Validating morphological condition indices and their relationship with reproductive success in great-tailed grackles

Affiliations:

1. Arizona State University School of Life Sciences
2. Max Planck Institute for Evolutionary Anthropology
3. University of California Santa Barbara

*Corresponding author: KB McCune (kelseybmccune@gmail.com)

Cite as: Berens JM, Logan CJ, Folsom M, Sevchik A, Bergeron L, McCune KB. Submitted to PCI Ecology Nov 2020. Validating morphological condition indices and their relationship with reproductive success in great-tailed grackles.

This preregistration has been pre-study peer reviewed and received an In Principle Acceptance by:

Marcos Mendez (2019 In Principle Acceptance) Are condition indices positively related to each other and to fitness?: a test with grackles. Peer Community in Ecology, 100035. 10.24072/pci.ecology.100035

• Reviewers: Javier Seoane and Isabel López-Rull

ABSTRACT

Morphological and physiological variation among individuals has the potential to influence multiple life history characteristics such as dispersal, migration, reproductive success, and survival. Individuals that are in better “condition” can disperse or migrate further or more successfully, have greater reproductive success, and survive longer, particularly in years where environmental conditions are harsh. Condition is defined in various ways, but is most often measured using an individual’s energetic state. These traits are difficult to measure directly, therefore a variety of morphological proxies to quantify energetic condition are used instead, including fat score, weight, ratio of weight to tarsus length, and a scaled mass index. However, there is mixed support regarding whether these energetic condition indices relate to life history characteristics, and whether the relationship is linear. Additionally, although some investigations use multiple morphological proxies for energetic condition, rarely have there been direct comparisons among proxies to validate that they measure the same trait. In this investigation, we define condition as an energetic state and we attempt to measure it by comparing two morphological indices (fat score and the scaled mass index) to validate whether they measure the same trait and whether they correlate with measures of reproductive success in our study system, the great-tailed grackle (Quiscalus mexicanus). We found that the morphological proxies did not correlate with each other, indicating that they do not measure the same trait. Further, neither proxy significantly correlated with reproductive success in males, measured as whether a male held a territory containing nests or not. We found that females with a high scaled mass index had a significantly lower probability that their nest would survive on any given day. However, there was no relationship between female fat score and nest survival. These results indicate that morphological measures of energetic condition should be validated before relying on their use as a condition proxy in grackles and birds in general. Future research should investigate behavioral mechanisms underlying our result that higher scaled mass index correlated with lower nest survival to better understand the importance of energetic condition for reproductive success - a necessary component for selection to act.

INTRODUCTION

Morphological and physiological variation among individuals has the potential to influence multiple life history characteristics such as dispersal, migration, reproductive fitness, and survival (Wilder et al., 2016). One trait that might be particularly likely to influence these life history characteristics is energetic condition. Individuals that are in better “condition” can disperse or migrate further or more successfully, have greater reproductive success, and survive longer (Heidinger et al., 2010; Liao et al., 2011; Wilder et al., 2016), particularly in years where environmental conditions are harsh (Milenkaya et al., 2015). For example, a study conducted on vipers showed that while the level of fat reserves in males was not related to their sexual activity, females with low fat reserves engaged in sexual interactions less frequently than those with higher fat reserves (Aubret et al., 2002). In contrast, mantids showed conflicting results regarding the relationship between fat reserves and reproductive success (Barry & Wilder, 2013). Female mantids were fed either a high protein, low lipid diet, or a high lipid, low protein diet. The females that received the high lipid diet had higher lipid content in most parts of their body compared to that of their high protein diet counterparts. However, they were not able to produce even half as many eggs as the females fed the high protein, low lipid diet. This led to lower male attraction, measured by the number of copulation events, thus negatively impacting further reproductive success.

A variety of morphological proxies have been used to quantify energetic condition [i.e., fat score, weight, ratio of mass to structural size, residuals from a linear regression of mass as a function of structural body size; Labocha et al. (2014); Jacobs et al. (2012)]. However, there is mixed support regarding whether and how these proxies relate to life history characteristics (Labocha et al., 2014; Wilder et al., 2016). A review conducted by Barnett (2015) shows that, while mass or body size measures of energetic condition are often assumed to have a positive linear relationship with fitness, this is not always the case, and the relationship should first be empirically validated before being used as a proxy (Barnett et al., 2015). In some instances, the proxy might relate to life history characteristics, but in a non-linear way. For example, theoretical simulations of small birds show that survival does not increase linearly with energy (i.e., fat) reserves (McNamara et al., 2005). If the reserves are too low, the individual is at risk of starvation. However, once the reserves get too high, the individual is at an increased risk of predation (McNamara et al., 2005). Thus, fat reserves can relate to a life history variable (survival), but in a U-shaped relationship rather than a linear one.

Although some studies use multiple morphological proxies for energetic condition (e.g., Warnock & Bishop, 1998), rarely are these variables directly compared. Multiple proxies should correlate with each other if they measure the same trait (energetic condition). Furthermore, there is still confusion about what trait some proxies actually measure. For example, a study conducted on two species of crickets showed that three estimates of energetic condition based on fat content or on the relationship between body mass and body length (scaled mass index or ordinary least squares regression) did not correlate with each other (Kelly et al., 2014). A similar lack of a relationship was found in flying animals such as birds (A. G. Gosler et al., 1995; Jacobs et al., 2012) and bats (McGuire et al., 2018) thus indicating cross-taxa support that morphological proxy measures do not always measure the same trait. This is an example of the jingle fallacy (Block, 1995; Carter et al., 2013), where a single trait label (“energetic condition”) actually encompasses more than one distinct trait. In this case, two investigations using different proxies can be conducted on the same research question, using the same species, but may end up with different results. This is problematic because inconsistency in results among researchers can result in potentially misleading interpretations of the impact of variation in morphology in relation to life history and population variables (Stevenson & Woods Jr, 2006).

Here we compare two indices (fat score and the scaled mass index) of an individual’s energetic state to validate whether they correlate with each other, which would indicate that they both measure energetic condition. Fat score, as described by Kaiser (1993), is a numerical estimate of the amount of fat visible under the skin (Fig. 1). The score ranges from 0 to 8 depending on the size and appearance of the fat located in the individual’s abdomen and interclavicular depression, with 0 indicating no visible fat and 8 indicating extensive fat covering the ventral surface such that no muscle tissue is visible. For example, a score of 1 corresponds to sparse traces of fat visible in the interclavicular depression and abdomen. This measure is frequently used in birds (Cornelius Ruhs et al., 2019; Erciyas et al., 2010; Merilä & Svensson, 1997), and is a straightforward, non-invasive method for estimating energetic condition. However, previous research found that it does not always positively relate with life history variables. For example, Haas (1998) found no difference between fat scores in individuals that had successful or failed nests in American robins and brown thrashers, indicating that fat score may not explain much of the variation in nest success in some species. Further research is needed to understand the relationship between fat score measures and life history characteristics.

