DOI: 10.5061/dryad.3r2280gpv
title: Data from: Season, prey availability, sex and age explain prey size selection in a large solitary carnivore

description: Prey selection is a fundamental aspect of ecology that drives evolution
 and community structure, yet the impact of intraspecific variation on the
 selection for prey size remains largely unaccounted for in ecological
 theory. Here, we explored puma (Puma concolor) prey selection across 6
 study sites in North and South America. Our results highlighted the strong
 influence of season and prey availability on puma prey selection, and the
 smaller influence of puma age. Pumas in all sites selected smaller prey in
 warmer seasons following the ungulate birth pulse. Our top models included
 interaction terms between sex and age, suggesting that males more than
 females select larger prey as they age, which may reflect experiential
 learning. When accounting for variable sampling across pumas in our 6
 sites, male and female pumas killed prey of equivalent size, even though
 males are larger than females, challenging assumptions about this species.
 Nevertheless, pumas in different study sites selected prey of different
 sizes, emphasizing that the optimal prey size for pumas is likely
 context-dependent and affected by prey availability. The mean prey weight
 across all sites averaged 1.18 times mean puma weight, which was less than
 predicted as the optimal prey size by energetics and ecological theory
 (optimal prey = 1.45 puma weight). Our results help refine our
 understanding of optimal prey for pumas and other solitary carnivores, as
 well as corroborate recent research emphasizing that carnivore prey
 selection is impacted not just by energetics but by the effects of diverse
 ecology.
description: <strong>GPS Programming and Identifying Puma
 Prey</strong> We programmed GPS collars to obtain
 location data at 1- or 2-hr intervals (i.e., 12 or 24 locations/day). GPS
 data was transmitted through an Argos uplink at 3-day intervals in
 Patagonia and Mendocino, or 2-6 times per day via Iridium and Globalstar
 uplinks for the remaining sites.             We
 identified aggregated GPS points, termed GPS clusters (Anderson Jr and
 Lindzey 2003), via visual assessments in GoogleEarth or ArcGIS, except in
 Siskiyou and Washington, where we employed a Python script (Python
 Software Foundation Hampton, NH) to assess GPS data and identify clusters.
 We defined clusters as any ³2 points within 150 m of each other spanning
 2-hrs to two weeks, except in Wyoming and Washington, where we identified
 clusters that spanned 4-hrs to 2 weeks, and Mendocino, where identified
 clusters spanned 8-hrs to 2 weeks. Researchers investigated GPS clusters
 in the field using handheld GPS units to navigate to sites, and assessed
 hair, skin, rumen, and bone fragments to identify prey species and sex. We
 differentiated predation from scavenging based upon associated signs,
 including bite marks, blood splatter, and signs of chase or struggle
 (Elbroch et al. 2013). Ungulate prey age was determined based on tooth
 eruption sequences and lower mandible wear, with individuals ³3 years
 considered as adults (Elbroch et al. 2013). We determined prey weights
 from the published literature and, in some cases, utilized ungulate
 neonate growth curves (Table A.1; A.2).
 <em>Statistical Analyses </em> We
 evaluated 10 <em>a priori</em> candidate models (Table 1) that
 tested varying aspects of our three hypotheses in R Statistical Software
 (Version 4.2.2 R Core Team 2022). To determine whether pumas killed larger
 prey in winter, in sites where larger prey were available, and with
 increased age (our first hypothesis), we utilized the fixed effect
 variables <em>season</em>, <em>site</em> (i.e.,
 research site), <em>max prey</em> (prey availability) and puma
 <em>age</em>. We examined the prediction that males will
 select larger prey than females (our second hypothesis) using variable
 <em>sex</em> and interaction terms
 <em>sex</em>*<em>age</em>, as well as
 <em>sex</em>*<em>max prey</em>. To test our third
 hypothesis, we calculated <em>mean prey size</em> that pumas
 utilized at both the site and the multi-site level.
             We determined seasonal classifications
 (<em>season</em>) based on ungulate parturition dates at each
 site, which occur in late May and early June for ungulates, including deer
 and elk across California, Wyoming, Washington, and Colorado (Hines and
 Lemos 1979; Bowyer 1991; Smith 1994; Whittaker and Lindzey 1999; Peterson
 et al. 2017), and November and December in Patagonia (Gonzalez et al.
 2006; Corti et al. 2010). For northern sites, we defined summer as the 3
 months from May 15 until August 15, and then Autumn, Winter, and Spring as
 the 3-month intervals following summer. In Patagonia, we defined summer as
 the 3-month interval from November 15 until February 15, and then Autumn,
 Winter and Spring following at 3-month intervals. We
 categorized the largest prey available to each puma in its home range
 (<em>max prey</em>) using a categorical variable that was
 based on prey weight (3 values: deer, guanaco, elk). We classified puma
 <em>age</em> (months) using gum line recession measured at
 captures, following Laundré (2000), or by birthdate for pumas for which we
 knew this information. We estimated puma age at the time of each kill by
 adding an individual’s age at capture to the number of days since said
 capture before the kill was made. We log-transformed
 <em>age</em> at the time of the kill for analyses. We
 determined puma<em> sex</em> (M or F) at the capture
 event. <strong> </strong>We
 used<strong> </strong>Generalized Linear Models (GLMs) with a
 log-link function and gamma distribution for hypothesis testing. In our
 gamma regression analyses, we used <em>prey weight</em> (in
 kg) as the response variable. To estimate <em>prey weight</em>
 for each prey item that pumas consumed at a site, we excluded prey with
 neither discernible age nor sex characteristics. We assigned prey with
 identifiable age characteristics but no discernible sex the median
 species-specific weight for males and females within that age class. We
 excluded kill sites with no corresponding date for the kill from this
 analysis.       We included a random effect for puma
 (<em>ID</em>) to avoid pseudoreplication and biases introduced
 by sampling one puma more than another. We used Variance Inflation Factors
 (VIF) to assess multicollinearity amongst covariates. Amongst correlated
 covariates, we considered any VIF scores &gt;2 to have large impacts,
 with VIF &gt;5 considered highly correlated and VIF &gt;10
 considered a severe correlation (Graham 2003). We fit all 10 models using
 the ‘lme4’ package (Bates et al. 2015) in R. We ranked models using
 Akaike’s Information Criterion corrected for small sample size (AICc). We
 considered any model within ∆AICc &lt;2 of the lowest AICc model as
 top models (Burnham and Anderson 2002).             We
 conducted post-hoc ANOVA tests to determine whether pumas selected
 different prey sizes at different sites. When a significant
 <em>p</em>-value was generated, we assumed at least two sites
 had significant differences. To investigate this further, we ran a Tukey
 HSD test for site comparisons. Finally, we calculated
 mean prey size for pumas as compared to mean puma weights, to test the
 assumption that mean prey size would be 1.45 times larger than mean puma
 weight, following Carbone et al. (1999) optimal prey size
 estimates.
