Publications by Emily Runnion
Final Pristine Analysis: Brood
Bees are frequently exposed to fungicides in agricultural landscapes, and while these chemicals are generally not considered to be harmful to insect pollinators, the sublethal effects of fungicides are not well understood. We investigated the non-target effects of exposure to field-realistic concentrations of the fungicide, Pristine®, for the ...
14746 sym Python (17970 sym/48 pcs) 5 img
Pristine With Steps Organized - Drone Dry Weights
Bees are frequently exposed to fungicides in agricultural landscapes, and while these chemicals are generally not considered to be harmful to insect pollinators, the sublethal effects of fungicides are not well understood. We investigated the non-target effects of exposure to field-realistic concentrations of the fungicide, Pristine®, for the ...
19190 sym Python (20222 sym/58 pcs) 6 img
Final Dose Response Data Analysis
1 Input Pollen Data Start by imputing pollen data and creating a new data frame with average pollen consumption on a per-colony basis ### Figure out average pollen consumption by treatment pollen <- read_csv("pollen1.csv", col_types = cols(round = col_factor(levels = c("1", ...
59950 sym R (127193 sym/351 pcs) 96 img 2 tbl
Truncated - Emerge Time and Drone Counts
1 Input Pollen Data pollen <- read_csv("pollen1.csv", col_types = cols(round = col_factor(levels = c("1", "2")), treatment = col_factor(levels = c("1", ...
14072 sym Python (34938 sym/83 pcs) 6 img
Workers No Round 1
1 Input Pollen Data Start by imputing pollen data and creating a new data frame with average pollen consumption on a per-colony basis ### Figure out average pollen consumption by treatment pollen <- read_csv("pollen1.csv", col_types = cols(round = col_factor(levels = c("1", ...
13224 sym Python (81263 sym/96 pcs) 33 img
Brood Counts without Round 1
1 Input Pollen Data Start by imputing pollen data and creating a new data frame with average pollen consumption on a per-colony basis ### Figure out average pollen consumption by treatment pollen <- read_csv("pollen1.csv", col_types = cols(round = col_factor(levels = c("1", ...
25283 sym R (77108 sym/224 pcs) 59 img
Drones No Round 1
1 Input Pollen Data Start by imputing pollen data and creating a new data frame with average pollen consumption on a per-colony basis ### Figure out average pollen consumption by treatment pollen <- read_csv("pollen1.csv", col_types = cols(round = col_factor(levels = c("1", ...
14706 sym Python (39014 sym/121 pcs) 23 img
Colony Weights
1 Input Pollen Data pollen <- read_csv("pollen1.csv", col_types = cols(round = col_factor(levels = c("1", "2")), treatment = col_factor(levels = c("1", ...
10021 sym Python (17660 sym/52 pcs) 25 img
Pollen Consumption
1 Input Pollen Data pollen <- read_csv("pollen1.csv", col_types = cols(round = col_factor(levels = c("1", "2")), treatment = col_factor(levels = c("1", ...
8882 sym Python (10619 sym/28 pcs) 12 img
Drone Dry Weights
1 Input Pollen Data Start by imputing pollen data and creating a new data frame with average pollen consumption on a per-colony basis ### Figure out average pollen consumption by treatment pollen <- read_csv("pollen1.csv", col_types = cols(round = col_factor(levels = c("1", ...
17119 sym Python (19298 sym/66 pcs) 9 img