In contrast, the scaled mass index (SMI) is more difficult to calculate than the fat score, but it has become the predominant ratio method for quantifying energetic condition within and among populations (Delciellos et al., 2018; English et al., 2018; Maceda-Veiga et al., 2014). The SMI is an individual’s mass scaled by skeletal body size (Peig & Green, 2009). Unlike the common alternative which uses a simple ratio of tarsus (lower leg) length to body mass, the SMI accounts for the tendency towards allometric scaling where the relationship between body mass and structural size increases by a power law (Huxley, 1932). When individuals with different structural body sizes can be standardized to the population average structural body size, then energetic condition (the amount of mass not explained by structural body size) can be more directly compared within and across populations. That is, the SMI calculates the energetic condition as the mass of an individual relative to the population by first computing the mass that the individual would have at the population average of a specific body measurement (e.g., tarsus length). Next, structural body size of the individual is standardized by scaling the individual’s structural body length by the population average of that body measurement, which accounts for population differences. The SMI is calculated as: \(Mass_i\left[ \frac{AvgLength_p}{Length_i} \right]^{slope_p}\) where \(Mass_i\) is each individual’s weight in grams, \(Length_i\) is the value of the chosen measure of structural body length for each bird, \(AvgLength_p\) is the average structural body length in the population, and \(slope_p\) is calculated from the standard major axis regression (which is used to compare variables that were both directly measured and thus have residual error) of a structural body size measure, like tarsus length on mass (Peig & Green, 2009), and is interpreted as the expected change in structural length for a one unit increase in mass. Therefore, individuals with superior energetic condition (larger weight for their structural body size) will have a higher SMI compared to individuals that with inferior energetic condition. Studies across taxa found that the SMI relates positively to reproductive success and survival. For example, mallards with a lower SMI had lower rates of survival compared to their higher SMI counterparts (Champagnon et al., 2012), while in crimson finches SMI was positively related to the number of young that survived to independence (Milenkaya et al., 2015).

Our research will determine whether these two indices of energetic condition measure the same trait, and whether this trait relates to an important life history characteristic: reproductive success. Measuring reproductive success in birds involves finding and monitoring nests (Mayfield, 1961). However, nests are usually built in cryptic locations and parents behave secretly (Gill, 1995), thus making it difficult to quantify the number of eggs and nestlings inside the nest over time. Additionally, it is difficult and time-consuming to track the survival of offspring once they leave the nest. Therefore, we will use the predominant method in this field for quantifying reproductive success: whether a nest fledged offspring (Mayfield, 1961).

Our study system is a population of great-tailed grackles (Quiscalus mexicanus), hereafter “grackles,” in Tempe, Arizona. This system is ideal for this investigation because grackles are native to the tropical climates of Central America (Johnson & Peer, 2001), but have rapidly expanded their geographic range into new areas (Wehtje, 2003). Because grackles are a water-associated species, the desert habitat of Tempe presents physiological challenges that could lead to an increased likelihood of a tradeoff between survival and reproductive attempts (Henderson et al., 2017). Deserts are characterized by a scarcity of water and extreme temperature fluctuations, which require behavioral and physiological adaptations (Costa, 2012). Wide variation in energetic condition and reproductive success is possible if grackle physiology requires more water than is present in the environment, and some individuals may cope with physiological stress, or find hidden sources of water, better than others (Henderson et al., 2017).

Hypotheses

We measured two morphological proxy variables of energetic condition and observed reproductive success in grackles to test two hypotheses. The first examined the relationship between two morphological proxies of energetic condition to validate that they measure the same inherent trait. Secondly, we hypothesized that energetic condition, as measured by either or both of the morphological proxy variables, would relate to reproductive success in male and female grackles.

METHODS

The methods below are based on the preregistration, with small changes summarized in the Deviations from the planned methods section and further explained in the preregistration (in italics).

Preregistration details

The preregistration used secondary data that were collected as part of other ongoing investigations (tarsus length in http://corinalogan.com/Preregistrations/g_flexgenes.html; tarsus length, body weight, number of fledglings, and whether a male holds a territory in http://corinalogan.com/Preregistrations/g_withinpop.html; and tarsus length in http://corinalogan.com/Preregistrations/g_expansion.html). The preregistration, containing the hypotheses, methods, and analysis plan, was written (July 2019) and submitted to Peer Community In Ecology for pre-study peer review (August 2019) before any analyses were conducted. We revised according to reviewer comments and received in principle acceptance by PCI Ecology of the version on 8 Nov 2019. After that, we conducted the analyses in the preregistration. Our final methods, results, and discussion, including all data and code, are listed below.

Summary of methods

Great-tailed grackles are caught year-round in the wild in Tempe, Arizona using a variety of methods (e.g., walk-in trap, bownet, mist net). After capture we immediately processed birds by attaching colored leg bands in unique combinations for individual identification, conducted morphological measurements of weight, tarsus length, flattened wing length, tail length, skull length, bill length and fat score (Fig. 1, the amount of visible fat under the skin in the clavicle and abdomen as in Kaiser, 1993). Most grackles were released after completion of color band marking, measurements, and acquiring a blood sample. A subset of grackles were held in aviaries for up to 6 months for behavioral testing, and then released back to the wild at their location of capture. We tested the repeatability of our structural size measures on this subset of individuals by measuring them again before release. The second measures were collected by the same experimenter in 11 out of 17 females and 10 out of the 18 males that were repeatedly sampled.

Figure 1: A male grackle showing the yellow/orange tint of fat under the skin in the intraclavicular depression (left); and a female grackle showing no fat under the skin of the intraclavicular region, but significant fat deposits under the skin of the abdomen (right).

From March - August, we monitor the behavior of all color-marked grackles to determine their nesting status. We follow females carrying nesting materials to find their nest. We determine whether the male territory owner is color-marked as well. Then we check each nest approximately every day to determine the status based on the female’s behavior (building, incubation, feeding nestlings, feeding fledglings, failed).

