% 2 Eriogonum nudum var. oblongifolium naked or oblong leaved buckwheat 0% 2 Erodium brachycarpum long-beaked filaree

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 0% 2 Erythronium grandiflorum Glacier Lily 0% 2 Erythronium howellii Howell's fawn lily 0% 2 Eurybia radulina (Aster radulinus) broad-leaved aster 0% 2 Festuca idahoensis Idahoe fescue 0% 2 Galium parisiense wall bedstraw 0% 2 Gentiana sceptrum king's scepter 0% 2 Gilia capitata ssp. capitata pacific gilia 0% 2 Glyceria striata Fowl mannagrass 0% 2 Isopyrum stipitatum (new genus Enemion) Siskiyou rue-anemone 0% 2 Lathyrus glandulosus sticky pea 0% 2 Lilium bolanderi Bolander's lily 0% 2 Limnanthes douglasii Douglas’ meadowfoam 0% 2 Lotus angustissimus annual birdfoot trefoil 0% 2 Lotus pinnatus Lotus 0% 2 Luzula subcongesta 0% 2 Melica hartfordii Hartford’s melica 0% 2 Mimulus cardinalis scarlet monkey flower 0% 2 Mitella pentandra Five-stemmed mitrewort 0% 2 Nemophila menziesii var. atomaria white-flowered baby blue-eyes 0% 2 Nuphar lutea ssp. polysepala pond-lily 0% 2 Phacelia heterophylla var. virgata varied-leaf phacelia 0% 2 Piperia elegans elegant piperia 0% 2 Plantago sp. Plantago 0% 2 Polygonum bistortoides western bistort 0% 2 Pyrola asarifiolia ssp. bracteata wintergreen 0% 2 Ranunculus muricatus prickly-fruit buttercup 0% 2 Romanzoffia sitchensis Sitka romanzoffia 0% 2 Saxifraga sp. 0% 2 Sidalcea oregana ssp. eximia coast checkerbloom 0% 2 Silene campanulata catchfly 0% 2 Sisyrinchium idahoense Idaho blue-eyed grass 0% 2 Solidago sp. goldenrod 0% 2 Streptanthus sp. jewel flower 0% 2 Symphyotrichum chilensis (aster) common California aster 0% 2 Thalictrum fendleri var. polycarpum meadow rue 0% 2 Triphysaria sp. Triphysaria 0% 2 Triphysaria versicolor yellow owl's clover 0% 2 Triteleia sp. 0% 2 Verbena lasiostachys western verbena 0% 2 Veronica arvensis speedwell 0% 2 Viola ocellata two-eyed violet or western heart’s ease 0% 1 Adiantum sp. 0% 1 Allium falcifolium scytheleaf onion 0% 1 Allium validum Onion

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 0% 1 Alopecurus sp. foxtail 0% 1 Aphanes occidentalis dew drops 0% 1 Arabis mcdonaldiana McDonald's rock cress 0% 1 Azolla sp. mosquito fern 0% 1 Calamagrostis sp. reed grass 0% 1 Calystegia occidentalis chaparral false bindweed 0% 1 Calystegia sp. morning glory 0% 1 Camissonia ovata coast sun cup 0% 1 Camissonia sp. sun cup 0% 1 Cardamine angulata seaside bittercress 0% 1 Carex brainerdii Brainerd's sedge 0% 1 Carex fracta fragile-sheathed sedge 0% 1 Carex luzulina var. ablata woodrush sedge 0% 1 Carex praticola meadow sedge 0% 1 Carex serpenticola serpentine sedge 0% 1 Carex subbracteata smallbract sedge 0% 1 Castilleja ambigua ssp. humboldtiensis Humboldt Bay owl's clover 0% 1 Castilleja pruinosa frosted paintbrush 0% 1 Centaurea cyanus bachelor's button 0% 1 Centaurea solstitialis yellow starthistle 0% 1 Centaurea stoebe ssp. micranthos (old spotted knapweed 0% 1 Chimaphila sp. 0% 1 Cirsium brevistylum Indian thistle 0% 1 Cirsium occidentale var. candidissimum snowy thistle 0% 1 Cirsium occidentale var. venustum venus thistle 0% 1 Clarkia amoena ssp. huntiana farewell-to-spring 0% 1 Claytonia sp. 0% 1 Collinsia parviflora blue-eyed Mary 0% 1 Collinsia sparsiflora spinster’s blue-eyed Mary 0% 1 Collomia linearis narrow leaved collomia 0% 1 Coronopus didymus lesser wart-cress 0% 1 Cotula sp. brass buttons 0% 1 Crepis sp. hawksbeard 0% 1 Cypripedium californicum California lady's-slipper 0% 1 Cypripedium montanum mountain lady's slipper 0% 1 Cystopteris fragilis fragile fern 0% 1 Delphinium decorum ssp. tracyi coastal larkspur 0% 1 Dulichium arundinaceum three-way sedge 0% 1 Eleocharis macrostachya creeping spike-rush 0% 1 Elymus trachycaulus slender wheatgrass 0% 1 Epipactis sp. Stream orchid 0% 1 Erigeron aliceae Alice's fleabane 0% 1 Erigeron maniopotamicus Mad River fleabane daisy 0% 1 Erythronium citrinum var. citrinum lemon-colored fawn lily

