# Title of Dataset: colaptes_woodpeckers_nest_survival --- Brief summary of dataset contents, contextualized in experimental procedures and results: Empirical data (observational study) obtained after monitoring Colaptes campestris and Colaptes melanochloros nests during three breeding seasons in east-central Argentina. We searched for nests intensively through every woodland within the area (~100 ha surface) between October and January (Austral spring-summer). Once nests were found (cavities with eggs or nestlings), we monitored them every 3-9 days until nest attempt finished, which was either chicks leaving the cavity fully feathered or the nest attempt failing for several reasons. After we stablished nest fate, we recorded nest-site features at cavity and lanscape scales. We found 52 nests (25 succesful and 27 failed) of Colaptes campestris and 79 (42 succesful and 37 failed) of C. melanochloros. ## Description of the Data and file structure Excel CSV: Columns within the data set are variables measured. Each row is an interval which represents the time elapsed between two visits to the nests. The response variable is coded as: 1 = nest content survived this inverval; 0 = nest content not survived. Which means each row has a 1 or 0 value assigned (Survival column). Ultimately, using a log-exposure model, the idea is to account for the time a nest was exposed. The model returns a value known as Daily Survival Rate (DSR) which is the probability that a nest survives from one day to the next one. Details of CSV file columns: nido = Nest.ID (each nest has a different ID, and repeated numbers are different intervals from the same nest). especie = woodpecker species. Colaptes melanochloros = Green-barred Woodpecker; Colaptes campestris = Campo Flicker. temp = breeding season. ti_nido = time (in min) the nest visit lasted. To test a researcher effect on interval fate. Interval = intervals within the nest attempt. Every nest starts with interval = 1 onwards until nest attempt finished. Exposure = number of days elapsed between two nest visits. If nests are found during construction, there are not considered as "exposed". This only applies for nests with eggs or nestlings. Hence, if a nest is found during construction and the next visit is still under construction, there is no exposure interval between those visits. Surv = response variable coded as 1 = nest content survived the interval; 0 = nest content did not survive the interval. FechaMedia = average date (in days) within the breeding season when the interval occurred. It was considered as the midpoint between the visits. Day 1 = October 1st. E.g. Nest visit 1 = day 10, nest visit = day 16; Date assigned to the interval = day 13. EdadMedia = average nest age of the nest for that interval. Considered as the age of the nest at the midpoint between nest visits. Day 1 (of the nest) = day the first egg was laid. E.g. Nest 11 was 6 days old (6 days elapsed since the first egg was laid). Interval 1 "EdadMedia" for nest 11 = 9 days. As 7 days passed between first and second visits (Exposure = 7 days), we added half this value to initial nest age (6+3 = 9 is the average age of the nest for that interval). For uneven numbers (like this example), we used the lower value of the two possible (7/2 = 3.5, we kept 3). arbol = tree species where the cavity was excavated. alt_nido = distance (in m) between cavity opening and the ground (i.e. nest height). dap = diameter (in cm) at breast height (DBH) of the tree bearing the cavity. inc = cavity opening vertical inclination (in °). dist_min = diameter (in cm) of the cavity opening. Recorded as the shortest distance an individual of any kind has to pass through to access cavity content. vol_cav = cavity volume (in cm3). Modelling the cavity as a cilinder, cavity volume was calculated using cavity depth (cilinder height) and diameter (base circle distance). cob500 = surface covered by forest (forest cover, recorded in ha) within a 500 m radius circle centered on each nest-tree. shape_in500 = a ratio estimated as the amount of forest edge per amount of forest surface. Formula is: P/2*√π*F; where P is forest edge and F is forest cover. Amount of edge and forest within a 500 m radius circle centered on the nest-tree. dist_cam = distance (in m) from the nest to the nearest road. promB = amount of foliage cover 50 cm above the cavity opening. We used a 1-m pole (divided into five 20-cm sections) 50 cm above the cavity hole in the direction of the four cardinal directions, which resulted in eight measurements per nest. We assigned a foliage cover category (from 1 to 5) for each pole section, considered as: 1 = 0–20 % of coverage; 2 = 21–40 % of coverage; 3 = 41–60 % of coverage; 4 = 61–80 % of coverage; and 5 = 81–100 %. These categories were established by locating a 10 cm radius paperboard circle (centered in the pole) at each pole section and observing the amount of foliage (branches, thorns and leaves) covering the circle. We then averaged values and larger values represented greater foliage cover. promC = amount of foliage cover 50 cm below the cavity opening. We used a 1-m pole (divided into five 20-cm sections) 50 cm below the cavity hole in the direction of the four cardinal directions, which resulted in eight measurements per nest. We assigned a foliage cover category (from 1 to 5) for each pole section, considered as: 1 = 0–20 % of coverage; 2 = 21–40 % of coverage; 3 = 41–60 % of coverage; 4 = 61–80 % of coverage; and 5 = 81–100 %. These categories were established by locating a 10 cm radius paperboard circle (centered in the pole) at each pole section and observing the amount of foliage (branches, thorns and leaves) covering the circle. We then averaged values and larger values represented greater foliage cover. There is only one script file (R code) separated in two parts, one for each species. Although data set is also one and analyses are the same, we suggest to split it into two files, to analyze both species separately. After separating the original data frame into to files, one of them should be loaded to R. Please note that data frame has to be named as 'carpint' for everything to work. If you run the line codes from that moment onwards, you should reach the same results as we did in the related paper. Basically, differences appear in the last part, as which models to average and which graphs to make depends on the results of each species. Preliminary model sets are exactly the same. ## Sharing/access Information Links to other publicly accessible locations of the data: Was data derived from another source? No.