Individuals included in our sample were those for which we have measures of energetic condition when they were adults. We did not include individuals whose data were collected as juveniles. We also excluded data that was collected from the grackles when they were released from the aviaries to avoid any confounds due to their time in the aviary (e.g., perhaps unlimited nutritious food in the aviaries affected their fat score). However, to validate that our measures of structural body size (tarsus length or wing length) are precise and accurate, we measured twice the subset of grackles brought into aviaries - once when they were initially caught, and again up to 6 months later when we released them. We calculated the repeatability of these multiple measures. All other data included in this study came from wild-caught grackles (including the data from the birds that were brought into the aviaries on their first capture).

We first used logistic mixed-effect models to determine whether SMI and fat score are correlated. We also tested whether SMI and fat score varied by season because grackles are difficult to catch such that we were unable to structure our data collection to coincide with the breeding season and instead caught and measured grackles as often as possible. Previous research found a non-linear relationship between reproductive success and energetic condition variables (Milenkaya et al., 2015). To check whether this is occurring in our data, we visually examined our raw data to determine if we need to include a non-linear energetic condition independent variable into our models (i.e. FatScore²). Then we used we used two types of logistic mixed-effect models to determine the relationship between energetic condition and reproductive success. Both types are supported in the literature, but are slightly different in the way in which the link function is specified. First, we modeled the effect of energetic condition on reproductive success using a generalized linear mixed model framework with a logit link function (i.e. Milenkaya et al., 2015). We then also used a logistic exposure model that has a link function which accounts for the time interval between nest checks when estimating the probability of daily nest survival (Bolker, 2014; Shaffer, 2004).

After pre-study peer review: Deviations from the planned methods

1. We realized that the sexual dimorphism of male and female body sizes necessitates separate analyses. Therefore, we calculated SMI for males and females separately, and ran separate models for each sex for the repeatibility analysis (P1 and P2).

2. Fat score data were distributed such that the majority of scores were 0, with some 1’s and very few higher numbers. Specifically, of the 21 males, 15 had fat scores at 0, 5 scored 1, and a single male had a fat score of 2. Out of 47 females, 26 scored 0, 18 scored 1, 2 scored 2, and a single female scored 3. This lack of variance in the response variable led to problems when we ran the models: it was difficult to fit models using an ordinal regression. The function “simulateResiduals,” which we used to check our data, does not work with data in the ordinal family. Consequently, we modified the model to use a logistic regression where the dependent variable FatScore is categorized as individuals that showed no visible fat (y = 0), or some fat was present (y = 1) where we combined all individuals that had fat score values of 1 or greater. Subsequent data checking indicated that these data were not zero-inflated or overdispersed.

Deviations when testing hypothesis 1: correlation between SMI and Fat score

3. Warning messages occurred during the repeatability analysis using the “rptR” package in R (Stoffel et al., 2017) indicating that the fit was singular, likely because the variance for the Experimenter random effect in the model for both female and male wing length was 0.001. We thus conducted an unregistered analysis where we confirmed that our repeatability values from the repeatability models were valid, despite the warning, by hand calculating repeatability following Nakagawa & Schielzeth (2010). The hand-calculated repeatabilities were nearly identical (female R = 0.5, male R = 0.71) to the output from the rpt function.

4. Despite the data checking which indicated our model was not overdispersed or zero inflated, we could not get the fixed effects or random effect to converge using the Bayesian package in R “MCMCglmm.” We found no improvement in model fit by tweaking the priors or iterations/burnin/thin options. Therefore, we fit these models using the function glmer, a frequentist framework.

5. The Season variable only includes 2 males in the breeding season category, thus we do not have a large enough sample to produce reliable estimates. We removed the Season variable from the model for males.

Deviations when testing hypothesis 2: energetic condition and reproductive success

6. Only two females had reproductive success data from more than one year in our study (2019 and 2020). Consequently, there were very few repeated measures in this sample and our random effect of bird ID accounted for zero variance. This led to a warning that our model fit was singular. Therefore, we removed the data for these females for 2020 so we could remove ID as a random effect from the model, which resulted in the model running without warnings. We removed the 2020 data for these females because their energetic condition data was collected in 2019 and these measures were more likely to relate to their 2019 reproductive success data than to their reproductive success in 2020.

7. The fit of the model analyzing the relationship between energetic condition and male reproductive success (ability to hold a territory containing female nests) was singular. The Year random effect accounted for zero variance in the data, so we removed it. The fit was still singular, but we retained the ID random effect (although it also explained zero variance) to account for repeated measures in this sample.

8. The model fit was again singular in our logistic exposure model because the Year random effect explained zero variance in the data. We removed this random effect from the analysis.

RESULTS

Prediction 1: correlation between SMI and Fat Score

Table 1. Sample sizes for P1 and P2. The Breeding and Non-breeding season categories refer to the number of individuals measured in each season. The Reprod. success category represents the total number of individuals in each year observed engaging in breeding behaviors. Note that the 2019 and 2020 reproductive success sample sizes include some of the same individuals that were observed in both years. Whereas, the Prop. successful category represents the proportion of the total individuals observed engaging in breeding behaviors in each year that held a territory containing nests (males) or fledged young (females).

Category	Males	Females
Breeding Season Fat	2	12
Non-breeding fat	20	35
Breeding season SMI	6	24
Non-breeding SMI	18	38
Aviaries	16	9
Repro. success 2019	8	9
Repro. success 2020	17	13
Prop. successful 2019	0.63	0.22
Prop. successful 2020	0.47	0.54

Table 2. Results from the logistic mixed-effect regression for 47 females and fixed-effect regression for 21 males to determine whether fat score and scaled mass index (SMI) are correlated. Estimates are presented with the standard error in parentheses. Our sample size was too small to test for a season effect in males.

	Females		Males
Parameter	Estimate (SE)	p-value	Estimate (SE)	p-value
Intercept	-0.20 (0.74)	0.79	-0.82 (0.64)	0.21
SMI	0.07 (0.30)	0.81	0.46 (0.62)	0.46
Season	0.27 (0.71)	0.70	NA	NA

We were able to calculate SMI for 24 males and 62 females, and fat score values were available for 22 males and 47 females (Table 1).

We found that wing length was more tightly correlated with body mass than tarsus length in both sexes, therefore we used wing length in our SMI calculations (female n = 62, r = 0.26, p = 0.03; male n = 24, r = 0.35, p = 0.08). This allows us to account for as much variation in body mass as possible that is associated with skeletal body size because leftover variation in body mass is more likely to relate to energetic condition. Consequently, we used wing length in our calculation of SMI.