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 0% 1 Fritillaria sp. Fritillaria 0% 1 Gayophytum diffusum var. parviflorum spreading groundsmoke 0% 1 Hackelia sp. stickseed 0% 1 Helenium bigelovii Bigelow’s sneezeweed 0% 1 Helianthemum scoparium dwarf rock-rose 0% 1 Hemizonia congesta ssp. luzulaefolia Hayfield tarweed 0% 1 Hierochloe odorata vanilla-grass 0% 1 Hordeum pusillum little barley 0% 1 Hydrophyllum occidentale western waterleaf 0% 1 Iliamna latibracteata California globe mallow 0% 1 Iris bracteata Siskiyou iris 0% 1 Iris tenax ssp. klamathensis Oregon iris 0% 1 Isatis tinctoria woad 0% 1 Juncus lesueurii 0% 1 Keckiella corymbosa redwood keckiella 0% 1 Kelloggia galioides milk kelloggia 0% 1 Lathyrus brownii brush pea 0% 1 Lathyrus cicera pea 0% 1 Lathyrus nevadensis var. nevadensis 0% 1 Lathyrus palustris marsh pea 0% 1 Lewisia nevadensis nevada lewisia 0% 1 Lewisia pygmaea pygmy bitterroot 0% 1 Ligusticum californicum California licorice root 0% 1 Limnanthes striata foothill meadowfoam 0% 1 Linanthus parviflorus small-flowered linanthus 0% 1 Linaria genistifolia ssp. dalmatica Dalmation toadflax 0% 1 Lomatium martindalei Coast Range lomatium 0% 1 Lotus crassifoloius big deervetch 0% 1 Lotus grandiflorus large-flowered lotus 0% 1 Lupinus latifolius var. viridifolius broad leaved lupine 0% 1 Luzula divaricata forked wood rush 0% 1 Madia exigua small tarweed or threadstem madia 0% 1 Medicago arabica spotted bur clover 0% 1 Minuartia douglasii Douglas' sandwort 0% 1 Monardella odoratissima ssp. pallida pallid mountain monardella 0% 1 Monardella sheltonii Shelton's coyote mint 0% 1 Monardella villosa ssp. villosa coyote mint 0% 1 Montia chamissoi toad lily 0% 1 Narthecium californicum bog asphodel 0% 1 Nemophila heterophylla variable leaf nemophila 0% 1 Oxalis albicans hairywood sorrel 0% 1 Oxalis corniculata yellow or creeping wood-sorrel 0% 1 Parentucellia sp. 0% 1 Penstemon laetus var. sagittatus mountain penstemon

