Females may adjust prenatal allocation in relation to ecological conditions that affect reproductive success, such as weather conditions or predation risk. In cooperative breeders, helpers might also influence reproductive success, and previous studies suggest that females can lay smaller eggs or larger clutches when breeding with more helpers. Although recent work suggests that helper effects can vary according to climatic variables, how social and ecological factors interact to shape prenatal allocation is poorly understood. Here, we examine how ecological and social components of the breeding environment covary with egg mass and clutch size, using as a model the sociable weaver Philetairus socius, a colonial, cooperatively breeding passerine. The study spanned 9 years and included over 1,900 eggs from over 550 clutches. Our analyses combined natural variation in weather conditions (rainfall before each reproductive event) with a nest predator‐exclusion experiment and continuous monitoring of the mother's social environment, allowing us to estimate how individual females adjust allocation to reproduction as their number of helpers varies. We found that egg mass varied consistently within females and did not clearly differ in relation to rainfall or predation risk. Contrary to previous studies, there was no evidence for plastic adjustments as females gained and lost helpers, and egg mass was instead better predicted by mother size and identity. Females laid larger clutches when breeding in environments where predation risk was experimentally reduced and after higher rainfall levels. Yet, there was no evidence for increasing clutch size as the number of helpers increased, nor for an interaction between helper effects and ecological factors. We conclude that while sociable weaver females can vary their clutch size, they show high individual consistency in egg mass. In addition, we found no evidence that females may maximize fitness through plastic prenatal allocation in relation to the number of helpers, or that the presence/absence of helper effects is modulated by rainfall levels or predation risk. These results challenge our current knowledge on some of the possible benefits of breeding with helpers and call for more long‐term analyses on reproductive allocation adjustments in other cooperative systems.

Social network analyses allow studying the processes underlying the associations between individuals and the consequences of those associations. Constructing and analyzing social networks can be challenging, especially when designing new studies as researchers are confronted with decisions about how to collect data and construct networks, and the answers are not always straightforward. The current lack of guidance on building a social network for a new study system might lead researchers to try several different methods and risk generating false results arising from multiple hypotheses testing. Here, we suggest an approach for making decisions when starting social network research in a new study system that avoids the pitfall of multiple hypotheses testing. We argue that best edge definition for a network is a decision that can be made using a priori knowledge about the species and that is independent from the hypotheses that the network will ultimately be used to evaluate. We illustrate this approach with a study conducted on a colonial cooperatively breeding bird, the sociable weaver. We first identified two ways of collecting data using different numbers of feeders and three ways to define associations among birds. We then evaluated which combination of data collection and association definition maximized (a) the assortment of individuals into previously known “breeding groups” (birds that contribute toward the same nest and maintain cohesion when foraging) and (b) socially differentiated relationships (more strong and weak relationships than expected by chance). This evaluation of different methods based on a priori knowledge of the study species can be implemented in a diverse array of study systems and makes the case for using existing, biologically meaningful knowledge about a system to help navigate the myriad of methodological decisions about data collection and network inference.

Climate exerts a major influence on reproductive processes, and an understanding of the mechanisms involved and which factors might mitigate adverse weather is fundamental under the ongoing climate change. Here, we study how weather and nest predation influence reproductive output in a social species, and examine whether larger group sizes can mitigate the adverse effects of these factors. We used a 7‐year nest predator‐exclusion experiment on an arid‐region cooperatively breeding bird, the sociable weaver. We found that dry and, especially, hot weather were major drivers of nestling mortality through their influence on nest predation. However, when we experimentally excluded nest predators, these conditions were still strongly associated with nestling mortality. Group size was unimportant against nest predation and, although positively associated with reproductive success, it did not mitigate the effects of adverse weather. Hence, cooperative breeding might have a limited capacity to mitigate extreme weather effects.

23Constructing and analysing social networks data can be challenging. When designing 24 new studies, researchers are confronted with having to make decisions about how data 25 are collected and networks are constructed, and the answers are not always 26 straightforward. The current lack of guidance on building a social network for a new 27 study system might lead researchers to try several different methods, and risk 28 generating false results arising from multiple hypotheses testing. We suggest an 29 approach for making decisions when developing a network without jeopardising the 30 validity of future hypothesis tests. We argue that choosing the best edge definition for a 31 network can be made using a priori knowledge of the species, and testing hypotheses 32 that are known and independent from those that the network will ultimately be used to 33 evaluate. We illustrate this approach by conducting a pilot study with the aim of 34 identifying how to construct a social network for colonies of cooperatively breeding 35 sociable weavers. We first identified two ways of collecting data using different 36 numbers of feeders and three ways to define associations among birds. We then 37 identified which combination of data collection and association definition maximised (i) 38 the assortment of individuals into 'breeding groups' (birds that contribute towards the 39 same nest and maintain cohesion when foraging), and (ii) socially differentiated 40 relationships (more strong and weak relationships than expected by chance). Our 41 approach highlights how existing knowledge about a system can be used to help 42 navigate the myriad of methodological decisions about data collection and network 43 inference. 44 SIGNIFICANCE STATEMENT 45General guidance on how to analyse social networks has been provided in recent 46 papers. However less attention has been given to system-specific methodological 47 decisions when designing new studies, specifically on how data are collected, and how 48 edge weights are defined from the collected data. This lack of guidance can lead 49 researchers into being less critical about their study design and making arbitrary 50 decisions or trying several different methods driven by a given preferred hypothesis of 51 interest without realising the consequences of such approaches. Here we show that 52 pilot studies combined with a priori knowledge of the study species' social behaviour 53 can greatly facilitate making methodological decisions. Furthermore, we empirically 54show that different decisions, even if data are collected under the same context (e.g. 55 foraging), can affect the quality of a network. 56

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