To validate that we were measuring structural body size consistently across experimenters, we analyzed the repeatability of wing length in the birds in our sample that were measured more than once. We found that average wing length was repeatable (n = 17 females, Repeatability ± standard error = 0.53 ± 0.18; n = 18 males, Repeatability ± SE = 0.75 ± 0.11). Data permutations and a likelihood ratio test both confirmed that these repeatability values were statistically significant at p < 0.01.

In females, we found that for every one unit increase in SMI, the bird is 1.3 times more likely to have some fat (a 30% increase in the odds of having fat), which is not a statistically significant relationship (female p = 0.81; Table 2). In males, a one unit increase in SMI corresponds to an odds ratio of 1.6, or a 60% increase in the odds of having some fat, which is also not a statistically significant relationship (p = 0.50; Table 2). Together, this indicates that SMI and fat score are not equally measuring energetic condition. There was also no relationship between season (breeding or non-breeding) and female fat score (p = 0.71). Only 2 males were measured during the breeding season, therefore we omitted season as an independent variable in the male model (Table 1).

Prediction 2: energetic condition and reproductive success

Our sample size for P2, where individuals had measures of reproductive success, SMI, and fat scores, was 20 for females and 20 for males.

To determine whether we should include any non-linear effects of SMI in our models (A. G. Gosler et al., 1995; Milenkaya et al., 2015), we visually evaluated whether individuals in any of 5 categories, ranging from low to high SMI, were more likely to be reproductively successful (Fig. 2). We found no visual evidence for a non-linear relationship between reproductive success and SMI for males or females (Fig. 3). Consequently, we did not include non-linear terms in subsequent models.

Figure 2: Frequency histogram of the SMI scores, illustrating the SMI categories, for the 33 males and 31 females for which we also had reproductive success data. The mean SMI value is indicated by a red vertical line. We created SMI category bins (indicated with vertical blue lines) in 1 standard deviation increments, centered on the mean. Category 3 indicates the SMI value is close to the population mean value. Categories 1 and 2 are individuals that have SMI scores that are low, and moderately low, respectively, compared to the population mean value. Similarly, categories 4 and 5 contain individuals that have SMI scores that are moderately high and high, respectively, compared to the population mean value.

Figure 3: The proportion of individuals that successfully fledged nests (females: left) or held a territory (males: right) in low (1), moderately low (2), moderate (3), moderately high (4) and high (5) scaled mass index (SMI) categories. Dots are sized according to the number (n) of individuals in that category. There is no evidence of a non-linear relationship.

We used linear models to determine whether season would be important to include in our models testing whether body condition relates to reproductive success. We found that SMI did not differ by season for females (Estimate (SE): \(\beta\) = -0.30 (0.26), p = 0.26) or males (\(\beta\) = -0.65 (0.43),p = 0.15). Similarly, fat score for females (\(\beta\) = 0.28 (0.68), p = 0.68) and males (\(\beta\) = 17.08 (2797.4), p = 0.99) did not differ by season (Fig. 4). Although we note that, as stated above and indicated in the standard error value, we lack sufficient fat score data from males in the breeding season so these results should be interpreted with caution. Consequently, we did not include season as an independent variable in our subsequent models testing the relationship between our body condition proxies and reproductive success.

Figure 4: Scaled mass index (SMI) was not significantly different between the breeding and non-breeding seasons for either sex.

Because fat score and SMI did not correlate, we included both as independent variables in our models testing prediction 2. For both males and females, we found no statistically significant relationships between either proxy of energetic condition and reproductive success (Table 3). Of note, the inconsistent direction of the effects for the parameter estimates further supports that SMI and fat score do not measure the same trait.

For females, our SMI parameter estimate of -0.92 (exponentiated to get the log odds = 0.40) indicates that a one unit increase in SMI corresponded to a 60% decrease in the odds a female would fledge an offspring (p = 0.13). Whereas an increase from no visible fat to showing some fat corresponded to a 16% increase in the odds a female would fledge an offspring (log odds = 1.16, p = 0.82). There was also no evidence of a significant relationship between the ability of a female to produce fledglings and having previously spent time in the aviaries (log odds = 0.25, p = 0.22), where the odds that a female would fledge an offspring were 75% lower if females spent time in the aviaries.

For males, there was also no statistically significant support for a relationship between whether a male defended a territory and SMI (log odds = 3.25, p = 0.13). Nevertheless, this relationship may be biologically important because a one unit increase in SMI corresponded to a more than 300% increase in the odds a male will hold a territory containing nests. Fat score was also statistically unrelated to male reproductive success where an increase from showing no visible fat to showing some fat corresponded to a 28% decrease in territory holding (log odds = 0.72, p = 0.76). Lastly, we found that those males who spent time in the aviaries were statistically less likely (97% decrease in the odds) to hold a territory compared with males who were never in the aviaries (log odds = 0.03, p = 0.02). However, we stress that our sample size was relatively small (20 males), and we did not have a balanced sample because there were no males that did not defend a territory and were never in the aviaries. Additionally, only five males had data from more than one breeding season, which resulted in our model fit being singular because the random effect for bird ID accounted for essentially zero variance. However, we kept ID in the model to account for the repeated samples.

Table 3. Results from the logistic regression for 20 females and 20 males to test whether reproductive success relates to condition. Estimates are presented with the standard error in parentheses.

	Females		Males
Parameter	Estimate (SE)	p-value	Estimate (SE)	p-value
Intercept	-0.02 (0.73)	0.98	3.05 (1.40)	0.03
FatScore	0.15 (1.02)	0.89	-0.33 (1.10)	0.77
SMI	-0.92 (0.61)	0.13	1.18 (0.78)	0.13
Aviary	-1.38 (1.14)	0.23	-3.62 (1.56)	0.02*

Prediction 2: energetic condition and probability of daily nest survival

Logistic regression analyses to determine reproductive success from nests discovered in different stages will be systematically biased (Shaffer, 2004). Nests discovered at a more progressed stage (i.e., nestling stage compared to building stage) are statistically more likely to succeed and nests with frequent and prolonged adult visits (such as those that occur when nests survive longer) are more likely to be discovered. Therefore, nests that fail early are less likely to be detected (Shaffer, 2004). Consequently, we analyzed female reproductive success using a logistic exposure model (Bolker, 2014), which uses survival analysis to determine the factors affecting the probability of daily nest survival, while accounting for incomplete nest observations.