Entire Database Records since 2001: Vascular Plant Species List Scientific Name Common Name %Occ. Count HERBACEOUS 0% 1 Penstemon newberryi 0% 1 Piperia candida (unconfirmed) white-flowered piperia 0% 1 Piperia unalascensis Alaska rein orchid 0% 1 Plagiobothrys undulatus coast popcorn flower 0% 1 Platanthera leucostachys white bog orchid 0% 1 Plectritis congesta sea blush 0% 1 Poa palustris fowl bluegrass 0% 1 Polygonum arenastrum common knotweed 0% 1 Potamogeton amplifolius broad-leaved pondweed 0% 1 Pterospora andromedea pine drops 0% 1 Rumex obtusifolius bitter dock 0% 1 Sagina apetala dwarf pearlwort 0% 1 Sanguisorba minor ssp. muricata garden burnet 0% 1 Sanicula tuberosa turkey pea 0% 1 Saxifraga marshallii Marshall's saxifrage 0% 1 Schoenoplectus subterminalis water bulrush 0% 1 Scirpus congdonii Congdon's bulrush 0% 1 Sedum laxum stone crop 0% 1 Sedum radiatum star-fruited stonecrop 0% 1 Sedum spathulifolium Pacific sedum 0% 1 Sisyrinchium californicum golden-eyed grass 0% 1 Stachys ajugoides var. rigida rigid hedge nettle 0% 1 Taeniatherum caput medusea medusah head 0% 1 Thalictrum fendleri var. fendleri meadow rue 0% 1 Thermopsis californica California false lupine 0% 1 Torreyochloa sp. 0% 1 Trifolium variegatum white-tipped clover 0% 1 Tropaeolum majus Nasturtium 0% 1 Turritis glabra tower mustard 0% 1 Veratrum insolitum Siskiyou false hellebore 0% 1 Vicia cracca cow vetch 0% 1 Viola hallii Hall's violet 0% 1 Viola macloskeyi small white violet 0% 1 Wyethia angustifolia narrow-leaf mule ear

Analysis of Howell’s Montia Occupancy and Density

Analysis Report May 6, 2014

Prepared for: Green Diamond Resource Company

900 Riverside Road, Korbel, CA 95519

Prepared by: Leigh Ann Starcevich and Trent McDonald Western EcoSystems Technology, Inc.

415 West 17th Street, Suite 200

Cheyenne, Wyoming 82001

Executive summary

MOHO Occupancy  No significant trend in MOHO occupancy was detected for 2011 – 2013 with a binomial generalized linear model (p = 0.2400).  The mean cover of competing vegetation is found to have a significant negative effect on MOHO occupancy (p < 0.0001).  The year-to-year variation was estimated as 0 either because this variance component is truly small or because the three years of pilot data was insufficient to estimate this component.  Power to detect a 1% annual trend was found to attain the 0.80 level for an annual sample of a sample 50 sites within 12 years and within 10 years for a 4% trend.  Power approximations for detection of trends in occupancy assume no substantial year-to- year variation in the segment-level occupancy rate and may be optimistic if the year-to-year variation is not negligible.

MOHO Density  No significant trend in MOHO density is detected for the monitoring period ranging from 2011 to 2013 (p = 0.7575).  The proportion of the quadrats within a segment that are occupied is a significant predictor of MOHO density (p < 0.0001).  Power to detect a 1% annual trend is uniformly low and does not attain 0.80 power for any of the simulation scenarios.  Power to detect a 4% annual trend is higher but still requires at least 90 occupied sites per year for up to 20 years to detect this large trend.  There were 36 segments in 2011, 28 segments in 2012, and 34 segments in 2013 that were occupied and replicated during the 2011-2013 monitoring period. Substantially more effort would be required to achieve adequate power to detect trends in MOHO density.

MOHO Sampling design  The power analysis results are a function of the existing sampling design. Changes to the sampling design will also impact the power to detect trends.  The evaluation of segment-level occupancy and the assumption of negligible false-positive and false-negative error rates are dependent on the evaluation of occupancy at the 25 quadrats within a segment.

