callsync: An R package for alignment and analysis of multi-microphone animal recordings.

To better understand how vocalisations are used during interactions of multiple individuals, studies are increasingly deploying on-board devices with a microphone on each animal. The resulting recordings are extremely challenging to analyse, since microphone clocks drift non-linearly and record the vocalisations of non-focal individuals as well as noise. Here we address this issue with callsync, an R package designed to align recordings, detect and assign vocalisations to the caller, trace the fundamental frequency, filter out noise and perform basic analysis on the resulting clips. We present a case study where the pipeline is used on a dataset of six captive cockatiels (Nymphicus hollandicus) wearing backpack microphones. Recordings initially had a drift of ~2 min, but were aligned to within ~2 s with our package. Using callsync, we detected and assigned 2101 calls across three multi-hour recording sessions. Two had loud beep markers in the background designed to help the manual alignment process. One contained no obvious markers, in order to demonstrate that markers were not necessary to obtain optimal alignment. We then used a function that traces the fundamental frequency and applied spectrographic cross correlation to show a possible analytical pipeline where vocal similarity is visually assessed. The callsync package can be used to go from raw recordings to a clean dataset of features. The package is designed to be modular and allows users to replace functions as they wish. We also discuss the challenges that might be faced in each step and how the available literature can provide alternatives for each step.

Multilevel Bayesian analysis of monk parakeet contact calls shows dialects between European cities.

Geographic differences in vocalizations provide strong evidence for animal culture, with patterns likely arising from generations of social learning and transmission. Most studies on the evolution of avian vocal variation have predominantly focused on fixed repertoire, territorial song in passerine birds. The study of vocal communication in open-ended learners and in contexts where vocalizations serve other functions is therefore necessary for a more comprehensive understanding of vocal dialect evolution. Parrots are open-ended vocal production learners that use vocalizations for social contact and coordination. Geographic variation in parrot vocalizations typically take the form of either distinct regional variations known as dialects or graded variation based on geographic distance known as clinal variation. In this study, we recorded monk parakeets (Myiopsitta monachus) across multiple spatial scales (i.e., parks and cities) in their European invasive range. We then compared calls using a multilevel Bayesian model and sensitivity analysis, with this novel approach allowing us to explicitly compare vocalizations at multiple spatial scales. We found support for founder effects and/or cultural drift at the city level, consistent with passive cultural processes leading to large-scale dialect differences. We did not find a strong signal for dialect or clinal differences between parks within cities, suggesting that birds did not actively converge on a group level signal, as expected under the group membership hypothesis. We demonstrate the robustness of our findings and offer an explanation that unifies the results of prior monk parakeet vocalization studies.

Evidence for vocal signatures and voice-prints in a wild parrot.

In humans, identity is partly encoded in a voice-print that is carried across multiple vocalizations. Other species also signal vocal identity in calls, such as shown in the contact call of parrots. However, it remains unclear to what extent other call types in parrots are individually distinct, and whether there is an analogous voice-print across calls. Here we test if an individual signature is present in other call types, how stable this signature is, and if parrots exhibit voice-prints across call types. We recorded 5599 vocalizations from 229 individually marked monk parakeets (Myiopsitta monachus) over a 2-year period in Barcelona, Spain. We examined five distinct call types, finding evidence for an individual signature in three. We further show that in the contact call, while birds are individually distinct, the calls are more variable than previously assumed, changing over short time scales (seconds to minutes). Finally, we provide evidence for voice-prints across multiple call types, with a discriminant function being able to predict caller identity across call types. This suggests that monk parakeets may be able to use vocal cues to recognize conspecifics, even across vocalization types and without necessarily needing active vocal signatures of identity.

Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide.

As global wildlife populations continue to decline, the health and sustainability of ex situ populations in zoos and aquariums have become increasingly important. However, the majority of managed ex situ populations are not meeting sustainability criteria and are not viable in the long term. Historically, ex situ flamingo (Phoenicopteriformes) populations have shown low rates of reproductive success and improvements are needed for long-term viability. Both flock size and environmental suitability have previously been shown to be important determinants of ex situ flamingo reproductive success in a limited number of sites in some species. Here we combined current and historic globally shared zoological records for four of the six extant species of flamingo (Phoeniconaias minor, Phoenicopterus chilensis, Phoenicopterus roseus, and Phoenicopterus ruber) to analyze how flock size, structure, and climatic variables have influenced reproductive success in ex situ flamingo populations at 540 zoological institutions from 1990 to 2019. Flock size had a strong nonlinear relationship with reproductive success for all species, with flock sizes of 41-100 birds necessary to achieve ca. 50% probability of reproduction. Additionally, an even sex ratio and the introduction of new individuals to a flock both increased ex situ reproductive success in some cases, while climatic variables played a limited role. We demonstrate the conservation management potential from globally shared zoological data and provide species-specific management recommendations to increase the reproductive success of global ex situ flamingo populations: minimum flock sizes should be increased, and we encourage greater collaboration between individual institutions and regional associations in exchanging birds between flocks.