We found that the probability of daily nest survival was significantly negatively related to SMI (log odds = 0.50, p = 0.03; Table 4), where, for every unit increase in SMI, the odds of daily nest survival decreased by half. This indicates that a female with a larger SMI (more mass for her structural body size) was less likely to have her nest survive each day (Fig. 5). There was no statistically significant relationship between the probability of daily nest survival and fat score (log odds = 2.48, p = 0.06), day of the year (log odds = 0.81, p = 0.16), or time spent in the aviaries (log odds = 0.63, p = 0.44, Table 4). Although not statistically significant, the effect size for the relationship between fat score and daily nest survival is large (Fig. 5) and potentially biologically meaningful. The odds of a nest surviving on a given day are almost 2.5 times greater (248%) for birds with some fat (a score of 1) compared to no fat (a score of 0).

Table 4. Results of the logistic exposure model showing the relationship between the probability of daily nest survival and scaled mass index (SMI), fat score, the amount of time spent in the aviaries, and the day of the year. Parameter estimates are presented with the standard error in parentheses. Odds ratios (OR) represent the exponentiated estimates, and are presented to increase interpretability with 95 percent confidence intervals in parentheses.

Parameter	Estimate (SE)	OR (CI)	p-value
Intercept	1.99 (0.40)	7.32 (3.3-16.0)	<0.001
Fat score	0.91 (0.49)	0.50 (0.27-0.92)	0.06
SMI	-0.69 (0.31)	2.48 (0.95-6.49)	0.03*
Day of year	-0.21 (0.15)	0.63 (0.19-2.10)	0.16
Aviary	-0.47 (0.61)	0.81 (0.60-1.10)	0.44

Figure 5: Odds ratios for independent variables affecting the probability of a nest surviving a given day. The dots and corresponding values represent the odds ratio values, and lines represent the confidence intervals around the odds ratio value. The vertical line at x = 1 delineates the odds ratio value for no relationship between the estimates and the probability of daily nest survival. The asterisk indicates an odds ratio value that is statistically significant.

DISCUSSION

Energetic condition is not directly observable, but variation can affect life history characteristics (Barnett et al., 2015; Labocha et al., 2014). Consequently, a large corpus of research attempts to measure energetic condition using various proxy measures (Labocha et al., 2014) and largely assumes that the chosen proxy accurately reflects energetic condition as a singular trait. Although it is often implicitly assumed that all proxy measures for energetic condition reflect the same inherent trait, it is rare for one study to compare multiple proxies. However, if all proxy measures are affected similarly by a singular energetic condition phenotype, then multiple proxy measures should produce correlated results. The aim of the current study was therefore to test the idea that multiple commonly used morphological proxies equally measure energetic condition (by correlating with each other), and that these measures can explain variation in reproductive success.

Here we found that two morphological proxies of energetic condition, fat score and SMI, did not correlate with each other in the great-tailed grackle, regardless of whether it was the breeding or non-breeding season. While both proxies are well supported in previous research as measures of energetic condition, our results indicate that they may not be measuring the same trait. This has also been found in studies on bats (McGuire et al., 2018), which are species that similarly experience distinct demands on body structure to facilitate flight. There are several potential reasons why grackle fat score and SMI did not correlate. First, it is possible that we were unable to accurately measure the amount of fat the birds actually stored. In addition to storing fat under their skin, birds may also store fat intraperitoneally (Musacchia, 1953), which would not have been detected with our fat score measure. Second, SMI and fat score may measure different components of energetic condition because variation in mass among grackles could be attributable to muscle or body water content, whereas fat score only accounts for subcutaneous fat (Labocha & Hayes, 2012). Research shows that stored fat is the primary source of energy in many taxa (Walsberg, 1988), especially in birds (Blem, 1990; Pond, 1981) because the energy per ounce from fat is much higher than from proteins or carbohydrates (Gessaman, 1999). However, because desert birds, such as the grackles in our investigation, have inconsistent access to water sources, variation in body water content may obscure variation in lipid content. Measuring muscle content often requires destructive methods [i.e. sacrificing the birds; Zhang et al. (2015)] or less objective assessments such as keel prominence or breast muscle shape (Abolins-Abols & Ketterson, 2017; A. Gosler, 1991), which was beyond the scope of the current research program. Third, it is possible that fat score and SMI did not correlate due to temporal variation at a fine scale that we were unable to capture. Although we found no evidence that SMI or fat score varied by season, there is evidence from other studies that avian mass changes with time of day (Nip et al., 2019) and stage of breeding (Milenkaya et al., 2013). It was logistically impossible in our project (and in many avian research programs) to capture birds multiple times within a season or at several times per day, therefore temporal variation in data collection could obscure the correlation between these two proxies, if such a correlation exists. However, the stage of breeding is unlikely to introduce additional variance to our study because we did not catch any females that were actively engaged in any stage of the breeding process. Finally, our sample sizes might have been too small to detect an effect, but the effect size for the relationship between fat score and SMI was essentially zero (0.001), therefore it is unlikely that a larger sample size would find a biologically informative relationship between these two proxies.

Energetic condition can have a large impact on reproductive success in birds (Drent & Daan, 1980; Montreuil-Spencer, 2017) and in flying mammals (Welbergen, 2011). For example, female chickadees with higher winter fat scores are more likely to lay eggs earlier in the subsequent breeding season, as well as go on to feed those offspring more frequently (Montreuil-Spencer, 2017). Energetic condition is likely a factor in reproductive success in our system because previous research in great-tailed grackles found that larger and heavier males were more likely to hold territories, have more social mates, and sire more offspring (Johnson et al., 2000). Our study additionally considered female morphology and reproductive success, subcutaneous fat, and controlled for the impact of structural body size on mass. However, we found reproductive success, measured as the ability to produce fledglings (females) or to hold a territory containing nests (males), did not significantly correlate with fat score or SMI. Although our results were not statistically significant, in some cases the parameter estimates revealed log-odds that may be large enough to be biologically significant. Notably, a one unit increase in SMI corresponded to a more than 300% increase in the odds a male will hold a territory containing nests, but a 60% decrease in the odds a female would fledge an offspring.

