Wild edible berry shrub

Wild edible berry shrub and shrub traits at 1 forest
Style: Other / CSE- Council of Science Editors 8th ed
Number of pages: 45 pages/double spaced (12375 words)
PowerPoint slides: 0
Number of source/references: 170
Extra features: Abstract page
Order instructions:
Most up to date versions r on Google: https://docs.google.com/document/d/1GkTmXa7q5Jkrq4YQRK1LyBku6pOLdIfKKxtL6kuGdWE/edit?usp=sharing 120 pg draft thesis w/ notes. Template: https://docs.google.com/document/d/1GF1piEOJTnRscJYrmIdbWXB-lReNuEQro32u9RxPqyQ/edit?usp=sharing Spreadshet includes 130 GPS pts @ plots: https://docs.google.com/spreadsheets/d/1cvrRjwLw_QJOKFqRlm_nQiktvaQYG6rNyJbvi9i6Jeg/edit?usp=sharing Plots mimick berry patch size/shape: circular plot 10 m diameter measured with 2 field tape measures layed out perpendicular crossing @ plot center. Plot centers based on being walk-able accessible areas (near roads) of forest since I looked at logging road map layer in Arc Maps. Also have map layer that classifies forest into 7 categories based on "4 possible dominant overstory tree species" & "logging history based on 3 type: unlogged, clearcut, or thinned." I used Arcmap random# generator to place roughly equal# of plots in each of 7 categories at random GPS locations within those 7 categorical areas of forest. Use Garmin GPS to walk to each randomly generated GPS point & look for target species w/ most berries of any of the target species shrubs within 30 m of that randomly generated GPS point, since that's how I as an indigenous traditional berry picker would find shrubs to harvest berries from for food security, as that who I want to inform: local indigenous berry pickers. If no target species found w/ stem length over 3 m (since shrubs w/ stems < 3 m length generally don't produce berries) were within 30 m of that random GPS point I noted a zero. Each (130) plot has row in spreadsheet. Each (12) trait recorded at every plot (including dominant overstory tree species & logging history from map layer) has column in spreadsheet. Traits are quantitative based or qualitative value based which were recorded to see if traits could explain the quantity of berries per shrub which I recorded as a berry productivity score of 1, 2 or 3 based on # of berries on single target shrub species at center of plot (berries per plant code: 0= 0 berries, 150 berries); standing @ plot center looking 10 m away @ land: clinometer for slope quantity (0-100) & categorical # (1-360) for aspect; canopy openness quantity (0-100%) indicating % tree canopy is open to sunlight based on 4 convex densiometer readings facing in each cardinal direction while standing at plot center, quantity of target species shrubs individuals per 10 m diameter plot, mean length of longest stem to 0.1 m of target species in plot: measured longest stem of each target species in plot for using field tape measure, mean diameter of each overstory tree by species w/in plot: measured diameter @ breast height for trees w/ diameter > 10 cm @ breast height using Biltmore stick; mean ground cover quantity (0-100%) for: each plant species (all grass species= grass), woody debris (dead wood w/ diameter > 10 cm) & leaf litter (dead plant matter w/ diameter < 10 cm) w/in plot: used 2 field tape measures perpendicular across plot crossing @ center to divide plot into 4 quarters to visually estimate ground cover from plot center @ each quarter & later get mean; quantity of nodes (branch forks or bumps on stem from broke off branch) on longest stem of target species w/in plot; & mean quantity of stems per individual shrub of target species w/in plot. 170 citations about topic from online, hard copies, & government papers. Please include: Purpose is to find traits of plots with highest quantity of berries to inform tribal gatherers & land managers about productive berry patch traits & provide location of productive berry patches @ 1 forest. Traits: target species, forest & land traits I measured; indigenous (tribe's) knowledge from citations & my own experience being a tribal member. Describe data: show plots w/ most berries (& perhaps longest stem & most target-species shrubs per plot) & traits of those most productive plots. If literature suggests traits I recorded can influence berry quantity (& perhaps longest stem & most target-species shrubs per plot). Include local tribe’s (indigenous) perspective on target species as I'm a tribe member myself & shrub's berries traditionally eaten by tribe. Forest w/ plots is close to tribe communities; US Forest Service owns forest & wants tribe to co-manage forest & have tribal people gather plants there like berries. Choose 1of2 options: Option1: mark locations of plots w/ most berries (& perhaps longest stems & highest quantity of single shrubs per plot) on map (use ArcMap or other academic-ok’d software) (overlay productive plot locations onto tree overstory species & logging type map layers I have); or Option2: Are plots w/ most berries (& perhaps longest stems & most target-species shrubs per plot) determined by traits I recorded; demonstrate extremely simple formula or similar in "R" stats software or Excel; does target species berry quantity vary with change in traits I recorded.