Perennial Pepperweed Mapping

Perennial Pepperweed Mapping

Noxious Weeds Monitoring 2025-2026 LORP Workplan

Published

September 18, 2025

Modified

September 18, 2025

Summary

This data report covers the monitoring of Lepidium latifolium (perennial pepperweed) in the Lower Owens River Project (LORP) area from 2018 to 2025. The data is sourced from the ArcGIS Online Noxious Weeds 2025 feature service and provides updated spatial occurrence as of 2025.

“Perennial pepperweed (Lepidium latifolium), an introduced plant from southeastern Europe and Asia, is invasive throughout the western United States. It can establish in a wide range of environments and is a common problem in flood plains, irrigation structures, pastures, wetlands, riparian areas, roadsides, and residential site. Recent surveys identify perennial pepperweed as a weed problem in nearly all of California, and both the California Department of Food and Agriculture (CDFA) and California Invasive Plant Council (Cal-IPC) list it as a noxious weed of great ecological concern.” - UC IPM

California Invasive Plant Council (Cal-IPC) Rating: High
High – These species have severe ecological impacts on physical processes, plant and animal communities, and vegetation structure. Their reproductive biology and other attributes are conducive to moderate to high rates of dispersal and establishment. Most are widely distributed ecologically.

California Department of Food and Agriculture (CDFA) Rating: -*
* – included in the CCR Section 4500 list of California State Noxious Weeds.

LORP Area Filter Applied:
- Northernmost 2025 point latitude: 36.9747 °N
- Filtered dataset now contains 1647 observations

Data Overview

The dataset includes 1647 total observations across 8 years, with peak survey activity in 2025 (426 records).

Key Findings Summary

Total Observations: 1647 records across all years (2018-2025)
Peak Survey Year: 2025 with 426 observations
Recent Activity: 852 observations in 2022-2025
Historical Baseline: 795 observations in 2018-2021
Data Coverage: 100% of records have complete date information (using observation date with creation date fallback)

Interactive Map Comparison

The following interactive map shows pepperweed populations across all years (2018-2025). Use the layer controls in the top-right corner to toggle between different years. The map includes all 1647 documented observations, with feature creation dates used when observation dates are unavailable.

Data Downloads

📊 Download GeoJSON 🌍 Download KML

Complete dataset with all 1647 pepperweed observations

Study Area Boundaries

North: 36.9747°N
South: 36.5478°N
East: -117.981°W
West: -118.2343°W
Center: 36.7612°N, -118.1077°W

Re-reading with feature count reset from 1 to 0

2025 Distribution - plants per site estimated from field observations

The following histogram shows the distribution of abundance values for all pepperweed point locations documented in 2025:

### 2025 Site Distribution
- **Total Sites**: 426 
- **Small Sites (≤ 100 plants)**: 325 sites ( 76.3 %)
- **Large Sites (> 100 plants)**: 99 sites ( 23.2 %)

### Detailed Abundance Distribution
- **1 plants**: 57 sites
- **2 plants**: 135 sites
- **4 plants**: 133 sites
- **5 plants**: 99 sites

### Abundance Range
- **Minimum**: 5 plants
- **Maximum**: 200 plants

The interactive map above provides the primary visualization of pepperweed distribution patterns.

Recent Sites

Data Quality and Limitations

Data Sources

Primary Source: ArcGIS Online Noxious Weeds 2025 Feature Service
Last Updated: August 11, 2025
Coordinate System: WGS84 (EPSG:4326)

Limitations

Data represents documented observations only
Population estimates may vary based on survey timing and conditions
Some areas may be under-sampled due to access constraints
Abundance categories are based on field estimates

River Reach Analysis

Number of river reaches loaded: 6

Reach names: LORP Reach 1 LORP Reach 2 LORP Reach 3 LORP Reach 4 LORP Reach 5 LORP Reach 6

Sites before filtering: 1647

Sites after spatial join: 1647

Sites with reach assignments: 1647

### LORP Reach Distribution Summary
**Total Reaches Analyzed**: 6 
**Years Covered**: 2018 - 2025 
**Reaches with Data**: 6 

### Reach Activity Summary
- ** LORP Reach 2 **: 999 sites
- ** LORP Reach 3 **: 423 sites
- ** LORP Reach 1 **: 163 sites
- ** LORP Reach 4 **: 47 sites
- ** LORP Reach 5 **: 12 sites
- ** LORP Reach 6 **: 3 sites

Occupancy Analysis

### Overall Occupancy Analysis

**Resolution**: 1/10 mile segments (600 total segments across 60 miles)

#### Overall Occupancy by Year



Table: Number of 1/10 mile segments occupied by pepperweed

| Year | Segments Occupied | Total Segments | Occupancy % |
|:----:|:-----------------:|:--------------:|:-----------:|
| 2018 |        15         |      600       |     2.5     |
| 2019 |        74         |      600       |    12.3     |
| 2020 |        86         |      600       |    14.3     |
| 2021 |        21         |      600       |     3.5     |
| 2022 |        37         |      600       |     6.2     |
| 2023 |         1         |      600       |     0.2     |
| 2024 |        87         |      600       |    14.5     |
| 2025 |        145        |      600       |    24.2     |


#### Reach-Specific Occupancy



Table: Percentage of each reach occupied by pepperweed (by year)

|LORP Reach   | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 |
|:------------|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
|LORP Reach 1 | 23.7 | 52.6 | 39.5 | 2.6  |  NA  |  NA  | 10.5 | 55.3 |
|LORP Reach 2 | 3.1  | 17.6 | 23.9 | 5.0  | 15.7 |  NA  | 21.4 | 40.3 |
|LORP Reach 3 | 0.7  | 17.6 | 20.3 | 5.4  | 7.4  | 0.7  | 29.1 | 31.8 |
|LORP Reach 4 |  NA  |  NA  | 8.3  | 11.1 | 2.8  |  NA  | 19.4 | 11.1 |
|LORP Reach 5 |  NA  |  NA  |  NA  |  NA  |  NA  |  NA  | 2.9  | 17.6 |
|LORP Reach 6 |  NA  |  NA  |  NA  |  NA  |  NA  |  NA  |  NA  | 8.1  |