We additionally used logistic exposure models to determine whether the energetic condition of females related to the probability of daily nest survival. We only included females in this analysis because males were never observed contributing to nest building, incubation, or feeding nestlings in our population and so will not have a direct effect on daily nest survival. We found a negative relationship between female SMI and the likelihood of daily nest survival. This could be due to larger females actually carrying proportionally smaller energetic reserves than their smaller female counterparts (Jacobs et al., 2012), as seen in red-winged blackbirds (Langston et al., 1990). In some species, females with smaller body sizes are able to initiate breeding earlier because they can allocate more resources to reproduction compared to larger individuals that have higher bodily energy demands and therefore fewer excess energetic resources (Barbraud et al., 2000; Langston et al., 1990; Murphy, 1986). This indirectly affects reproductive success because nesting earlier increases the probability of nesting success and multiple nesting attempts (Johnson & Peer, 2001; Perrins, 1970). Yet, in our study we found no relationship between the probability of daily nest survival and day of the year, therefore this is unlikely to explain the negative relationship between SMI and nest survival. Alternatively, it is possible that larger females are unable to build a more concealed nest in the most dense vegetation, or that larger females are unable to build nests in delicate vegetation structure that is more likely to be inaccessible to predators. Moreover, the parameter estimate for the relationship between fat score and the daily probability of nest survival indicates that females with some visible fat are more than twice as likely to have a nest survive a given day. Because the direction of this effect is opposite to the relationship between SMI and nest survival, this is further evidence that these two proxies represent different traits.

Great-tailed grackles are an interesting system to study energetic condition and reproductive success because they recently expanded their range into Arizona, where the climate and habitat are distinct from that in Central America where the species originally evolved (Wehtje, 2003). The increase in temperature variation and decrease in available water at our desert study site are both environmental stressors that have previously been found to negatively affect energetic condition (Pendlebury et al., 2004). Although our study spanned only two years, our data are likely representative of reproductive success in this environment because the temperatures during our study were in line with those from the previous three years (National Climatic Data Center, 2020). Reproductive success is vital to species persistence and abundance in novel environments (Maspons et al., 2019). Therefore, an understanding of energetic condition and its relationship with reproductive success in grackles outside of their original range could broadly inform conservation research in invasive and non-native species. While reproductive success of certain avian species may be easier to monitor at a more fine scale (i.e. cavity nesters), the predominant measure of reproductive success currently used by avian ecologists is the ability of adults to fledge offspring (since foundational work by Mayfield, 1961) because it is financially and logistically accessible to more researchers. Therefore, we believe our measure of reproductive success in grackles is informative, and that research that spans taxa with diverse reproductive strategies is important for understanding general trends in energetic condition and the appropriate proxies.

The results of this study highlight the need to better understand proxy measures of energetic condition, not only in grackles, but for birds in general. Most studies on avian energetic condition only use one proxy variable, but because energetic condition is difficult to measure directly, it is important to compare multiple proxy variables to determine whether the proxy is measuring the intended trait (Block, 1995; Carter et al., 2013). If financially and logistically feasible, future research could measure total body composition and relative mass of fat using the relatively new and promising method of quantitative magnetic resonance (Guglielmo et al., 2011), or researchers could incorporate additional physiological methods to measure energetic condition, for example, blood hematocrit levels (Dawson & Bortolotti, 1997; but see Fair et al., 2007). Additionally, studying traits that could relate to variation in energy stores, such as dispersal (Ellers et al., 1998), migratory endurance (Deppe et al., 2015), or survival (Liao et al., 2011) would allow us to disentangle whether morphological proxies like fat score and SMI are poor proxy measures for energetic condition, or whether fat score and SMI do not affect reproductive success but may be associated with other life history characteristics. Because SMI can perform poorly in birds with low lipid mass, future research should also compare several mass by structural body size equations to determine the most appropriate proxy for a specific study system (Jacobs et al., 2012). Lastly, future research would benefit from using logistic exposure models to examine the relationship between energetic condition and reproductive success because these models control for the bias that arises when early nest failures are not detected, which is not possible in logistic regression models, and it is more sensitive to changes in a bird’s nest status (Shaffer, 2004).

DETAILED HYPOTHESES AND METHODS FROM THE PREREGISTRATION

HYPOTHESES

We measured two morphological proxy variables of energetic condition and observed reproductive success in grackles to test two hypotheses:

H1 - There is a relationship between two different morphological indices of energetic condition: fat score and the scaled mass index.

Prediction 1: Fat score and the scaled mass index will be positively correlated. This would indicate that these two indices measure the same trait, and it is likely they both are proxies for fat content.

Prediction 1 alternative 1: There is a negative correlation between fat score and the scaled mass index. This would indicate that there may be a tradeoff between the two indices where a larger value of the scaled mass index may measure muscle content rather than fat, and individuals with more muscle have less visible fat.

Prediction 1 alternative 2: There is no correlation between fat score and the scaled mass index. This indicates that these two variables do not measure the same trait. Fat score may not adequately capture a bird’s energetic condition because birds may be selected to only store the minimal fat necessary to prevent starvation, while also minimizing the weight gain that would make them easier targets for predators (Barnett et al., 2015). Similarly, the scaled mass index could be heavily influenced by body size, therefore reflecting structural size rather than fat storage (Labocha & Hayes, 2012).

H2 - Energetic condition (as measured by fat score and the scaled mass index) relates to reproductive success (measured as a binary variable of whether a female had one or more fledglings (1) or not (0), and whether a male defended a territory containing nests (1) or not (0)).

Prediction 2: Morphological indices of energetic condition (fat score and the scaled mass index) will correlate positively with reproductive success. This would indicate that individuals with more fat, and therefore higher energy reserves, are better able to acquire the resources necessary for reproduction.

Prediction 2 alternative 1: Morphological indices of energetic condition (fat score and the scaled mass index) will correlate negatively with reproductive success. This indicates that individuals may make trade offs, with some acquiring more food and increasing their energy reserves, and others prioritizing reproductive activities over increasing energy reserves.

Prediction 2 alternative 2: Morphological indices of energetic condition (fat score and the scaled mass index) do not correlate with reproductive success. This indicates that other, potentially non-morphological, individual characteristics relate to reproductive success (i.e., cognition, nest site selection, breeding experience, predator vigilance, etc.).

METHODS

The methods below are based on the preregistration, with small changes as described in the Deviations from the planned methods section above.