Contents

Executive summary ...... i MOHO Occupancy ...... i MOHO Density ...... i MOHO Sampling design ...... i INTRODUCTION ...... 3 Table 1: Vegetation and road substrate factor definitions ...... 3 ANALYSIS OF MOHO OCCUPANCY ...... 4 Figure 1: Distribution of potential covariates for unoccupied (“FALSE”) and occupied (“TRUE”) road segments...... 5 Table 2: False-negative and false-positive detection rates by year ...... 5 Trend analysis ...... 6 Figure 2: Boxplots of estimated occupancy rates by year ...... 7 Power analysis ...... 7 Table 3: Annual and net trends over time ...... 8 Figure 3: Power to detect a 1% annual trend for a range of annual sample sizes ...... 9 Figure 4: Power to detect a 4% annual trend for a range of annual sample sizes ...... 10 ANALYSIS OF MOHO DENSITY IN OCCUPIED SITES ...... 11 Figure 5: Plots of segment-level MOHO density against potential covariates for occupied road segments ...... 11 Trend analysis ...... 12 Power analysis ...... 13 Figure 6: Power to detect a 1% annual trend over a 20-year monitoring period for 6 different annual samples of occupied sites ...... 14 Figure 7: Power to detect a 4% annual trend over a 20-year monitoring period for 6 different annual samples of occupied sites ...... 15 DISCUSSION AND CONCLUSIONS ...... 16 REFERENCES ...... 17 APPENDIX: Occupancy power simulation approach ...... 18

INTRODUCTION Green Diamond Resource Company (GDR) conducts annual surveys of Howell’s Montia (MOHO) to determine if occupancy and plant density (number of plants per square foot) parameters are changing over time. Surveys conducted in 2011 to 2013 are used to assess the power to detect trends in MOHO occupancy rates and MOHO density and to determine what covariates might be associated with each parameter of interest.

The sampling design incorporates a two-stage sample, with 50’ road segments selected at the first stage with GRTS sampling (Stevens and Olsen 2003, 2004). At the second stage, each first-stage segment is divided into 25 quadrats and examined for MOHO presence/absence. The second stage sample is stratified by quadrat-level occupancy status. A sample of up to two occupied quadrats is randomly selected and one unoccupied quadrat is randomly selected. If two occupied quadrats cannot be identified, then only one is selected for the second stage if the segment is occupied. Within the second stage quadrats, MOHO plants are counted and covariate information is collected within 1-square-foot cells within each quadrat of the road segment. Variable road width results in an average of 32 1-square foot cells per quadrat. Covariates include an indicator of road running surface vs. pullout, an indicator of whether or not the cell falls at the road edge, and factors indicating the density of competing vegetation and road substrate (Table 1). Note that summaries of road edge result in a measurement of the proportion of road versus edge resulting in a smaller proportion of edge for wide roads than for narrow roads.

Table 1: Vegetation and road substrate factor definitions Substrate Substrate definition Percent Percent vegetation class vegetation class definition class 1 Rock: 95-100% rock and 0 0-12% 0-5% dirt 2 Mostly rock with some 1 13-37% fines: 61-94% rock and 6- 39% dirt 3 Half rock and half fines: 2 38-62% 40-60% rock and 40-60% dirt 4 Mostly fines with some 3 63-87% rock: 6-39% rock and 61- 94% dirt 5 All fines: 0-5% rock and 4 88-100% 95-100% dirt

The goals of this analysis are to assess the power to detect trends in MOHO occupancy rate and MOHO density, to identify covariates associated with these parameters, and to evaluate the inferential impact of a reduction in annual survey effort. MOHO data collected in 2011 to 2013 are used to obtain suitable models for trends in MOHO occupancy rate and MOHO density. Estimates of fixed and random effects are used to inform Monte Carlo simulations to approximate power. The results of the power analyses for detecting trends in the MOHO occupancy rate and density are provided in the subsequent sections.

ANALYSIS OF MOHO OCCUPANCY Occupancy is measured at the quadrat level within each segment. However, to detect trends in segment-level occupancy, the quadrat-level information is distilled to presence/absence at the segment level. Therefore, if any quadrat within a segment is occupied, the segment-level occupancy indicator is 1 and 0 if all quadrats are unoccupied within a segment. Similarly, covariates information is summarized at the segment level for trend modeling. Recall that the second stage of the sampling design selects quadrats stratified by occupancy status. The mean of the cell-level vegetative cover class measurements, the mode of the cell-level road substrate factor, and the proportions of the cell-level road edge and road (versus pullout) indicators are calculated for each quadrat. Within each occupancy stratum of quadrats (occupied quadrats versus unoccupied quadrats), the quadrat-level estimates of the covariates are inflated by their second-stage design weights to obtain mean stratum-level covariate metrics (note that the substrate metric is the mean of a mode). The second-stage design weights are calculated as the total number of quadrats within each occupancy stratum divided by the number of quadrats in the stratum selected for cell-level surveys. Segment-level covariate metrics are calculated as weighted means of within-segment stratum-level covariate metrics weighted by the relative proportion of occupied and unoccupied quadrats (Figure 1). Therefore, covariate information from occupied quadrats is only extrapolated to occupied quadrats, and covariate information from the unoccupied quadrat is only extrapolated to unoccupied quadrats. Note that only one quadrat is surveyed within the unoccupied stratum, so variance estimates are inestimable for this stratum. However, this approach provides a design-based method for obtaining segment-level covariate metrics for occupancy modeling. Therefore, the measurements in the coral boxplots in Figure 1 representing the unoccupied segments are based on only one quadrat per segment.

