DOI: 10.5061/dryad.w9ghx3fzc
title: Potential drivers of differences in breeding phenology as a component of life history strategies among coexisting species

description: Breeding phenology can have strong demographic consequences. Yet, the
 reasons why coexisting animal species differ in breeding phenology have
 received little attention. We tested selection pressures that underlie
 life history variation, such as age-specific mortality, diet, and body
 size to explain the breeding phenology of 16 coexisting songbird species
 in arid shrubland in South Africa. The average start and end dates for the
 earliest and latest species differed by 1.5 months, with a gradient among
 the remaining species. Nest predation risk generally increased through the
 season, although species differed in seasonal patterns. Species with lower
 annual adult mortality, greater seasonal increases in nest predation, and
 greater nest predation rates had earlier start and stop dates, thereby
 reducing demographic risks. Species with higher adult mortality had larger
 brood sizes that required more food and they bred later when food was more
 abundant. Evolved timing of breeding thereby reflecting risk management
 and food availability related to longevity and brood size. These factors
 may place unrecognized constraints on within-species responses to climate
 change. Given the importance of phenology for fitness, phenology should be
 integrated as a core life history trait in future theory, and evolutionary
 constraints need to be considered in responses to climate change.
description: We studied the breeding birds in the coastal shrubland of Koeberg
 Nature Reserve, South Africa during 7 breeding seasons: 2000-2004, 2014,
 and 2016. This site and its habitat were described in a study by Nalwanga
 et al. (2004). Adult mortality of banded birds was studied from
 2001-2007. Nests were found using parental behaviors,
 where nest-building activity in particular was readily observed, following
 Martin & Geupel (1993). Large numbers of nests were monitored each
 year using long-term protocols to estimate nest predation rates and dates
 of nest initiation. We located and monitored a total of 6754 nests for the
 16 most common species over the seven years (Table 1).
 We used the date that the first egg was laid in a nest as the
 initiation date of a nest. In cases where we found nests after the clutch
 was complete or had nestlings, we back-dated based on the average clutch
 size, incubation, and nestling periods. We defined the starting and
 stopping dates each year as the initiation dates of the 5% earliest and 5%
 last nests found for each species. We used the averages of these 5% dates
 across the seven years as the average first and last initiation dates for
 each species. <strong>Replication Statement –
 </strong>The authors wished to examine the causes of differences in
 the timing of breeding among coexisting bird species, based on 87 to 1520
 nests per species studied over 7 years. 
 Scale of
 inference The scale at which the
 factor of interest is applied 
 Number of replicates at the appropriate scale
 Species
 Species 
 16 species 
 Nests were generally checked every
 other day, but sometimes up to four days following work breaks, to
 determine status and predation. Nests were checked daily or twice daily
 near hatching and fledging to obtain exact transition timing. Nest
 predation was assumed when all nestlings disappeared more than two days
 prior to average fledging age, and parents could not be found feeding
 fledglings following Martin & Geupel (1993).
 Daily nest predation rates were estimated using the logistic
 exposure method (Shaffer, 2004)<sup> </sup>based on R x64
 v4.1.l0 for Windows (R Development Core Team, Vienna, Austria). This
 approach considers the number of days a nest was observed with eggs or
 nestlings that were exposed to possible predation between each nest check
 to minimize any potential bias from successful nests found late in their
 nesting cycle (Mayfield, 1961; Shaffer, 2004). The Mayfield Maximum
 Likelihood method provides a single estimate of nest predation rate,
 whereas the logistic exposure approach allows examination of changes in
 nest predation with covariates, such as date. A recent study showed that
 these estimates are highly correlated between these two methods (Şahin
 Arslan & Martin, 2024). Using the logistic exposure method, we
 estimated the rate of change (slope) of daily nest predation rates
 relative to the ordinal date for each species separately. We pooled all
 data across years to obtain the largest and most robust sample possible
 for each species. We used the ordinal date of each nest check, nest fate
 at the check, and the exposure interval since the previous check, or half
 the interval if predation occurred, to model nest predation as a function
 of ordinal date under the logistic exposure method for each species
 (Shaffer, 2004). Our data included 33,339 nest exposure intervals
 encompassing 56,047.5 exposure days across the 16 species. We modelled
 nest predation from the average first 5% of initiation dates through 6
 days after the average last 5% of initiation dates for each species as a
 general estimate of nest activity over the season.