Planned Sample

Great-tailed grackles are caught in the wild in Tempe, Arizona using a variety of methods (e.g., walk-in trap, bownet, mist net). After capture we immediately process birds by attaching colored leg bands in unique combinations for individual identification, conducting morphological measurements of weight, tarsus length, flattened wing length, tail length, skull length, bill length and fat score (the amount of visible fat under the skin in the clavicle and abdomen as in Kaiser, 1993). Most grackles are released after completion of color band marking, measurements, and acquiring a blood sample. A subset of grackles are held in aviaries for up to 6 months for behavioral testing, and then released back to the wild at their location of capture.

From March - August, we monitor the behavior of all color-marked grackles to determine their nesting status. We follow females carrying nesting materials to find their nest. We determine whether the male territory owner is color-marked as well. Then we check each nest approximately every day to determine the status based on the female’s behavior (building, incubation, feeding nestlings, feeding fledglings, failed).

Individuals included in this sample will be those for which we have measures of energetic condition when they were adults. We will not include individuals whose data were collected as juveniles. As of 30 July 2019, we have fledgling data for 14 females that exhibited breeding behavior (5 had 1+ fledgling, 9 had no fledglings) and breeding territory status for 10 males (7 territory holders, 3 non-territory holders, 2 not observed so not part of this sample). Therefore, the minimum sample size for H2 will be 24. The minimum sample size for H1 will be 72, because that is how many marked individuals we have biometric data for so far. However, we expect to be able to add to the sample size for both H1 and H2 before the end of this investigation in Tempe, Arizona. UPDATE Oct 2020: In the second breeding season we had 20 females and 20 males with reproductive success and energetic condition data.

Sample size rationale

We will continue to color mark as many grackles as possible, and collect biometric data and fat scores. Our current sample of reproductive success is small because the grackles in Tempe nest in very tall palms, making it difficult to determine nest status. However, we plan to collect additional reproductive success data during the breeding season in summer 2020. UPDATE Oct 2020: In the second breeding season we had 20 females and 20 males with reproductive success and energetic condition data.

Data collection stopping rule

We will stop collecting data for this project in early August 2020 when research at the Tempe, Arizona field site will be finished.

Open materials

Biometric measurement protocol: https://gitlab.com/corinalogan/the-grackle-project/blob/master/protocolBiometrics.pdf

Nest check protocol: https://gitlab.com/corinalogan/the-grackle-project/blob/master/protocolNestCheck.pdf

Open data

All data (Berens et al., 2020) are available at https://knb.ecoinformatics.org/view/doi:10.5063/F1NZ862D and at github (the provided code will load these files directly from github).

Randomization and counterbalancing

There is no randomization or counterbalancing in this investigation.

Blinding of conditions during analysis

No blinding is involved in this investigation.

Dependent Variables

P1: correlation between fat and the scaled mass index

1. Fat score [the amount of visible fat under the skin in the clavicle and abdomen reported as a score from 0 (no fat) to 8 (fat completely covers muscles and underside of the bird); Kaiser (1993)] UPDATE Oct 2020: Fat score was heavily 0 skewed with few scores greater than one. To increase model fit we used a binomial response variable instead, where 0 is no fat and 1 is some fat observed undert the skin.

P2: energetic condition and reproductive success

1. Female had one or more fledglings (yes, no)

2. Male held a territory consisting of 1 to 3 clumped palms containing nests (yes, no)

Independent Variables

P1: correlation between fat and the scaled mass index

1. Scaled mass index using measures of body weight and tarsus length or flattened wing length (average of left and right as in Bleeker et al., 2005). We will choose the measure that is most correlated with body weight (Peig & Green, 2009).

2. Season (non-breeding [Sep-Feb], breeding [Mar-Aug]). UPDATE Oct 2020: The Season variable only includes 2 males in the breeding season category, thus we do not have a large enough sample to produce reliable estimates. We removed the Season variable from the model for males.

3. Random effect: Experimenter (because several different experimenters measure dependent variables on multiple different birds)

P2: energetic condition and reproductive success

1. Fat score

   • Note 1: if the fat score and the scaled mass index are positively correlated, then we will use only fat score in the model for P2. If they are not positively correlated, then we will add the scaled mass index as an independent variable in the P2 analysis
   • Note 2: if fat score and/or the scaled mass index vary by season (breeding or non-breeding), then we will only use the data from the breeding season to ensure that less time has elapsed between the collection of energetic condition and reproductive success variables

2. Temporarily held in aviaries for behavioral testing at any point during this study, because this may affect breeding behaviors (yes, no)

3. Random effect: Year (to determine whether conditions in a given breeding season similarly affected all grackle behavior and nest success)

4. Random effect: Bird ID (because there may be multiple measures of reproductive success for each bird)

ANALYSIS PLAN

UPDATE Oct 2020:

1) We realized that the sexual dimorphism of male and female body sizes necessitates separate analyses. Therefore, we calculated SMI for males and females separately, ran separate models for each sex for the repeatibility analysis, P1 and P2.

2) Fat score data were distributed such that the majority of scores were 0, with some 1’s and very few higher numbers. This made it difficult to fit models using an ordinal regression. The function simulateResiduals, which we used to check our data, does not work with data in the ordinal family. Consequently, we used logistic regression where the dependent variable FatScore represents no fat (score = 0), or some fat (score = 1)

3) Despite the data checking which indicated our model was not overdispersed or zero inflated, we could not get the fixed effects or random effect to converge using the Bayesian MCMCglmm. We found no improvement in model fit by tweaking the priors or iterations/burnin/thin options. Therefore, we fit these models using the function glmer, a frequentist framework.

4) The Season variable only includes 2 males in the breeding season category, thus we do not have a large enough sample to produce reliable estimates. We removed the Season variable from the model for males.

We will exclude data that was collected from the grackles when they were released from the aviaries to avoid any confounds due to their time in the aviary (e.g., perhaps unlimited nutritious food in the aviaries affected their fat score). However, to validate that our measures of structural body size (tarsus length or wing length) are precise and accurate, we will measure twice a subset of grackles brought into aviaries - once when they are initially caught, and again up to 6 months later when we release them. We will then calculate the repeatability of these multiple measures. All other data included in this study will come only from wild-caught grackles (including the birds that were brought into the aviaries on their first capture). When missing data occur, the existing data for that individual will be included in the analyses for which their data exist. Analyses will be conducted in R [current version 4.0.3; R Core Team (2017)].

