DOI: 10.5061/dryad.2280gb5vg
title: Data from: Boom and bust: The effects of masting on seed predator range dynamics and trophic cascades

description: Aim: Spatiotemporal variation in resource availability is a strong driver
 of animal distributions. In northern hardwood and boreal forests of the
 northeastern United States, tree mast events provide resource pulses that
 drive the population dynamics of small mammals, including the American red
 squirrel (Tamiasciurus hudsonicus), a primary songbird nest predator. This
 study sought to determine whether mast availability ameliorates their
 abiotic limits, enabling red squirrel elevational distributions to
 temporarily expand and negatively impact high elevation songbirds.
 Location: Northeastern United States Methods: We used two independent
 datasets to evaluate our hypotheses. First, we fit a dynamic occupancy
 model using data from camera trap surveys to evaluate red squirrel
 distributional responses to pulses in tree mast. We also assessed
 population responses using systematic auditory surveys analyzed with an
 open-population binomial mixture model. Further, we used modeled red
 squirrel abundance in nest-survival models to evaluate whether their
 abundance is correlated with daily nest survival of three songbird
 species. Results: Tree mast provided a critical resource pulse that
 resulted in a two-fold increase in the annual elevational distribution of
 red squirrels. The elevational distribution of red squirrels ranged from a
 minimum of ~450 m (range: 663 – 1145 m asl) following two consecutive
 years without a masting event to a maximum of over 1000 m (range: 443 –
 1545 m asl) after a large mast event. Daily nest survival of three
 songbird species tended to decline with an increase in the abundance of
 red squirrels. Main Conclusions: Tree mast is a central biological
 phenomenon in many temperate and boreal forests. This study reveals how
 this resource pulse results in range changes in a small mammal that is
 both a seed and bird predator, as well as prey for many carnivores. Thus,
 understanding this phenomenon can inform the conservation and management
 of northern forests, including breeding songbirds.
description: # Data from: Boom and bust: The effects of masting on seed predator range
 dynamics and trophic cascades ## Occupancy data
 **Occupancy_Cameras_RESQ.rds** - .rds file containing all associated data
 to run the occupancy model. ### Description of data **occ**: A three
 dimensional array [sites, weeks, months] with binary occurrence data of
 American red squirrels **nTraps**: The total number of camera traps
 **nWeeks**: The maximum number of weeks in a primary period (months)
 **nMonths**: The number of primary periods (month) **camera**: Integer for
 camera **wkmon**: The number of weeks within in primary period
 **mon_sampled**: Array [sites,start:end] with start and end month for each
 camera **ncovs**: number of covariates **npcovs**: number of detection
 covariates **nintcovs**: number of intercepts **nTrapsOp**: number of
 traps operating in each primary period **TrapsOp**: array with a vector of
 trap numbers operating **date**: centered date for week **date2**:
 centered date^2 for week **ecotone**: centered distance from center of
 ecotone **ecotone2**: centered distance from center of ecotone^2
 **BalsamMast**: centered probability of balsam fir mast **BeechMast**:
 centered probability of American beech mast ### Structure of occupancy
 data \## List of 18 \##  $ occ        : num [1:252, 1:5, 1:67] NA NA NA NA
 0 0 0 0 0 0 ... \##   ..- attr(*, "dimnames")=List of 3 \##  $
 nTraps     : int 252 \##  $ nWeeks     : int 5 \##  $ nMonths    : int 67
 \##  $ camera     : int [1:252] 1 2 3 4 5 6 7 8 9 10 ... \##  $ wkmon     
 : int [1:70] 5 4 4 5 4 4 5 4 4 5 ... \##  $ mon_sampled: int [1:252, 1:2]
 2 2 13 2 1 1 1 1 1 1 ... \##   ..- attr(*, "dimnames")=List of 2
 \##  $ ncovs      : num 8 \##  $ npcovs     : num 2 \##  $ nintcovs   :
 num 4 \##  $ nTrapsOp   : int [1:67] 51 61 61 55 52 52 50 50 51 49 ...