Each second-stage quadrat is surveyed by two observers for accuracy assessment. However, false- negative and false-positive observations were encountered, particularly in intensive second-stage surveys. Comparisons of observer assessments to final occupancy status are made for the second-stage quadrats to assess the false-positive and false-negative rates of the combined observer assessments of occupancy (Table 2). The false-negative rate is consistently below 1%, so occupancy estimation is conducted assuming that a negligible proportion of plants are missed. In up to 1.33% of the quadrat surveys, a MOHO plant is identified that is not found in subsequent surveys. The false-positive rate is a consequence of surveying a rare and sparsely-distributed species but considered negligible for the analysis of trend.

Figure 1: Distribution of potential covariates for unoccupied (“FALSE”) and occupied (“TRUE”) road segments

Table 2: False-negative and false-positive detection rates by year Year False-negative False-positive detection rate detection rate 2011 0.44% 1.33% 2012 0.00% 0.52% 2013 0.95% 0.48%

Trend analysis Trends are more accurately estimated from a sample of sites exhibiting replication over time, so the subset of 110 annually-sampled 50-foot segments is used to model trend in occupancy. Trend is assessed with a binomial generalized linear mixed model. A mixed model is used rather than standard logistic regression to account for the temporal correlation of occupancy metrics measured at a given segment over time. AIC is used as the model selection criterion for the potential covariates of segment- level substrate mean, vegetative cover mean, the mean proportion of edge, and mean proportion of road (versus pullout) across all second-stage quadrats in each segment. Correlation analysis revealed that the proportion of road is highly correlated (ρ > 0.6) with both the year covariate and the proportion of edge. The year covariate exhibits only moderate correlation with the proportion of edge, so those two covariates are retained for model selection and the proportion of road is omitted from the trend analysis. The final occupancy trend model is given below. Note that four segments were omitted due to insufficient covariate data (NAs) for the occupancy model. No significant trend in occupancy is found over the 2011-2013 monitoring period (p = 0.2400). The mean cover of competing vegetation is found to have a significant negative effect on MOHO occupancy (p < 0.0001). While the mean substrate class is found to be an important predictor of segment-level occupancy when selecting models with AIC, the effect does not indicate statistical significance (p = 0.6300). The distribution of occupancy estimates by year is shown with boxplots in Figure 2.

Generalized linear mixed model fit by maximum likelihood ['glmerMod'] Family: binomial ( logit ) Formula: PA ~ WYear + SubstrateMean + VegMean + (1 + WYear | SID) Data: MOHO_Segment.Annual

Random effects: Groups Name Variance Std.Dev. Corr SID (Intercept) 14.6551 3.828 WYear 0.6724 0.820 -1.00 Number of obs: 326, groups: SID, 110

Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.1660 1.2410 0.134 0.894 WYear 0.2789 0.2373 1.175 0.240 SubstrateMean -0.1601 0.3327 -0.481 0.630 VegMean -1.1260 0.2780 -4.050 5.11e-05 ***

Figure 2: Boxplots of estimated occupancy rates by year

The final model includes variance components for variation among segments and variation among segment-level trend slopes. Year-to-year variation was estimated as 0, which means that either the variance in segment-level occupancy over time is negligible or could not be estimated with only three years of monitoring data. The latter is more likely, and subsequent monitoring may provide the basis for estimation of this variance component. Year-to-year variation greatly affects the power to detect trends, so the inability to estimate this variance component may result in over-optimistic approximations of the power to detect trends in segment-level occupancy.