Ability to detect actual effects

To begin to understand what kinds of effect sizes we will be able to detect given our sample size limitations, we used G*Power Faul et al. (2009) to conduct power analyses based on confidence intervals. G*Power uses pre-set drop down menus and we chose the options that were as close to our analysis methods as possible (listed in each analysis below). Note that there were no explicit options for GLMMs, thus the power analyses are only an approximation of the kinds of effect sizes we can detect. We realize that these power analyses are not fully aligned with our study design and that these kinds of analyses are not appropriate for Bayesian statistics (e.g., our MCMCglmm below), however we are unaware of better options at this time. Additionally, it is difficult to run power analyses because it is unclear what kinds of effect sizes we should expect due to the lack of data on this species for these particular research questions.

Data checking

The data will be checked for overdispersion, underdispersion, zero-inflation, and heteroscedasticity with the DHARMa R package (Hartig, 2019) following methods by Hartig.

P1 analysis: correlation between fat and the scaled mass index

We will calculate the scaled mass index as described by Peig & Green (2009) using either tarsus or flattened wing length - whichever measure is most correlated with body weight (Peig & Green, 2009).

We use a Generalized Linear Mixed Model (GLMM; MCMCglmm function, MCMCglmm package; (Hadfield 2010)) with an ordinal distribution (for categorical variables in MCMCglmm) and probit link using 130,000 iterations with a thinning interval of 10, a burnin of 30,000, and minimal priors (V=1, nu=0) (Hadfield, 2014). We will ensure the GLMM shows acceptable convergence [lag time autocorrelation values <0.01; Hadfield (2010)], and adjust parameters if necessary to meet this criterion. We will determine whether an independent variable had an effect or not using the Estimate in the full model.

Where we have multiple measures of tarsus or flattened wing length, we will check that our measurements are repeatable using the rptR package (Stoffel et al., 2017).

To roughly estimate our ability to detect actual effects (because these power analyses are designed for frequentist statistics, not Bayesian statistics), we ran a power analysis in G*Power with the following settings: test family=F tests, statistical test=linear multiple regression: Fixed model (R^2 deviation from zero), type of power analysis=a priori, alpha error probability=0.05. We changed the power and the effect size until we reached an output that we project our sample size will be (n=90). The number of predictor variables was restricted to only the fixed effects because this test was not designed for mixed models. The protocol of the power analysis is here:

Input:

Effect size f² = 0.15

α err prob = 0.05

Power (1-β err prob) = 0.86

Number of predictors = 3

Output:

Noncentrality parameter λ = 13.3500000

Critical F = 2.7119214

Numerator df = 3

Denominator df = 85

Total sample size = 89

Actual power = 0.8635760

This means that, with a sample size of 89, we would have an 86% chance of detecting a medium effect (approximated at f²=0.15 by Cohen, 1988).

code shown in .rmd

P2 analysis: energetic condition and reproductive success

To model the effect of energetic condition on reproductive success, we will use two types of logistic mixed-effect models. Both types are supported in the literature, but are slightly different in the way in which the link function is specified. First, we will model reproductive success using a generalized linear mixed model framework with a logit link function (i.e. Milenkaya et al., 2015). We will also use a logistic exposure model that has a link function which accounts for the time interval between nest checks when estimating the probability of daily nest survival (Bolker, 2014; Shaffer, 2004). If fat score and the scaled mass index are positively correlated in P1, then we will use only fat score as the independent variable in this GLMM. If they are not positively correlated, we will include both as independent variables.

Previous research found a non-linear relationship between reproductive success and energetic condition variables (Milenkaya et al., 2015). To check whether this is occurring in our data, we will first plot our raw data to determine if we need to include a non-linear energetic condition independent variable into our model (i.e. FatScore²). Our dependent variable is binary, so to more clearly see the trends in the data, on the x-axis we will bin our energetic condition scores into 5 categories based on standard deviations (sd) around the mean (low = < 2 sd, moderately low = -2 sd to -1 sd, moderate = -1 sd to +1 sd, moderately high = +1 sd to +2 sd, high = > 2 sd). Then on the y-axis we will use the proportion of individuals in each category that had successful nests. UPDATE Oct 2020: Because most individuals fell within the medium category when we grouped data using 1 standard deviation around the mean, we switched to using half standard deviation increments around the mean.

A power analysis was conducted as above for P1 and the protocol reported here:

Input:

Effect size f² = 0.15

α err prob = 0.05

Power (1-β err prob) = 0.90

Number of predictors = 2

Output:

Noncentrality parameter λ = 13.2000000

Critical F = 3.1038387

Numerator df = 2

Denominator df = 85

Total sample size = 88

Actual power = 0.9020264

This means that, with a sample size of 88, we would have a 90% chance of detecting a medium effect (approximated at f²=0.15 by Cohen, 1988).

code shown in .rmd

Do energetic condition variables vary by season?

code shown in .rmd

Does energetic condition relate to reproductive success?

code shown in .rmd

Does female energetic condition relate to the probability of daily nest survival?

Our measure of female nest success could be systematically biased against nests that failed early (Shaffer, 2004). Consequently, we also analyzed female reproductive success using a logistic exposure model. This type of model determines the factors affecting daily nest survival probability.

code shown in .rmd

ETHICS

This research is carried out in accordance with permits from the:

1. US Fish and Wildlife Service (scientific collecting permit number MB76700A-0,1,2)
2. US Geological Survey Bird Banding Laboratory (federal bird banding permit number 23872)
3. Arizona Game and Fish Department (scientific collecting license number SP594338 [2017], SP606267 [2018], and SP639866 [2019])
4. Institutional Animal Care and Use Committee at Arizona State University (protocol number 17-1594R)

AUTHOR CONTRIBUTIONS

Berens: Hypothesis development, data collection, revising/editing.

Logan: Study design, write up, revising/editing, materials/funding.

Folsom: Data collection, revising/editing.

Sevchik Data collection, revising/editing.

Bergeron: Data collection, revising/editing.

McCune: Hypothesis development, data collection, data analysis, write up, revising/editing.

FUNDING

This research is funded by the Department of Human Behavior, Ecology and Culture at the Max Planck Institute for Evolutionary Anthropology.

ACKNOWLEDGEMENTS

We thank Aaron Blackwell and Ken Kosik for being the UCSB sponsors of the Cooperation Agreement with the Max Planck Institute for Evolutionary Anthropology; and our research assistants for help with trapping the grackles and collecting the biometric and nest/territory data: Aelin Mayer, Nancy Rodriguez, Brianna Thomas, Aldora Messinger, Elysia Mamola, Michael Guillen, Rita Barakat, Adriana Boderash, Olateju Ojekunle, August Sevchik, Justin Huynh, Amanda Overholt, and Michael Pickett.

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