### Occupancy Summary Statistics

**Average Annual Occupancy**: 9.7 %

**Highest Occupancy Year**: 2025 ( 24.2 %)

**Lowest Occupancy Year**: 2023 ( 0.2 %)


**Most Occupied Reach**: LORP Reach 1 ( 30.7 % average)

**Least Occupied Reach**: LORP Reach 6 ( 8.1 % average)

River Mile Analysis

### Reach Boundaries (Northern Boundaries)

**Table showing minimum river mile for each reach (northern boundary):**



Table: Northern boundaries (minimum river miles) for each LORP reach

|LORP Reach   | Min River Mile | Max River Mile | Reach Number | Label Position |
|:------------|:--------------:|:--------------:|:------------:|:--------------:|
|LORP Reach 1 |      0.1       |      3.9       |      1       |      0.1       |
|LORP Reach 2 |      3.7       |      19.6      |      2       |      3.7       |
|LORP Reach 3 |      19.7      |      34.5      |      3       |      19.7      |
|LORP Reach 4 |      34.5      |      38.1      |      4       |      34.5      |
|LORP Reach 5 |      39.0      |      42.4      |      5       |      39.0      |
|LORP Reach 6 |      49.7      |      53.4      |      6       |      49.7      |


**Using actual reach boundaries from data:**

# A tibble: 6 × 5
  Name         Min_RiverMile Max_RiverMile Reach_Num Label_Position
  <chr>                <dbl>         <dbl>     <int>          <dbl>
1 LORP Reach 1           0.1           3.9         1            0.1
2 LORP Reach 2           3.7          19.6         2            3.7
3 LORP Reach 3          19.7          34.5         3           19.7
4 LORP Reach 4          34.5          38.1         4           34.5
5 LORP Reach 5          39            42.4         5           39  
6 LORP Reach 6          49.7          53.4         6           49.7


### Reach Labels and River Miles

**Table showing reach labels and their river mile positions:**



Table: Reach labels and their river mile positions

|Reach Label  | River Mile |
|:------------|:----------:|
|LORP Reach 1 |    0.1     |
|LORP Reach 2 |    3.7     |
|LORP Reach 3 |    19.7    |
|LORP Reach 4 |    34.5    |
|LORP Reach 5 |    39.0    |
|LORP Reach 6 |    49.7    |


### Reach Minimum River Miles

**Table showing minimum river mile for each reach:**



Table: Minimum river mile for each reach

|Reach        | Min River Mile |
|:------------|:--------------:|
|LORP Reach 1 |      0.1       |
|LORP Reach 2 |      3.7       |
|LORP Reach 3 |      19.7      |
|LORP Reach 4 |      34.5      |
|LORP Reach 5 |      39.0      |
|LORP Reach 6 |      49.7      |


### Manual Reach Boundaries for Plot
     Name Closest_Bin
1 Reach 1           0
2 Reach 2           4
3 Reach 3          20
4 Reach 4          35
5 Reach 5          39
6 Reach 6          50

### River Mile Distribution Summary
**Total River Miles Analyzed**: 61 
**Years Covered**: 2018 - 2025 
**River Mile Range**: 0 - 60 

### Top 10 Most Active River Miles
- **River Mile 10 **: 269 sites
- **River Mile 4 **: 196 sites
- **River Mile 7 **: 157 sites
- **River Mile 5 **: 99 sites
- **River Mile 6 **: 94 sites
- **River Mile 28 **: 83 sites
- **River Mile 3 **: 77 sites
- **River Mile 30 **: 64 sites
- **River Mile 34 **: 59 sites
- **River Mile 29 **: 51 sites

Field Documentation

Field Documentation Images

Pepperweed observations with field photos:

Feature ID: 1369 | Date: 2025-04-02 | Abundance: 200 plants

Feature ID: 1370 | Date: 2025-04-02 | Abundance: 200 plants

Feature ID: 1371 | Date: 2025-05-14 | Abundance: 200 plants

Notes: Dense vegetative cluster around low ditch

Feature ID: 1398 | Date: 2025-07-30 | Abundance: 200 plants

Notes: Among the tule next to the water, north of the willow

Feature ID: 1401 | Date: 2025-07-30 | Abundance: 200 plants

Notes: Among tule next to water, south of willow

Feature ID: 1465 | Date: 2025-07-30 | Abundance: 200 plants

Notes: W BAHY + SCAC

Feature ID: 1467 | Date: 2025-07-30 | Abundance: 200 plants

Notes: 10-15m south of rivers edge

Feature ID: 1473 | Date: 2025-07-30 | Abundance: 200 plants

Notes: 8-10m from waters edge; 12-15m long stretch of LELA bordering large TARA

Feature ID: 1475 | Date: 2025-07-30 | Abundance: 200 plants

Notes: 20m long stretch 2-5m from river, dense

Feature ID: 1482 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1485 | Date: 2025-07-31 | Abundance: 200 plants

Notes: W/ ASFA

Feature ID: 1490 | Date: 2025-07-31 | Abundance: 200 plants

Notes: W/ dead BAHY

Feature ID: 1495 | Date: 2025-07-31 | Abundance: 200 plants

Notes: W) ASFA ELTR TYPHA

Feature ID: 1501 | Date: 2025-07-31 | Abundance: 200 plants

Notes: W/ ASFA

Feature ID: 1506 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1507 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1508 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1510 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1515 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1516 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1527 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1529 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1535 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1538 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1545 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1547 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1563 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1583 | Date: 2025-08-04 | Abundance: 200 plants

Notes: Along bank, flowering

Feature ID: 1598 | Date: 2025-08-04 | Abundance: 200 plants

Notes: Several large patches around this point

Feature ID: 1650 | Date: 2025-08-04 | Abundance: 200 plants

Feature ID: 1367 | Date: 2025-04-01 | Abundance: 100 plants

Field documentation images provide visual confirmation of pepperweed sites and help verify site characteristics.

Conclusion

The 2018-2025 data reveals expanding pepperweed distribution within the LORP area. The interactive mapping tool provides land managers with a visualization tool for identifying and prioritizing treatment areas over time.

This report will be updated as new data are added to the live feature service. The data source is available at: ArcGIS REST Service

Data Disclaimer

The data presented in this report are collected for monitoring and management purposes by the Inyo County Water Department. While every effort is made to ensure accuracy, field conditions, survey timing, and observer experience may affect data quality. Users should verify critical information independently before making management decisions.

This analysis is automatically updated when new data is added to the ArcGIS feature service.

Citation

BibTeX citation:

@report{untitled,
  author = {},
  publisher = {Inyo County Water Department},
  title = {Perennial {Pepperweed} {Mapping}},
  date = {},
  url = {https://github.com/inyo-gov/noxious-weeds},
  langid = {en}
}

For attribution, please cite this work as:

“Perennial Pepperweed Mapping.” n.d. Data Report. Inyo County Water Department. https://github.com/inyo-gov/noxious-weeds.

