DOI: 10.5061/dryad.8cz8w9h01
title: Nesting data from 42 Arabian Babbler (Argya squamiceps) nests

description: Human villages in deserts can provide resources in an otherwise stark
 environment, potentially buffering against extreme environmental
 conditions. It is thus expected that breeding within these villages would
 result in higher fitness. However, choosing to raise offspring in these
 resource-rich environments may have unintended negative consequences.
 Here, we studied the breeding success of a cooperative breeding bird
 nesting in habitats with different levels of human disturbance – the
 Arabian babbler (Argya squamiceps), in the Negev desert of Israel. We
 recorded 42 breeding attempts from 15 groups between March and July, 2022.
 We examined overall breeding success, brood size, and causes of nest
 failure. When nestlings were 6-days old we also calculated daily change in
 body mass and adult provisioning rate. We found that despite higher
 resource abundance in villages, proximity to villages did not affect
 provisioning rate, and nestling gained less mass at higher temperatures
 for all nests. Currently, there is no evidence that human villages are
 providing oases for nesting babblers. Nevertheless, various conservation
 interventions (e.g. encouraging residents to keep cats indoors) could
 improve babblers' overall fitness. Ultimately, we highlight how for
 some desert specialists, additional resources provided by humans may not
 do enough to counter potential negative effects.
description: Nests were monitored daily using remote motion-activated security
 cameras with 4G uplink via a SIM card (Reolink Go Plus 4G, Reolink USA)
 directly through the Reolink smartphone app (©Reolink). Nest cameras were
 placed on nests found at any stage (building, eggs, nestlings) and were
 left in place until a few days after the nest reached completion (either
 failing or succeeding). The cameras used wide-angle lenses, and so needed
 to be placed close to nests (~30 cm), but initial observations showed that
 babblers spent &lt; 5 min investigating the camera before resuming
 normal activity (e.g. brooding, incubating, feeding). We masked the
 cameras’ infra-red lights to reduce/eliminate attention to the nest when
 cameras were triggered by motion at night.
description: # Nesting data from 42 Arabian Babbler (Argya squamiceps) nests
 [https://doi.org/10.5061/dryad.8cz8w9h01](https://doi.org/10.5061/dryad.8cz8w9h01) We collected breeding data for 42 nests from 15 groups of babblers whose territories made use of habitats with different levels of anthropogenic presence. The Sde Tzin plateau consists of a mosaic of human-modified habitats including a) human villages (‘village’), b) agricultural fields and orchards (‘orchard’), and c) interspersed areas of semi-wild desert plateau (‘plateau’). For each nest we recorded its GPS location, habitat type (village, orchard, plateau), brood size (number of nestlings in the nest; from 2 – 8), group size (from 2 – 14 adults), nest attempt (from 1 – 6), the group identification (‘Group ID’), and the number of previously successful nests in that group (from 0 – 3). We calculated the distance (m) of each nest to the nearest village’s perimeter fence. For nests inside villages, this distance was set as negative values to indicate how far into the village the nest was built. We visited the nests on two days during the nestling period. On our first visit, we measured nestling Mb (g) in the morning and evening when nestlings were 6­days old to measure daily change in body mass (Δ Mb; day 1 = hatch day of first nestling). We chose 6-days old as Anava et al. (2001) found that nestling babblers gain the most Mb when they are 5.74-days old. Consequently, choosing this age would allow the most precision in identifying any effects on Mb. The second day we visited the nests was to give each nestling an identifying colored ring combination and record their pre-fledge Mb when nestlings were 10-days old. We chose 10-days old as this is the same age nestling babblers are ringed at the long-term research site in the Arava valley ~ 50 km east of Sde Tzin (Ostreiher 1999; Dragić et al. 2022), and this is a few days before the average fledging age (i.e. the age nestlings leave the nest; 12 – 14 days; Zahavi 1990). We collected adult behavioural data at the nest (provisioning rate, time spent brooding, time spent shading) from the 19 nests that had 6-day old nestlings (n = 3 village, n = 7 orchard, n = 9 plateau). We measured nestling daily Δ Mb by subtracting each nestling morning mass measurement (average time = 0800, range = 0742 – 0830 IST) from that measured in the evening (average time = 1755, range = 1727 – 1828 IST). To capture all activities at the nest during days of behavioral and nestling Mb measurements, we increased the nest-camera’s motion-detection sensitivity to medium (level 60). Due to technical difficulties with four cameras, we obtained daily Δ Mb data from 19 nests, but behavioral data from just 14 nests. Nestling daily Δ Mb (%) was then standardized to a 10h period (Equation 1). Eqn. 1: Δ* Mb = [(Mb2 – Mb1) / ((t2 – t1)/600)] / Mb1 * 100* When adult babblers visited nests, we recorded their sex (identifiable via iris color), ID (via ring color combination, when possible), and occurrence of provisioning. We then calculated a provisioning rate – the number of times an adult brought food for the nestlings per hour. When possible, we also recorded the size of prey item delivered to nestlings, based on a rough division of “small” (less than 1 cm3, almost imperceptible, no effort to place in nestlings’ mouth), “medium” (2 – 4 cm3, takes some effort to place into nestlings’ mouth), “large” (&gt; 4 cm3, adult struggles and tries multiple times to place item in nestlings’ mouth). We obtained temperature data from the Israeli Meteorological Service’s weather station at Sde Boker (ims.data.gov.il), which records air temperature (Tair) at 10-min intervals. During the study period, daytime (0600 – 2000 IST) Tair ranged from 1.2 °C – 38.5 °C, with average Tair of 23.4 °C. Missing Codes- "na" ## Code/Software Statistics were carried out in the R statistical environment v. 4.2.1 (R Core Team 2022) in RStudio v. 2022.07.1 (R Studio Team 2020). Packages used included *lme4* (Bates et al. 2015), *car* (Fox and Weisberg 2019), *multcomp* (Hothorn et al. 2008), and *dplyr* (Wickham et al. 2020). Visuals were created using packages *ggplot2* (Wickham 2016) and *reshape2* (Wickham 2017). Descriptive statistics are presented as mean ± SD. Significance was inferred when *p*-values were &lt; 0.05. We used *post hoc *(Tukey’s) tests on significant multi-level factors (i.e. habitat type) to assess the source of the variance.