Power analysis The power to detect a two-sided test of trend is approximated with Monte Carlo simulations. For one of 6 possible annual sample sizes (50, 70, 90, 110, 130, and 150), fixed and random effects estimates obtained from the segment-level occupancy trend model are used to simulate the 1000 realizations of population of interest for a known trend level (see the Appendix for more detail on the population simulation approach). Annual declines of 1% and 4% in the mean occupancy rate are simulated for each of the proposed sample sizes, and tests of trend are obtained from each application of the binomial generalized linear mixed model for the 1000 iterations. Trend is evaluated using a Wald F-test with

7 denominator degrees of freedom equal to the number of years in the monitoring period minus 2 for the balanced data set (Piepho and Ogutu 2002), and the p-value is compared to a Type I error rate of 0.05. Power is approximated as the proportion of times that the null hypothesis is correctly rejected in favor of trend detection. Note that the power analysis does not include existing data for 2011-2013 in the simulation data sets, so the analysis of power is purely prospective.

Test size is the observed false positive rate or the actual proportion of times that the null hypothesis is erroneously rejected. Trend testing should be conducted with a test that maintains a nominal test size, i.e. achieves the Type I error rate of 0.05. The test size can be evaluated with the Monte Carlo power simulation by generating populations with no trend and examining the proportion of times when the true null hypothesis of no trend is rejected. The z-test provided in the generalized linear mixed model output in R is not used as the trend test because simulations indicated that testing was conducted at higher-than-nominal levels. Simulations of populations exhibiting no trend yielded test size approximations of 0.08 – 0.15 for monitoring periods of at least 10 years and as high as 0.35 for a monitoring period of 4 years. The Wald F-test attained test sizes ranging from 0.05 to 0.09 for monitoring periods of at least 10 years. For monitoring periods of spanning fewer than 10 years, the test size for the Wald F-test ranges from 0.00 to 0.04. Therefore, the probability of detecting a false trend is lower than planned for small sample sizes, yielding conservative power for short-term trend detection.

Annual trends of 1% and 4% are simulated in this power analysis. A comparison of annual trend and net trend over time is provided in Table 3. For example, a 1% annual increase corresponds to a net increase of 10% over 10 years and a 22% mean increase after 20 years. A 4% annual trend is dramatically larger, resulting in an overall a net increase of 48% over 10 years and a 119% mean increase after 20 years. The results of the power analysis to detect annual trends of 1% and 4% are provided in Figures 3 and 4, respectively. The power plots indicate high power to detect trends for even the smallest sample size, with a 1% trend detected with 0.80 power within 12 years and a 4% trend detected within 10 years for an annual sample of the same 50 sites. Recall that these power approximations assume no substantial year-to-year variation in the segment-level occupancy rate. Even relatively low year-to-year variation may greatly impact the power to detect trend.

Table 3: Annual and net trends over time Years Annual trend Net trend 5 1% 5% 10 1% 10% 15 1% 16% 20 1% 22% 5 4% 22% 10 4% 48% 15 4% 80% 20 4% 119%

Figure 3: Power to detect a 1% annual trend for a range of annual sample sizes

Figure 4: Power to detect a 4% annual trend for a range of annual sample sizes

ANALYSIS OF MOHO DENSITY IN OCCUPIED SITES MOHO density is measured at the quadrat level for occupied segments. Covariates summarized at the segment level for the occupancy analysis are also use for MOHO density trend modeling. An additional covariate, Occ, represents the proportion of each segment evaluated as occupied among the 25 quadrats within a segment. Plots of MOHO density against each of the potential covariates are provided in Figure 5.