--- title: "Perennial Pepperweed Mapping" subtitle: "Noxious Weeds Monitoring 2025-2026 LORP Workplan" tbl-cap-location: top affiliation: "Inyo County Water Department" date: today date-modified: today citation: type: report container-title: "Data Report" publisher: "Inyo County Water Department" issued: today url: https://github.com/inyo-gov/noxious-weeds google-scholar: true params: current_year: 2025 start_year: 2018 format: html: theme: cosmo toc: true toc-depth: 3 code-fold: true code-tools: true embed-resources: true css: styles.css docx: toc: true toc-depth: 3 number-sections: true reference-doc: custom-reference.docx execute: echo: false warning: false message: false prefer-html: true --- ```{r setup} #| message: false #| warning: false library(sf) library(dplyr) library(tidyr) library(leaflet) library(leaflet.extras2) library(lubridate) library(htmltools) library(knitr) library(DT) library(ggplot2) library(scales) library(httr) ``` # Summary This data report covers the monitoring of **Lepidium latifolium** (perennial pepperweed) in the Lower Owens River Project (LORP) area from `r params$start_year` to `r params$current_year`. The data is sourced from the [ArcGIS Online Noxious Weeds 2025 feature service](https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0) and provides updated spatial occurrence as of `r params$current_year`. > "Perennial pepperweed (Lepidium latifolium), an introduced plant from southeastern Europe and Asia, is invasive throughout the western United States. It can establish in a wide range of environments and is a common problem in flood plains, irrigation structures, pastures, wetlands, riparian areas, roadsides, and residential site. Recent surveys identify perennial pepperweed as a weed problem in nearly all of California, and both the California Department of Food and Agriculture (CDFA) and California Invasive Plant Council (Cal-IPC) list it as a noxious weed of great ecological concern." - [UC IPM](https://ipm.ucanr.edu/home-and-landscape/perennial-pepperweed/#gsc.tab=0) **California Invasive Plant Council (Cal-IPC) Rating: High** High – These species have severe ecological impacts on physical processes, plant and animal communities, and vegetation structure. Their reproductive biology and other attributes are conducive to moderate to high rates of dispersal and establishment. Most are widely distributed ecologically. **California Department of Food and Agriculture (CDFA) Rating: -*** * – included in the CCR Section 4500 list of California State Noxious Weeds. ```{r data-loading} #| message: false #| warning: false # ArcGIS Feature Service URL base_url <- "https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0/query?" # Query parameters for all data query_params <- paste0( "where=1=1", "&outFields=*", "&f=geojson", "&outSR=4326" ) # Construct full query URL query_url <- paste0(base_url, query_params) # Load data from ArcGIS feature service pepper_data <- st_read(query_url, quiet = TRUE) # Process dates - convert Unix timestamps to dates pepper_data <- pepper_data %>% mutate( Date_Observed = as.POSIXct(Date/1000, origin='1970-01-01'), Date_Created = as.POSIXct(CreationDate/1000, origin='1970-01-01'), Year_Observed = year(Date_Observed), Year_Created = year(Date_Created), # Use creation year as fallback when observation year is missing Year_Display = ifelse(is.na(Year_Observed), Year_Created, Year_Observed), Date_Display = as.POSIXct(ifelse(is.na(Date_Observed), as.numeric(Date_Created), as.numeric(Date_Observed)), origin='1970-01-01') ) # Filter to LORP area based on northernmost current year data point pepper_current_year_for_bounds <- pepper_data %>% filter(Year_Display == params$current_year) if(nrow(pepper_current_year_for_bounds) > 0) { # Get the northernmost latitude from current year data northernmost_lat <- max(st_coordinates(pepper_current_year_for_bounds)[,2]) # Filter all data to LORP area (south of northernmost current year point) pepper_data <- pepper_data %>% filter(st_coordinates(.)[,2] <= northernmost_lat) cat("LORP Area Filter Applied:\n") cat("- Northernmost", params$current_year, "point latitude:", round(northernmost_lat, 4), "°N\n") cat("- Filtered dataset now contains", nrow(pepper_data), "observations\n") } else { cat("No", params$current_year, "data found for LORP area filtering\n") } # Include all records (no year filtering) # Separate data by display year (observation year with creation year fallback) pepper_2018 <- pepper_data %>% filter(Year_Display == 2018) pepper_2019 <- pepper_data %>% filter(Year_Display == 2019) pepper_2020 <- pepper_data %>% filter(Year_Display == 2020) pepper_2021 <- pepper_data %>% filter(Year_Display == 2021) pepper_2022 <- pepper_data %>% filter(Year_Display == 2022) pepper_2023 <- pepper_data %>% filter(Year_Display == 2023) pepper_2024 <- pepper_data %>% filter(Year_Display == 2024) pepper_2025 <- pepper_data %>% filter(Year_Display == 2025) # Create recent vs historical comparison pepper_recent <- pepper_data %>% filter(Year_Display >= 2022) pepper_historical <- pepper_data %>% filter(Year_Display >= 2018 & Year_Display <= 2021) # Summary statistics for all years (using display year with fallback) summary_stats <- pepper_data %>% filter(!is.na(Year_Display)) %>% group_by(Year_Display) %>% summarise( Total_Observations = n(), Avg_Abundance = round(mean(Abundance, na.rm = TRUE), 1), Avg_Height = round(mean(Height, na.rm = TRUE), 1), Max_Abundance = max(Abundance, na.rm = TRUE), Records_With_Obs_Date = sum(!is.na(Year_Observed)), Records_With_Creation_Date_Only = sum(is.na(Year_Observed) & !is.na(Year_Created)), .groups = 'drop' ) %>% arrange(Year_Display) ``` ## Data Overview The dataset includes `r nrow(pepper_data)` total observations across `r length(unique(pepper_data$Year_Display[!is.na(pepper_data$Year_Display)]))` years, with peak survey activity in `r names(sort(table(pepper_data$Year_Display, useNA='ifany'), decreasing=TRUE)[1])` (`r max(table(pepper_data$Year_Display, useNA='ifany'))` records). ## Key Findings Summary - **Total Observations**: `r nrow(pepper_data)` records across all years (2018-2025) - **Peak Survey Year**: `r names(sort(table(pepper_data$Year_Display, useNA='ifany'), decreasing=TRUE)[1])` with `r max(table(pepper_data$Year_Display, useNA='ifany'))` observations - **Recent Activity**: `r nrow(pepper_recent)` observations in 2022-2025 - **Historical Baseline**: `r nrow(pepper_historical)` observations in 2018-2021 - **Data Coverage**: `r round((nrow(pepper_data) - sum(is.na(pepper_data$Year_Display))) / nrow(pepper_data) * 100, 1)`% of records have complete date information (using observation date with creation date fallback) # Interactive Map Comparison The following interactive map shows pepperweed populations across all years (`r params$start_year`-`r params$current_year`). Use the layer controls in the top-right corner to toggle between different years. The map includes all `r nrow(pepper_data)` documented observations, with feature creation dates used when observation dates are unavailable. <div class="download-section"> <h3>Data Downloads</h3> <div class="download-links"> <a href="pepperweed_data.geojson">📊 Download GeoJSON</a> <a href="pepperweed_data.kml">🌍 Download KML</a> </div> Complete dataset with all `r nrow(pepper_data)` pepperweed observations </div> ```{r generate-geojson} #| message: false #| warning: false #| echo: false #| results: hide # Generate GeoJSON file for download st_write(pepper_data, "pepperweed_data.geojson", driver = "GeoJSON", delete_dsn = TRUE, quiet = TRUE) # Generate KML file for Google Earth (will be zipped to KMZ) st_write(pepper_data, "pepperweed_data.kml", driver = "KML", delete_dsn = TRUE, quiet = TRUE) # Calculate spatial extent bbox <- st_bbox(pepper_data) extent_info <- list( north = bbox$ymax, south = bbox$ymin, east = bbox$xmax, west = bbox$xmin, center_lat = (bbox$ymax + bbox$ymin) / 2, center_lon = (bbox$xmax + bbox$xmin) / 2 ) ``` <div class="spatial-extent"> <h4>Study Area Boundaries</h4> <ul> <li>North: `r round(extent_info$north, 4)`°N</li> <li>South: `r round(extent_info$south, 4)`°N</li> <li>East: `r round(extent_info$east, 4)`°W</li> <li>West: `r round(extent_info$west, 4)`°W</li> <li>Center: `r round(extent_info$center_lat, 4)`°N, `r round(extent_info$center_lon, 4)`°W</li> </ul> </div> ```{r interactive-map} #| message: false #| warning: false # Add reach information to pepper_data if available if(exists("pepper_data_with_reaches")) { # Merge reach information back to main dataset pepper_data <- pepper_data %>% left_join( pepper_data_with_reaches %>% st_drop_geometry() %>% select(OBJECTID, Name), by = "OBJECTID" ) } # Load water quality stations as reference points wq_url <- "https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/lorp_wq_view/FeatureServer/0/query?where=1=1&outFields=*&f=geojson&outSR=4326" wq_stations <- st_read(wq_url, quiet = TRUE) # Load river reaches for labeling (if not already loaded) if(!exists("river_reaches")) { reach_url <- "https://inyocounty.maps.arcgis.com/sharing/rest/content/items/90e5870bd5914a928bd97b023f07b807/data" river_reaches <- st_read(reach_url, quiet = TRUE) river_reaches <- st_transform(river_reaches, st_crs(pepper_data)) } # Get reach centroids for labeling - only LORP reaches 1-6 reach_centroids <- river_reaches %>% filter(Name %in% c("LORP Reach 1", "LORP Reach 2", "LORP Reach 3", "LORP Reach 4", "LORP Reach 5", "LORP Reach 6")) %>% st_centroid() %>% mutate(Reach_Label = gsub("LORP Reach ", "R", Name)) # Create comprehensive map with all years combined_map <- leaflet(pepper_data) %>% # Standard basemaps addProviderTiles(providers$Esri.WorldImagery, group = "World Imagery") %>% addProviderTiles(providers$OpenStreetMap, group = "OpenStreetMap") %>% # Add 2018 markers addCircleMarkers( data = pepper_2018, radius = 5, color = "black", fillColor = "black", fillOpacity = 0.7, weight = 2, popup = ~paste0( "2018 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", ifelse(exists("Name") && !is.na(Name), paste0("LORP Reach: ", Name, " "), ""), "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2018 Data" ) %>% # Add 2019 markers addCircleMarkers( data = pepper_2019, radius = 5, color = "darkred", fillColor = "darkred", fillOpacity = 0.7, weight = 2, popup = ~paste0( "2019 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2019 Data" ) %>% # Add 2020 markers addCircleMarkers( data = pepper_2020, radius = 5, color = "red", fillColor = "red", fillOpacity = 0.7, weight = 2, popup = ~paste0( "2020 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2020 Data" ) %>% # Add 2021 markers addCircleMarkers( data = pepper_2021, radius = 5, color = "yellow", fillColor = "yellow", fillOpacity = 0.7, weight = 2, popup = ~paste0( "2021 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2021 Data" ) %>% # Add 2022 markers addCircleMarkers( data = pepper_2022, radius = 5, color = "orange", fillColor = "orange", fillOpacity = 0.7, weight = 2, popup = ~paste0( "2022 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2022 Data" ) %>% # Add 2023 markers addCircleMarkers( data = pepper_2023, radius = 5, color = "green", fillColor = "green", fillOpacity = 0.7, weight = 2, popup = ~paste0( "2023 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2023 Data" ) %>% # Add 2024 markers addCircleMarkers( data = pepper_2024, radius = 5, color = "blue", fillColor = "blue", fillOpacity = 0.8, weight = 3, popup = ~paste0( "2024 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2024 Data" ) %>% # Add 2025 markers addCircleMarkers( data = pepper_2025, radius = 5, color = "purple", fillColor = "purple", fillOpacity = 0.9, weight = 3, popup = ~paste0( "2025 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", ifelse(exists("Name") && !is.na(Name), paste0("LORP Reach: ", Name, " "), ""), "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2025 Data" ) %>% # Add water quality stations as reference points (if data available) {if(nrow(wq_stations) > 0) { addCircleMarkers( data = wq_stations, radius = 4, color = "white", fillColor = "blue", fillOpacity = 0.8, weight = 2, popup = ~paste0("Water Quality Station Reference Point"), group = "Water Quality Stations" ) } else { . # No stations to add }} %>% # Add reach labels (centroids) addLabelOnlyMarkers( data = reach_centroids, label = ~Reach_Label, labelOptions = labelOptions( noHide = TRUE, direction = "center", textOnly = TRUE, style = list( "color" = "white", "font-size" = "14px", "font-weight" = "bold", "text-shadow" = "1px 1px 2px rgba(0,0,0,0.8)" ) ), group = "Reach Labels" ) %>% addLayersControl( baseGroups = c("World Imagery", "OpenStreetMap"), overlayGroups = c("2018 Data", "2019 Data", "2020 Data", "2021 Data", "2022 Data", "2023 Data", "2024 Data", "2025 