description: # Season, prey availability, sex and age explain prey size selection in a
 large solitary carnivore
 [https://doi.org/10.5061/dryad.3r2280gpv](https://doi.org/10.5061/dryad.3r2280gpv) The following dataset includes all relevant kill site information across six sites in North and South America collected and recorded based on kill site investigations between 2008-2021. Our kill site dataset documents relevant information recorded at each kill site necessary to investigate predation dynamics, including differences in prey size selection for male vs. female pumas, relationships between age of the puma and the size of their prey, and influence of various characteristics including season and prey availability on puma prey size selection. ## Description of the data and file structure **Variables:** *site*: mendo = Mendocino site, siski = Siskiyou site, colo = Colorado site, patagonia = Patagonia site, olympic = Olympic Peninsula site, TCP = Wyoming site *cat_id*: individual ID of puma  *cat_age*: age of puma at time of kill (measured in months) *prey_type*: name of prey found at kill site *prey_weigh*t: weight (measured in kg) of prey determined either based on published literature or using ungulate neonate growth curves (this variable is discussed in more detail at the end of this section) *kill_date*: date of kill *cat_sex*: male = 1 female = 0 *max_prey*: largest prey available to each puma in its home range using a categorical variable that was based on prey weight (3 values: *deer*, *guanaco*, *elk*). *adj_season*: season categories based on ungulate parturition dates at each site For northern sites: summer was defined as the 3 months from May 15 until August 15, and then Autumn, Winter, and Spring as the 3-month intervals following summer For the southern site (Patagonia): we defined summer as the 3-month interval from November 15 until Februar 15, and then Autumn, Winter and Spring following at 3-month intervalw Prey Weight Variable continued: To estimate prey weight for each prey item that pumas consumed at a site, we excluded prey with neither discernible age nor sex characteristics. We assigned prey with identifiable age characteristics but no discernible sex the median species-specific weight for males and females within that age class. We excluded kill sites with no corresponding date for the kill from this analysis. ## Sharing/Access information There are no other publicly accessible locations of the data. ## Code/Software Our full reproducible code has been included as an R markdown file, including structuring of data and variables, running GLMs for outputs and AICc scores, and finally post-hoc ANOVA tests. We evaluated 10 a priori candidate models that tested varying aspects of our three hypotheses in R Statistical Software (Version 4.2.2 R Core Team 2022). We used Generalized Linear Models (GLMs) with a log-link function and gamma distribution for hypothesis testing. In our gamma regression analyses, we used prey weight (in kg) as the response variable. We included a random effect for puma (ID) to avoid pseudoreplication and biases introduced by sampling one puma more than another. We used Variance Inflation Factors (VIF) to assess multicollinearity amongst covariates. We fit all 10 models using the ‘lme4’ package in R. We ranked models using Akaike’s Information Criterion corrected for small sample size (AICc). We conducted post-hoc ANOVA tests to determine whether pumas selected different prey sizes at different sites. When a significant p-value was generated, we assumed at least two sites had significant differences. To investigate this further, we ran a Tukey HSD test for site comparisons. Finally, we calculated mean prey size for pumas as compared to mean puma weights, to test the assumption that mean prey size would be 1.45 times larger than mean puma weight, following Carbone et al. (1999) optimal prey size estimates. **Packages:** readxl, dplyr, lme4, AICcmodavg, car **Package Citations:** Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear   Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48.   doi:10.18637/jss.v067.i01. Fox J, Weisberg S (2019). \_An R Companion to Applied Regression_, Third edition.   Sage, Thousand Oaks CA.   [https://socialsciences.mcmaster.ca/jfox/Books/Companion/](https://socialsciences.mcmaster.ca/jfox/Books/Companion/). Mazerolle MJ (2023). \_AICcmodavg: Model selection and multimodel inference based   on (Q)AIC(c)_. R package version 2.3.3,   [https://cran.r-project.org/package=AICcmodavg](https://cran.r-project.org/package=AICcmodavg). Wickham H, Bryan J (2023). \_readxl: Read Excel Files_. R package version 1.4.3,   [https://CRAN.R-project.org/package=readxl](https://CRAN.R-project.org/package=readxl). Wickham H, François R, Henry L, Müller K, Vaughan D (2023). \_dplyr: A Grammar of   Data Manipulation_. R package version 1.1.4,   [https://CRAN.R-project.org/package=dplyr](https://CRAN.R-project.org/package=dplyr). **R Version:** R Core Team. 2022. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. [https://www.R-project.org/.](https://www.R-project.org/.)