Figure 5: Plots of segment-level MOHO density against potential covariates for occupied road segments

Trend analysis The trend analysis for MOHO density was conducted for the subset of sites visited annually that were occupied at least two of the three monitoring years. Replication of sites provides the basis to accurately estimate the standard error of the trend coefficient, and sites that were unoccupied for more than one year would not have sufficient replication for random effects estimation. The requirements of both occupancy and replication result in a data set with 36 segments in 2011, 28 segments in 2012, and 34 segments in 2013. Note that the data are “unbalanced” in that not all sites are represented in all years. Model selection is conducted using AIC and omits the road proportion covariate which is highly correlated with both time and the proportion of road edge. The final model of trend in MOHO density in occupied segments is provided below. Hypothesis testing is conducted with the Wald F-test with Satterthwaite degrees of freedom for the unbalanced data set (Piepho and Ogutu 2002). No significant trend in MOHO density is detected for the monitoring period ranging from 2011 to 2013 (Wald F-test statistic = 0.1794, df = 1 and 0.81, p = 0.7575). Note that the mean of cover for competing vegetation is retained by AIC as a meaningful predictor but this effect is not significantly different from zero (Wald F- test statistic = 0.0939, df = 1 and 68.75, p = 0.7602).

The proportion of the segment that is occupied (Occ) is found to be significantly different from zero (Wald F-test statistic = 218.77, df = 1 and 57.60, p < 0.0001). The Occ variable is calculated from the occupancy evaluation of each of the 25 quadrats within each segment. This covariate may also exhibit trend as MOHO density changes over time, and including this predictor may obscure some of the trend detected in MOHO density. However, in this exercise, omitting the Occ term does not change the inference on MOHO density (Wald F-test statistic = 0.40, df = 1 and 1.50, p = 0.6106). Including the Occ term does help to explain much of the variation among occupied segments, reducing the estimate segment-to-segment variance from 3.6051 to 0.2140 and the estimate of variance among segment-level slopes from 0.2894 to 0.0126. Therefore, this variable is included in the trend model for the power analysis.

Linear mixed model fit by REML ['lmerMod'] Formula: log(MOHODensity) ~ WYear + Occ + VegMean + (1+WYear|SID) + (1|Year) Data: MOHO_Segment.revisit REML criterion at convergence: 243.5056 Random effects: Groups Name Variance Std.Dev. Corr SID (Intercept) 0.21404 0.4626 WYear 0.01264 0.1124 1.00 Year (Intercept) 0.01513 0.1230 Residual 0.46932 0.6851 Number of obs: 96, groups: SID, 37; Year, 3

Fixed effects: Estimate Std. Error t value (Intercept) -5.80113 0.23685 -24.493 WYear -0.05291 0.12411 -0.426 Occ 4.67776 0.29867 15.662 VegMean 0.04960 0.13777 0.360

Power analysis A two-sided test of MOHO density trend serves as the basis for the assessment of power with Monte Carlo simulations. For one of 6 possible annual sample sizes of occupied sites (50, 70, 90, 110, 130, and 150), fixed and random effects estimates obtained from the MOHO density trend model are used to simulate the population of interest for annual mean increases of 1% and 4%. For each simulation scenario, defined as a sample size and trend rate combination, 1000 realizations of the population are generated and tests of trend are obtained from the results of the linear mixed model of the logarithmically-transformed MOHO density estimate. For each of the 1000 iterations, trend is evaluated using a Wald F-test with Satterthwaite degrees of freedom. The p-value is compared to a Type I error rate of 0.05 and power is approximated as the proportion of times that the null hypothesis is correctly rejected in favor of trend detection. Note that the power analysis does not include existing data for 2011-2013 in the simulation data sets, so the analysis of power is purely prospective.

The results of the power simulations to detect trends of 1% and 4% in MOHO density are plotted in Figures 6 and 7, respectively. Power to detect a 1% annual trend is uniformly low and does not attain 0.80 power for any of the simulation scenarios. Power to detect a 4% annual trend is higher but still requires at least 90 occupied sites per year for up to 20 years to detect this large trend. Note that these sample sizes of occupied sites are higher than the samples of occupied and replicated sites obtained during the 2011-2013 monitoring period (36 segments in 2011, 28 segments in 2012, and 34 segments in 2013). Therefore, substantially more effort would be required to achieve adequate power to detect trends in MOHO density.

Figure 6: Power to detect a 1% annual trend over a 20-year monitoring period for 6 different annual samples of occupied sites

Figure 7: Power to detect a 4% annual trend over a 20-year monitoring period for 6 different annual samples of occupied sites

DISCUSSION AND CONCLUSIONS The power to detect 1%- and 4%-annual trends in the occupancy rate of MOHO demonstrated acceptable levels within 10 to 12 years of monitoring. However, note that the year-to-year variation was estimated as 0. The power to detect trends at the sample sizes considered may be lower if the year-to-year variation is substantial. MOHO occupancy was found to be negatively impacted by increases in competing vegetation and possibly by increasingly fine substrate, although the test of this effect was not statistically significant.