Data", "Reach Labels", if(nrow(wq_stations) > 0) "Water Quality Stations"), options = layersControlOptions(collapsed = FALSE) ) %>% addLegend( position = "bottomright", colors = c("black", "darkred", "red", "yellow", "orange", "green", "blue", "purple", if(nrow(wq_stations) > 0) "blue"), labels = c("2018", "2019", "2020", "2021", "2022", "2023", "2024", "2025", if(nrow(wq_stations) > 0) "WQ Stations"), title = "Pepperweed Distribution by Year" ) combined_map ``` ## `r params$current_year` Distribution - plants per site estimated from field observations The following histogram shows the distribution of abundance values for all pepperweed point locations documented in `r params$current_year`: ```{r abundance-histogram} #| message: false #| warning: false # Filter for current year data only pepper_current_year <- pepper_data %>% filter(Year_Display == params$current_year) # Create abundance histogram if(nrow(pepper_current_year) > 0) { # Create abundance bins (preserve all bins, exclude empty ones) abundance_data <- pepper_current_year %>% filter(!is.na(Abundance)) %>% mutate( Abundance_Bin = case_when( Abundance <= 10 ~ "1-10", Abundance <= 25 ~ "11-25", Abundance <= 50 ~ "26-50", Abundance <= 100 ~ "51-100", TRUE ~ ">100" ), Abundance_Bin = factor(Abundance_Bin, levels = c("1-10", "11-25", "26-50", "51-100", ">100"), ordered = TRUE) ) # Only include bins that have data abundance_counts <- abundance_data %>% count(Abundance_Bin) %>% filter(n > 0) # Create bar chart with only populated bins hist_plot <- ggplot(abundance_counts, aes(x = Abundance_Bin, y = n)) + geom_bar(stat = "identity", fill = "#2E86AB", color = "white", alpha = 0.8, width = 0.7) + labs( title = paste("Distribution of Pepperweed Abundance in", params$current_year), subtitle = paste("Total sites:", nrow(pepper_current_year)), x = "Abundance Range (plants)", y = "Number of Sites" ) + theme_minimal() + theme( plot.title = element_text(size = 16, face = "bold", color = "#2E86AB"), plot.subtitle = element_text(size = 14, color = "#666666"), axis.title = element_text(size = 12), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.minor = element_blank(), panel.grid.major.x = element_blank() ) + scale_y_continuous(breaks = scales::pretty_breaks(n = 8)) print(hist_plot) # Management categories small_pops <- sum(pepper_current_year$Abundance <= 100, na.rm = TRUE) large_pops <- sum(pepper_current_year$Abundance > 100, na.rm = TRUE) cat("###", params$current_year, "Site Distribution\n") cat("- **Total Sites**:", nrow(pepper_current_year), "\n") cat("- **Small Sites (≤ 100 plants)**:", small_pops, "sites (", round(small_pops/nrow(pepper_current_year)*100, 1), "%)\n") cat("- **Large Sites (> 100 plants)**:", large_pops, "sites (", round(large_pops/nrow(pepper_current_year)*100, 1), "%)\n") # Detailed bin counts cat("\n### Detailed Abundance Distribution\n") for(i in 1:nrow(abundance_counts)) { bin_name <- abundance_counts$Abundance_Bin[i] bin_count <- abundance_counts$n[i] cat("- **", bin_name, " plants**: ", bin_count, " sites\n", sep = "") } # Range information cat("\n### Abundance Range\n") cat("- **Minimum**:", min(pepper_current_year$Abundance, na.rm = TRUE), "plants\n") cat("- **Maximum**:", max(pepper_current_year$Abundance, na.rm = TRUE), "plants\n") } else { cat("No", params$current_year, "data available for abundance analysis.\n") } ``` The interactive map above provides the primary visualization of pepperweed distribution patterns. ### Recent Sites ```{r new-sites} #| message: false #| warning: false # Show all recent sites (2024-2025) - prioritize small sites for early intervention recent_sites <- pepper_data %>% filter(Year_Display >= 2024) %>% select(Date_Display, Abundance, Height, Notes, Year_Display) %>% # Sort by abundance (smallest first) to prioritize satellite sites arrange(Abundance, desc(Year_Display)) if(nrow(recent_sites) > 0) { datatable(recent_sites, caption = "Recent Pepperweed Sites (2024-2025) - Prioritize Small Sites for Early Intervention", options = list(pageLength = 15, scrollX = TRUE)) } else { cat("No recent sites found.") } ``` # Data Quality and Limitations ## Data Sources - **Primary Source**: [ArcGIS Online Noxious Weeds 2025 Feature Service](https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0) - **Last Updated**: `r format(as.POSIXct(max(pepper_data$CreationDate, na.rm = TRUE)/1000, origin='1970-01-01'), "%B %d, %Y")` - **Coordinate System**: WGS84 (EPSG:4326) ## Limitations - Data represents documented observations only - Population estimates may vary based on survey timing and conditions - Some areas may be under-sampled due to access constraints - Abundance categories are based on field estimates # River Reach Analysis ```{r river-reach-analysis} #| message: false #| warning: false #| fig-width: 16 #| fig-height: 15 # Read river reach polygons from ArcGIS Online and filter to LORP reaches 1-6 reach_url <- "https://inyocounty.maps.arcgis.com/sharing/rest/content/items/90e5870bd5914a928bd97b023f07b807/data" river_reaches <- st_read(reach_url, quiet = TRUE) # Filter to only LORP reaches 1-6 - explicit selection desired_lorp_reaches <- c("LORP Reach 1", "LORP Reach 2", "LORP Reach 3", "LORP Reach 4", "LORP Reach 5", "LORP Reach 6") river_reaches <- river_reaches %>% filter(Name %in% desired_lorp_reaches) # Ensure both datasets have the same CRS (WGS84) river_reaches <- st_transform(river_reaches, st_crs(pepper_data)) # Check if we have reach data cat("Number of river reaches loaded:", nrow(river_reaches), "\n") cat("Reach names:", unique(river_reaches$Name), "\n") # Perform spatial join to determine which reach each pepperweed site is closest to # Use nearest neighbor join since sites might not fall exactly within reach polygons pepper_data_with_reaches <- pepper_data %>% st_join(river_reaches, join = st_nearest_feature) # Check how many sites were matched cat("Sites before filtering:", nrow(pepper_data), "\n") cat("Sites after spatial join:", nrow(pepper_data_with_reaches), "\n") cat("Sites with reach assignments:", sum(!is.na(pepper_data_with_reaches$Name)), "\n") # Filter to only sites within reaches pepper_data_with_reaches <- pepper_data_with_reaches %>% filter(!is.na(Name)) # Only keep sites that fall within a reach # Create summary data for reach analysis reach_summary <- pepper_data_with_reaches %>% st_drop_geometry() %>% group_by(Year_Display, Name) %>% summarise(Site_Count = n(), .groups = 'drop') %>% # Ensure all year-reach combinations are represented complete(Year_Display = unique(Year_Display), Name = unique(Name), fill = list(Site_Count = 0)) # Create faceted histogram for reaches if(nrow(reach_summary) > 0) { # Cap sites at 20 for better small site visibility reach_summary_capped <- reach_summary %>% mutate(Site_Count_Capped = pmin(Site_Count, 20)) # Create ordered factor for reaches (1 at top, 6 at bottom) reach_summary_capped <- reach_summary_capped %>% mutate(Name_Ordered = factor(Name, levels = rev(c("LORP Reach 1", "LORP Reach 2", "LORP Reach 3", "LORP Reach 4", "LORP Reach 5", "LORP Reach 6")))) reach_plot <- ggplot(reach_summary_capped, aes(x = Site_Count_Capped, y = Name_Ordered)) + geom_col(fill = "red", alpha = 0.8, width = 0.7) + facet_wrap(~ Year_Display, ncol = 8, scales = "fixed") + # Single row, fixed scales scale_x_continuous(limits = c(0, 20), breaks = c(0, 5, 10, 15, 20)) + # Cap at 20 scale_y_discrete() + # Reach names labs( title = "Pepperweed Sites by LORP Reach and Year", subtitle = "Distribution of sites within LORP river reaches (capped at 20 sites)", x = "Number of Sites (capped at 20)", y = "LORP Reach" ) + theme_minimal() + theme( plot.title = element_text(size = 18, face = "bold", color = "#2E86AB"), plot.subtitle = element_text(size = 16, color = "#666666"), axis.title = element_text(size = 14), axis.text.y = element_text(size = 11), axis.text.x = element_text(size = 11), strip.text = element_text(size = 13, face = "bold"), panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(), plot.margin = margin(25, 25, 25, 25) ) print(reach_plot) # Summary statistics cat("### LORP Reach Distribution Summary\n") cat("**Total Reaches Analyzed**:", length(unique(reach_summary$Name)), "\n") cat("**Years Covered**:", min(reach_summary$Year_Display), "-", max(reach_summary$Year_Display), "\n") cat("**Reaches with Data**:", length(unique(reach_summary$Name[reach_summary$Site_Count > 0])), "\n") # Most active reaches top_reaches <- reach_summary %>% group_by(Name) %>% summarise(Total_Sites = sum(Site_Count), .groups = 'drop') %>% arrange(desc(Total_Sites)) cat("\n### Reach Activity Summary\n") for(i in 1:nrow(top_reaches)) { cat("- **", top_reaches$Name[i], "**:", top_reaches$Total_Sites[i], "sites\n") } # Sites not in any reach sites_outside_reaches <- nrow(pepper_data) - nrow(pepper_data_with_reaches) if(sites_outside_reaches > 0) { cat("\n### Sites Outside LORP Reaches\n") cat("- **Sites outside reach polygons**:", sites_outside_reaches, "\n") cat("- **Percentage of total sites**:", round(sites_outside_reaches/nrow(pepper_data)*100, 1), "%\n") } } else { cat("No reach data available for analysis.\n") } ``` # Occupancy Analysis ```{r occupancy-analysis} #| message: false #| warning: false # Load river miles data if not already loaded if(!exists("rivermiles")) { rivermiles <- st_read('data/LORP_RiverMiles_revised.shp', quiet = TRUE) rivermiles <- rivermiles %>% mutate(RiverMile = as.numeric(NAME)) rivermiles <- st_transform(rivermiles, st_crs(pepper_data)) } # Create 1/10 mile segments for detailed occupancy analysis # Total of 600 segments across 60 miles (0.1 mile resolution) segment_size <- 0.1 total_miles <- 60 total_segments <- total_miles / segment_size # 600 segments # Create segment boundaries segment_boundaries <- data.frame( Segment_ID = 1:total_segments, Mile_Start = seq(0, total_miles - segment_size, by = segment_size), Mile_End = seq(segment_size, total_miles, by = segment_size) ) # Join pepperweed data with river miles and reaches pepper_data_with_miles_and_reaches <- pepper_data %>% st_join(rivermiles, join = st_nearest_feature) %>% st_join(river_reaches, join = st_nearest_feature) %>% mutate(RiverMile = as.numeric(NAME)) %>% filter(!is.na(Name)) # Only sites with reach assignments # Determine which segment each pepperweed site falls into pepper_data_with_segments <- pepper_data_with_miles_and_reaches %>% st_drop_geometry() %>% mutate( Segment_ID = floor(RiverMile / segment_size) + 1, # Ensure segments are within bounds Segment_ID = pmin(Segment_ID, total_segments) ) # Overall occupancy by year overall_occupancy <- pepper_data_with_segments %>% group_by(Year_Display) %>% summarise( Segments_Occupied = n_distinct(Segment_ID), Total_Segments = total_segments, Occupancy_Percent = round(Segments_Occupied / Total_Segments * 100, 1), .groups = 'drop' ) %>% arrange(Year_Display) # Reach-specific occupancy reach_occupancy <- pepper_data_with_segments %>% group_by(Year_Display, Name) %>% summarise( Segments_Occupied = n_distinct(Segment_ID), .groups = 'drop' ) %>% # Calculate total segments per reach left_join( pepper_data_with_miles_and_reaches %>% st_drop_geometry() %>% group_by(Name) %>% summarise( Reach_Mile_Start = min(RiverMile, na.rm = TRUE), Reach_Mile_End = max(RiverMile, na.rm = TRUE), Reach_Total_Segments = (Reach_Mile_End - Reach_Mile_Start) / segment_size, .groups = 'drop' ), by = "Name" ) %>% mutate( Occupancy_Fraction = round(Segments_Occupied / Reach_Total_Segments, 3), Occupancy_Percent = round(Occupancy_Fraction * 100, 1) ) %>% arrange(Year_Display, Name) # Display results cat("### Overall Occupancy Analysis\n") cat("**Resolution**: 1/10 mile segments (600 total segments across 60 miles)\n\n") # Overall occupancy table cat("#### Overall Occupancy by Year\n") print(kable(overall_occupancy, col.names = c("Year", "Segments Occupied", "Total Segments", "Occupancy %"), align = c("c", "c", "c", "c"), caption = "Number of 1/10 mile segments occupied by pepperweed")) # Reach occupancy table cat("\n#### Reach-Specific Occupancy\n") reach_occupancy_wide <- reach_occupancy %>% select(Year_Display, Name, Occupancy_Percent) %>% pivot_wider(names_from = Year_Display, values_from = Occupancy_Percent, names_prefix = "Year_") %>% arrange(Name) print(kable(reach_occupancy_wide, col.names = c("LORP Reach", "2018", "2019", "2020", "2021", "2022", "2023", "2024", "2025"), align = c("l", rep("c", 8)), caption = "Percentage of each reach occupied by pepperweed (by year)")) # Summary statistics cat("\n### Occupancy Summary Statistics\n") cat("**Average Annual Occupancy**:", round(mean(overall_occupancy$Occupancy_Percent), 1), "%\n") cat("**Highest Occupancy Year**:", overall_occupancy$Year_Display[which.max(overall_occupancy$Occupancy_Percent)], "(", max(overall_occupancy$Occupancy_Percent), "%)\n") cat("**Lowest