 \##  $ TrapsOp    : int [1:179, 1:67] 5 6 7 8 9 10 11 12 13 31 ... \##  $
 date       : num [1:5, 1:70] -1.54 -1.53 -1.53 -1.53 -1.52 ... \##  $
 date2      : num [1:5, 1:70] 2.37 2.35 2.34 2.33 2.31 ... \##  $
 ecotone    : num [1:252] 0.389 0.748 0.893 0.647 -0.308 ... \##  $
 ecotone2   : num [1:252] -0.958 -0.994 -0.957 -1.003 -0.362 ... \##  $
 BalsamMast : num [1:252, 1:72] 1.77 1.62 1.73 1.73 1.58 ... \##   ..-
 attr(*, "dimnames")=List of 2 \##  $ BeechMast  : num [1:252,
 1:72] -0.482 -0.482 -0.482 -0.482 -0.482 ... \##   ..- attr(*,
 "dimnames")=List of 2 ## Abundance data
 **Abundance_pointcounts_RESQ.rds** - .rds file containing all the data to
 estimate abundance from point-count surveys. ### Description of data
 **counts**: Three dimensional array [site,replicate,year] where the cell
 is populated by the number of American red squirrels encountered during
 the count. NA indicates a site was not surveyed. **elev**: centered
 elevation of the site **elev2**: centered elevation^2 **Ecotone**:
 centered distance from the center of ecotone **Ecotone2**: centered
 distance from the center of ecotone^2 **nsites**: the number of point
 count locations **nreps**: number of replicates for each site
 **nIntCovs**: number of intercepts **nGams**: number of parameters
 **nCovs**: number of covariate parameters **nPCovs**: number of detection
 parameters **nyears**: number of years **countprotocol**: vector
 indicating the count protocol **date**: centered date of count
 **BalsamMast**: centered probability of balsam fir mast **BeechMast**:
 centered probability of American beech mast ### Structure of the data \##
 List of 16\ \##  $ counts       : num [1:1150, 1:9, 1:10] NA NA NA NA NA
 NA NA NA NA NA ... \##   ..- attr(*, "dimnames")=List of 3\ \## 
 $ elev         : num [1:1150] 1.429 1.124 0.81 0.395 0.171 ... \##  $
 elev2        : num [1:1150] 1.6126 1.1898 0.7828 0.2868 0.0391 ... \##  $
 Ecotone      : Named num [1:1150] 1.851 1.545 1.252 0.834 0.605 ... \##  
 ..- attr(*, "names")= chr [1:1150] "Abraham.1"
 "Abraham.2" "Abraham.3" "Abraham.4" ... \## 
 $ Ecotone2     : Named num [1:1150] 3.1126 2.1111 1.2852 0.3345 -0.0717
 ... \##   ..- attr(*, "names")= chr [1:1150]
 "Abraham.1" "Abraham.2" "Abraham.3"
 "Abraham.4" ... \##  $ nsites       : int 1150\ \##  $
 nreps        : num [1:1150] 4 4 4 4 4 4 4 4 4 4 ... \##  $ nIntCovs     :
 num 4 \##  $ nGams        : num 2 \##  $ nCovs        : num 4 \##  $
 nPCovs       : num 3 \##  $ nyears       : int 10 \##  $ countprotocol:
 num [1:1150] 1 1 1 1 1 1 1 1 1 1 ... \##  $ date         : num [1:1150,
 1:9, 1:10] 0 0 0 0 0 0 0 0 0 0 ... \##  $ BalsamMast   : num [1:1150,
 1:11] 0.758 0.758 0.758 0.768 0.768 ... \##   ..- attr(*,
 "dimnames")=List of 2 \##  $ BeechMast    : num [1:1150, 1:11] 0
 0 0 0 0 0 0 0 0 0 ... \##   ..- attr(*, "dimnames")=List of 2 ##
 Nest survival data **OVEN_nest_windata** - .rds file containing data
 required to estimate daily nest survival for Ovenbirds
 **blpw_nest_windata** - .rds file containing data required to estimate
 daily nest survival for Blackpoll Warbler **bith_nest_windata** - .rds
 file containing data required to estimate daily nest survival for
 Bicknell's Thrush ### Description of the data **nest.ch**: Array
 [sites,days] of nest visits where 1 denotes the nest was active and 0
 indicates the nest had failed **first.nest**: The day in array of first
 observation **last.nest**: The day in array of last check **Nests**: total
 number of nests for a species **resq**: centered number of red squirrels
 **DAY**: Day of year **Year**: factor for year **nYears**: number of years
 in data set **simRESQ**: simulated number of red squirrels for predictions
 **meanRESQ**: mean red squirrels for predictions **stdRESD**: standard
 deviation for predictions ### Structure of the data \## List of 11 \##  $
 nest.ch   : int [1:177, 1:80] NA NA NA NA NA NA NA NA NA NA ... \##   ..-
 attr(*, "dimnames")=List of 2 \##  $ first.nest: int [1:177] 6
 25 8 15 38 38 31 9 14 19 ... \##  $ last.nest : int [1:177] 30 45 16 31 42
 42 35 13 29 33 ... \##  $ Nests     : int 177 \##  $ resq      : num
 [1:177] -0.591 -0.591 -0.591 -0.576 -0.556 ... \##  $ DAY       : Named
 int [1:80] 135 136 137 138 139 140 141 142 143 144 ... \##   ..- attr(*,
 "names")= chr [1:80] "day.1" "day.2"
 "day.3" "day.4" ... \##  $ Year      : num [1:177] 1 1
 1 1 1 1 1 1 1 1 ... \##  $ nYears    : num 3 \##  $ simRESQ   : num
 [1:100] -1.65 -1.62 -1.59 -1.56 -1.53 ... \##  $ meanRESQ  : num 0.887
 \##  $ stdRESQ   : num 0.536 ## Read the data into R pointcountData &lt;-
 readRDS("Abundance_pointcounts_RESQ.rds") cameraData &lt;-
 readRDS("Occupancy_Cameras_RESQ.rds") oven &lt;-
 readRDS("OVEN_nest_windata.rds") blpw &lt;-
 readRDS("blpw_nest_windata.rds") bith &lt;-
 readRDS("bith_nest_windata.rds")