The power to detect a 1% annual trend in MOHO density in occupied sites was found to be consistently low and did not attain 0.80 power for any of the investigated scenarios. A 4% annual trend could be detected with in at least 13 years for annual visits to a sample of 150 occupied sites or within 20 years for annual visits to a sample of 90 sites. However, the sample sizes of occupied sites explored in the power analysis exceed any annual sample of occupied sites observed in the monitoring period 2011- 2013. Therefore, substantially more effort in terms of segments and monitoring years would be needed to detect a trend in MOHO density.

Abundance and density tend to be more expensive metrics to estimate and monitor, and occupancy can provide an efficient proxy when a resource is too patchy or rare to monitor (MacKenzie et al 2006). The extensive effort required to detect trends in MOHO density may prove to be cost-prohibitive given the patchiness of its distribution and the large sample sizes required for detecting even large trends. The results provided in this power analysis reflect the sampling design used in the 2011-2013 monitoring period. Changes in the design will impact the power to detect trends in both occupancy and density and should be carefully considered. The evaluation of segment-level occupancy and the assumption of negligible false-positive and false-negative error rates are dependent on the evaluation of occupancy at the 25 quadrats within a segment.

REFERENCES

MacKenzie, D.I., J.D. Nichols, J.A. Royle, K.H. Pollack, L.L. Bailey, and J.E. Hines. 2006. Occupancy Estimation and Modeling. Academic Press: San Francisco.

Piepho, H.P. and J.O. Ogutu. 2002. A Simple Mixed Model for Trend Analysis in Wildlife Populations. Journal of Agricultural, Biological, and Environmental Statistics 7(3): 350–360.

Stevens, D.L. Jr and A.R. Olsen. 2003. Variance estimation for spatially balanced samples of environmental resources. Environmetrics 14: 593–610.

Stevens, D.L. Jr and A.R. Olsen. 2004. Spatially Balanced Sampling of Natural Resources. Journal of the American Statistical Association 99(465): 262-278.

APPENDIX: Occupancy power simulation approach

Let logit  XZ    , where η is the linear predictor to the logit of the probability of a success (i.e. the outcome of interest Y = 1), π is the probability of a success, X is the fixed-effects design matrix, β is the vector of fixed effects, Z is the random-effects design matrix, and γ is the vector of random effects. Assume that the model includes a single fixed effect of year with no interaction term so that the fxed effects can be partitioned into the year term and slope coefficient and a component that does not * include the year term and slope coefficient. In other words, let Xijβ = X ijβ + wjβ1, where Xij is the design th th * vector for the i site in the j year, X ij is the design vector excluding the year term for trend, wj is the year term for trend, and β1 is the trend coefficient.

Assume now that a multiplicative trend of (100)*p% is simulated for the mean of the binomial outcome, Y. Therefore, we have that:

E Y X, , Z, , w2  1  2 p . E Y X, , Z, , w1  0 1

Given the binomial GLMM, we then have that:

 expXZXZ 1    1  exp   1    expit XZij  1 1     ij 11   ij    pij  expitXZ 0   expXZXZ  1  exp     ij  1    ij  ij  **    exp1 1 exp XZXZij    exp  1 exp  1  1  exp ij         1 1 expXZXZ**     1  exp    exp    ij   ij   1   * Let a = exp Xij  Z  and b = exp 1  . Then ba1  p pb =  1ab 1  a 1  p p Therefore, exp11  and  log p  log 1  a 1  p . 11ap  Now the linear predictor is obtained as: X   Z   w   X*   Z   wlog p  log 1  a 1  p XZ*, ij ij ij j1 ij ij j      ij ij and the mean of the binary outcome, Y , is:

expij  ijexpit ij  . 1 expij 

* The X ij  is obtained from the model estimates from the pilot data with median 2012 values used for * the X ij . The simulated outcome of interest takes the value 1 when a random uniform on the interval

(0,1) exceeds the mean of the outcome, πij.