Occupancy Year**:", overall_occupancy$Year_Display[which.min(overall_occupancy$Occupancy_Percent)], "(", min(overall_occupancy$Occupancy_Percent), "%)\n") # Most/least occupied reaches reach_summary <- reach_occupancy %>% group_by(Name) %>% summarise( Avg_Occupancy = round(mean(Occupancy_Percent), 1), Max_Occupancy = max(Occupancy_Percent), .groups = 'drop' ) %>% arrange(desc(Avg_Occupancy)) cat("\n**Most Occupied Reach**:", reach_summary$Name[1], "(", reach_summary$Avg_Occupancy[1], "% average)\n") cat("**Least Occupied Reach**:", reach_summary$Name[nrow(reach_summary)], "(", reach_summary$Avg_Occupancy[nrow(reach_summary)], "% average)\n") ``` # River Mile Analysis ```{r river-mile-analysis} #| message: false #| warning: false #| fig-width: 16 #| fig-height: 12 # Read river mile shapefile rivermiles <- st_read('data/LORP_RiverMiles_revised.shp', quiet = TRUE) # Convert NAME to numeric for proper sorting rivermiles <- rivermiles %>% mutate(RiverMile = as.numeric(NAME)) # Ensure both datasets have the same CRS (WGS84) rivermiles <- st_transform(rivermiles, st_crs(pepper_data)) # Perform spatial join to find closest river mile for each pepperweed site pepper_data_with_rivermiles <- pepper_data %>% st_join(rivermiles, join = st_nearest_feature) %>% select(-LAYER, -sym, -gpxx.Displ, -RiverMile) %>% # Remove unnecessary columns and duplicate RiverMile rename(RiverMile = NAME) # Create summary data for faceted histogram - bin river miles into whole miles river_mile_summary <- pepper_data_with_rivermiles %>% st_drop_geometry() %>% mutate(RiverMile_Binned = floor(as.numeric(RiverMile))) %>% # Bin to whole miles group_by(Year_Display, RiverMile_Binned) %>% summarise(Site_Count = n(), .groups = 'drop') %>% # Ensure all year-rivermile combinations are represented (extend to mile 60) complete(Year_Display = min(Year_Display):max(Year_Display), RiverMile_Binned = 0:60, fill = list(Site_Count = 0)) # Calculate actual reach boundaries from pepperweed data # Get the minimum river mile for each reach (northern boundary) pepper_data_with_reaches_and_miles <- pepper_data %>% st_join(rivermiles, join = st_nearest_feature) %>% st_join(river_reaches, join = st_nearest_feature) %>% mutate(RiverMile = as.numeric(NAME)) %>% filter(!is.na(Name)) # Only sites with reach assignments reach_boundaries <- pepper_data_with_reaches_and_miles %>% st_drop_geometry() %>% group_by(Name) %>% summarise( Min_RiverMile = min(RiverMile, na.rm = TRUE), Max_RiverMile = max(RiverMile, na.rm = TRUE), .groups = 'drop' ) %>% mutate( Reach_Num = as.numeric(gsub("LORP Reach ", "", Name)), # Use minimum river mile as the northern boundary (top of reach) Label_Position = Min_RiverMile ) %>% # Sort by minimum river mile to ensure proper ordering (R1 at top, R6 at bottom) arrange(Min_RiverMile) %>% # Reassign reach numbers based on actual river mile order (1 at top, 6 at bottom) mutate(Reach_Num = row_number()) %>% # Create proper reach names mutate(Name = paste0("LORP Reach ", Reach_Num)) # Display the reach boundaries table cat("### Reach Boundaries (Northern Boundaries)\n") cat("**Table showing minimum river mile for each reach (northern boundary):**\n\n") print(kable(reach_boundaries, col.names = c("LORP Reach", "Min River Mile", "Max River Mile", "Reach Number", "Label Position"), align = c("l", "c", "c", "c", "c"), caption = "Northern boundaries (minimum river miles) for each LORP reach")) # Use these actual boundaries for the plot cat("\n**Using actual reach boundaries from data:**\n") print(reach_boundaries) # Create a simple table showing labels and river miles reach_labels_table <- reach_boundaries %>% select(Name, Label_Position) %>% arrange(Label_Position) cat("\n### Reach Labels and River Miles\n") cat("**Table showing reach labels and their river mile positions:**\n\n") print(kable(reach_labels_table, col.names = c("Reach Label", "River Mile"), align = c("l", "c"), caption = "Reach labels and their river mile positions")) # Create a simple table from pepper_data grouped by reach pepper_data_with_miles <- pepper_data %>% st_join(rivermiles, join = st_nearest_feature) %>% st_join(river_reaches, join = st_nearest_feature) %>% mutate(RiverMile = as.numeric(NAME)) %>% filter(!is.na(Name)) # Only sites with reach assignments reach_min_miles <- pepper_data_with_miles %>% st_drop_geometry() %>% group_by(Name) %>% summarise(Min_RiverMile = min(RiverMile, na.rm = TRUE), .groups = 'drop') %>% arrange(Min_RiverMile) cat("\n### Reach Minimum River Miles\n") cat("**Table showing minimum river mile for each reach:**\n\n") print(kable(reach_min_miles, col.names = c("Reach", "Min River Mile"), align = c("l", "c"), caption = "Minimum river mile for each reach")) # Create faceted histogram if(nrow(river_mile_summary) > 0) { # Calculate consistent x-axis limits across all years max_sites <- max(river_mile_summary$Site_Count, na.rm = TRUE) x_limit <- ceiling(max_sites * 1.1) # Add 10% padding # Cap sites at 20 for better small site visibility and reverse y-axis (0 at top) river_mile_summary_capped <- river_mile_summary %>% mutate(Site_Count_Capped = pmin(Site_Count, 20), RiverMile_Binned = factor(RiverMile_Binned, levels = sort(unique(RiverMile_Binned), decreasing = TRUE))) # Manually set reach boundaries based on the table values # LORP Reach 1: 0.1 -> bin 0 # LORP Reach 2: 3.7 -> bin 3 or 4 # LORP Reach 3: 19.7 -> bin 19 or 20 # LORP Reach 4: 34.5 -> bin 34 or 35 # LORP Reach 5: 39.0 -> bin 39 # LORP Reach 6: 49.7 -> bin 49 or 50 reach_boundaries_plot <- data.frame( Name = c("Reach 1", "Reach 2", "Reach 3", "Reach 4", "Reach 5", "Reach 6"), Closest_Bin = c("0", "4", "20", "35", "39", "50") ) cat("\n### Manual Reach Boundaries for Plot\n") print(reach_boundaries_plot) river_plot <- ggplot(river_mile_summary_capped, aes(x = Site_Count_Capped, y = factor(RiverMile_Binned))) + geom_col(fill = "red", alpha = 0.8, width = 0.7) + # Add horizontal lines for reach boundaries using closest binned values geom_hline(data = reach_boundaries_plot, aes(yintercept = Closest_Bin), color = "blue", linetype = "solid", alpha = 0.8, size = 1) + # Add reach labels - one per reach, positioned below the lines and well within visible area geom_text(data = reach_boundaries_plot %>% mutate(Year_Display = 2018), # Only show labels on 2018 panel aes(x = 10, y = Closest_Bin, label = Name), hjust = 0, vjust = 1.5, color = "blue", size = 6, fontface = "bold") + facet_wrap(~ Year_Display, ncol = 8, scales = "fixed") + # Single row, fixed scales scale_x_continuous(limits = c(0, 20), breaks = c(0, 5, 10, 15, 20)) + # Cap at 20 scale_y_discrete(breaks = function(x) x[seq(1, length(x), by = 5)]) + # Every 5 miles labs( title = "Pepperweed Sites by River Mile and Year", subtitle = "Distribution of sites along the LORP river corridor (binned by mile, capped at 20 sites)", x = "Number of Sites (capped at 20)", y = "River Mile" ) + theme_minimal() + theme( plot.title = element_text(size = 22, face = "bold", color = "#2E86AB"), plot.subtitle = element_text(size = 18, color = "#666666"), axis.title = element_text(size = 16, face = "bold"), axis.text.y = element_text(size = 14), axis.text.x = element_text(size = 14), strip.text = element_text(size = 16, face = "bold"), panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(), plot.margin = margin(25, 25, 25, 25) # Add margin for better spacing ) print(river_plot) # Summary statistics cat("### River Mile Distribution Summary\n") cat("**Total River Miles Analyzed**:", length(unique(river_mile_summary$RiverMile_Binned)), "\n") cat("**Years Covered**:", min(river_mile_summary$Year_Display), "-", max(river_mile_summary$Year_Display), "\n") cat("**River Mile Range**:", min(river_mile_summary$RiverMile_Binned), "-", max(river_mile_summary$RiverMile_Binned), "\n") # Most active river miles top_river_miles <- river_mile_summary %>% group_by(RiverMile_Binned) %>% summarise(Total_Sites = sum(Site_Count), .groups = 'drop') %>% arrange(desc(Total_Sites)) %>% head(10) cat("\n### Top 10 Most Active River Miles\n") for(i in 1:nrow(top_river_miles)) { cat("- **River Mile", top_river_miles$RiverMile_Binned[i], "**:", top_river_miles$Total_Sites[i], "sites\n") } } else { cat("No river mile data available for analysis.\n") } ``` # Field Documentation ```{r field-images} #| message: false #| warning: false #| results: 'asis' # Function to check if a feature has attachments check_feature_attachments <- function(objectid) { base_url <- "https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0" attachments_url <- paste0(base_url, "/", objectid, "/attachments?f=json") response <- GET(attachments_url) if(response$status_code == 200) { attachments_data <- content(response, 'parsed') return(length(attachments_data$attachmentInfos) > 0) } return(FALSE) } # Function to get attachments for a feature get_feature_attachments <- function(objectid) { base_url <- "https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0" attachments_url <- paste0(base_url, "/", objectid, "/attachments?f=json") response <- GET(attachments_url) if(response$status_code == 200) { attachments_data <- content(response, 'parsed') return(attachments_data$attachmentInfos) } else { return(NULL) } } # Function to display an image from attachment display_attachment_image <- function(objectid, attachment_id, width = 500) { base_url <- "https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0" image_url <- paste0(base_url, "/", objectid, "/attachments/", attachment_id) # Create HTML for image display html_content <- paste0( '<div style="display: inline-block; margin: 15px; text-align: center;">', '<img src="', image_url, '" style="max-width: ', width, 'px; height: auto; border: 2px solid #ddd; border-radius: 8px; box-shadow: 0 4px 8px rgba(0,0,0,0.15);">', '</div>' ) # Use cat to output HTML directly cat(html_content) } # Find features with attachments (check more features for up to 40 photos) cat("## Field Documentation Images\n") cat("Pepperweed observations with field photos:\n\n") # Check which features have attachments - look at more features to get up to 40 photos features_to_check <- pepper_data %>% filter(Year_Display >= params$current_year - 2) %>% # Include last 2 years arrange(desc(Year_Display), desc(Abundance)) %>% head(100) # Check more features to find ones with attachments features_with_attachments <- c() for(i in 1:nrow(features_to_check)) { objectid <- features_to_check$OBJECTID[i] if(check_feature_attachments(objectid)) { features_with_attachments <- c(features_with_attachments, objectid) } } # Display images for features that have attachments (up to 40) images_displayed <- 0 if(length(features_with_attachments) > 0) { for(objectid in features_with_attachments[1:min(40, length(features_with_attachments))]) { # Get feature data feature <- pepper_data[pepper_data$OBJECTID == objectid,] # Get attachments attachments <- get_feature_attachments(objectid) if(!is.null(attachments) && length(attachments) > 0) { # Get the first image attachment first_attachment <- attachments[[1]] # Display image first display_attachment_image(objectid, first_attachment$id) # Display metadata below the image cat("<div style='text-align: center; margin-bottom: 30px;'>") cat("Feature ID:", objectid, "| Date:", format(feature$Date_Display, "%Y-%m-%d"), "| Abundance:", feature$Abundance, "plants") # Include Notes if available if(!is.na(feature$Notes) && feature$Notes != "") { cat("Notes:", feature$Notes, "") } cat("</div>") images_displayed <- images_displayed + 1 } } } if(images_displayed == 0) { cat("No field documentation images available for recent observations.\n") } else { cat("\n*Field documentation images provide visual confirmation of pepperweed sites and help verify site characteristics.*\n") } ``` # Conclusion The `r params$start_year`-`r params$current_year` data reveals expanding pepperweed distribution within the LORP area. The interactive mapping tool provides land managers with a visualization tool for identifying and prioritizing treatment areas over time. This report will be updated as new data are added to the live feature service. The data source is available at: [ArcGIS REST Service](https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0) ## Data Disclaimer The data presented in this report are collected for monitoring and management purposes by the Inyo County Water Department. While every effort is made to ensure accuracy, field conditions, survey timing, and observer experience may affect data quality. Users should verify critical information independently before making management decisions. --- **Inyo County Water Department** © 2025 Inyo County, California. All rights reserved. *This analysis is automatically updated when new data is added to the ArcGIS feature service.*