 Species generally differ in nest predation rates based on nesting
 habits (Martin, 1995), which may affect starting and stopping dates of
 breeding.  Assuming seasonal changes in daily nest predation risk, then
 differing timing can yield differing nest predation rates that may not
 reflect differences in the nesting habits of species. For example, a
 species that breeds early may have a different predation rate than one
 that breeds later due to seasonal changes in predation rates and not
 necessarily reflecting the relative risk of their nesting habits.
 Consequently, we estimated a late-season date-controlled daily nest
 predation rate for each species to isolate the relative difference in risk
 among species controlled for seasonal changes in predation. We used the
 latest average initiation date (ordinal date = 285, or 12 October) in
 which all species were still initiating nests on average to obtain a
 late-season date-controlled daily predation rate for each species. We used
 the logistic exposure relationship for each species to predict their
 predation rate on this date. This date was constrained by Cape Robin-chats
 (<em>Dessonornis caffra</em>) which stopped breeding at the
 earliest. We used nest predation late in the season because species with
 higher risk may try to avoid this high nest predation late in the season
 by nesting earlier and we tested this possibility in our models for
 explaining start and stop dates. Adult mortality was
 estimated using netting, banding, and resighting of birds during the
 breeding seasons from 2001 to 2007 by Lloyd et al. (2014). Estimates were
 obtained using Cormack-Jolly-Seber modelling in a Bayesian framework and
 taken from Lloyd et al., (2014). We accounted for
 phylogenetic effects (Felsenstein 1985) in all analyses using the Caper
 package (Orme et al., 2018) in R x64 v4.1.0 for Windows (R Development
 Core Team, Vienna, Austria) based on phylogenetic generalized least
 squares analyses (PGLS). The phylogenetic tree was obtained from
 www.birdtree.org (Jetz et al., 2012) using the Hackett et al. (Hackett et
 al., 2008)<sup> </sup>backbone and imported into the program
 Mesquite (Maddison & Maddison, 2011) where a majority rules
 consensus tree was constructed from 1000 trees (see Figure S1 in
 Supporting Information). This consensus tree was then used in
 phylogenetically controlled analyses (PGLS) that incorporated Pagel’s
 lambda (Pagel, 1992) to transform branch lengths and reduce
 over-correction for phylogenetic effects (Symonds & Blomberg,
 2014). We standardized all variables to z-scores for analyses. We tested
 average start and stop dates as separate dependent variables and began
 with a global model of possible explanatory covariates that included:
 annual adult mortality rate, seasonal change in nest predation rate, late
 season nest predation rate, diet (insects vs fruit/seeds), nest type
 (enclosed vs open), and body mass. We used a standard backward, stepwise
 elimination modeling approach in the PGLS models, where we removed the
 most insignificant variable at each step to arrive at a final model of
 significant variables. Insignificant variables and their statistics at
 their last step are reported to clarify the full model that was
 tested. We further tested the possible importance of
 collinearity among covariates in influencing breeding phenology (see
 Figure 1) using a phylogenetic path analysis based on the R program
 phylopath (van der Bijl, 2018). In the end, we only report the path
 analyses in the supplementary material because they added little
 additional information beyond the PGLS results since inter-correlations
 among variables were largely lacking except for one between nest type and
 body mass and one between adult mortality probability and the rate of
 change in nest predation over the season, as discussed in
 results.
description: # Potential drivers of differences in breeding phenology as a component of
 life history strategies among coexisting species ## Description of the
 data and file structure ### dataset final.csv Species data for the
 variables tested in the paper. Variables are as follows: * spp = Latin
 scientific name of species * N = number of nests studied * first5 =
 average initiation date for the first 5% of nests for each species * last5
 = average initiation date for the last 5% of nests for each species *
 median = median initiation date of all nests for each species * mean =
 mean initiation date of all nests for each species * mort = apparent
 annual adult mortality probability * dprslope = the slope of the
 relationship between daily nest predation rate and ordinal date * Latedpr
 = daily predation rate late in the season and based on ordinal date 285 =
 12 October, which was the latest average initiation date in which all
 species were still initiating nests on average * lmas = log10 of average
 adult mass * enclose = a dummy variable for nest type, where 0 =
 open-nesting species, and 1 = enclosed-nesting species * diet = a dummy
 variable for primary diet, where 0 = insects, and 1 = fruits or seeds *
 Zvariables are all of the above variables that were standardized to
 z-scores. ## tree.txt Phylogenetic relationships of the 16 species studied
 based on www.birdtree.org (Jetz et al., 2012) using the Hackett et al.
 (Hackett et al., 2008) backbone and imported into program Mesquite
 (Maddison & Maddison, 2011) where a majority rules consensus tree was
 constructed from 1000 trees (see Figure S1 in Supporting Information).