ANIMAL-SPOT enables animal-independent signal detection and classification using deep learning.

Bioacoustic research spans a wide range of biological questions and applications, relying on identification of target species or smaller acoustic units, such as distinct call types. However, manually identifying the signal of interest is time-intensive, error-prone, and becomes unfeasible with large data volumes. Therefore, machine-driven algorithms are increasingly applied to various bioacoustic signal identification challenges. Nevertheless, biologists still have major difficulties trying to transfer existing animal- and/or scenario-related machine learning approaches to their specific animal datasets and scientific questions. This study presents an animal-independent, open-source deep learning framework, along with a detailed user guide. Three signal identification tasks, commonly encountered in bioacoustics research, were investigated: (1) target signal vs. background noise detection, (2) species classification, and (3) call type categorization. ANIMAL-SPOT successfully segmented human-annotated target signals in data volumes representing 10 distinct animal species and 1 additional genus, resulting in a mean test accuracy of 97.9%, together with an average area under the ROC curve (AUC) of 95.9%, when predicting on unseen recordings. Moreover, an average segmentation accuracy and F1-score of 95.4% was achieved on the publicly available BirdVox-Full-Night data corpus. In addition, multi-class species and call type classification resulted in 96.6% and 92.7% accuracy on unseen test data, as well as 95.2% and 88.4% regarding previous animal-specific machine-based detection excerpts. Furthermore, an Unweighted Average Recall (UAR) of 89.3% outperformed the multi-species classification baseline system of the ComParE 2021 Primate Sub-Challenge. Besides animal independence, ANIMAL-SPOT does not rely on expert knowledge or special computing resources, thereby making deep-learning-based bioacoustic signal identification accessible to a broad audience.

Memory for own actions in parrots.

The ability to recall one's past actions is a crucial prerequisite for mental self-representation and episodic memory. We studied whether blue-throated macaws, a social macaw species, can remember their previous actions. The parrots were trained to repeat four previously learned actions upon command. Test sessions included repeat trials, double repeat trials and trials without repeat intermixed to test if the parrots repeated correctly, only when requested and not relying on a representation of the last behavioral command. Following their success, the parrots also received sessions with increasing time delays preceding the repeat command and successfully mastered 12-15 s delays. The parrots successfully transferred the repeat command spontaneously at first trial to three newly trained behaviors they had never repeated before, and also succeeded in a second trial intermixed with already trained actions (untrained repeat tests). This corroborates that successful repeating is not just an artifact of intense training but that blue-throated macaws can transfer the abstract "repeat rule" to untrained action. It also implies that an important aspect of self-representation has evolved in this avian group and might be adaptive, which is consistent with the complex socio-ecological environment of parrots and previous demonstrations of their complex cognition.

Using relative brain size as predictor variable: Serious pitfalls and solutions.

There is a long-standing interest in the effect of relative brain size on other life history variables in a comparative context. Historically, residuals have been used to calculate these effects, but more recently it has been recognized that regression on residuals is not good practice. Instead, absolute brain size and body size are included in a multiple regression, with the idea that this controls for allometry. I use a simple simulation to illustrate how a case in which brain size is a response variable differs from a case in which relative brain size is a predictor variable. I use the simulated data to test which modeling approach can estimate the underlying causal effects for each case. The results show that a multiple regression model with both body size and another variable as predictor variable and brain size as response variable work well. However, if relative brain size is a predictor variable, a multiple regression fails to correctly estimate the effect of body size. I propose the use of structural equation models to simultaneously estimate relative brain size and its effect on the third variable and discuss other potential methods.

Coevolution of relative brain size and life expectancy in parrots.

Previous studies have demonstrated a correlation between longevity and brain size in a variety of taxa. Little research has been devoted to understanding this link in parrots; yet parrots are well-known for both their exceptionally long lives and cognitive complexity. We employed a large-scale comparative analysis that investigated the influence of brain size and life-history variables on longevity in parrots. Specifically, we addressed two hypotheses for evolutionary drivers of longevity: the cognitivebuffer hypothesis, which proposes that increased cognitive abilities enable longer lifespans, and the expensive brain hypothesis, which holds that increases in lifespan are caused by prolonged developmental time of, and increased parental investment in, large-brained offspring. We estimated life expectancy from detailed zoo records for 133 818 individuals across 244 parrot species. Using a principled Bayesian approach that addresses data uncertainty and imputation of missing values, we found a consistent correlation between relative brain size and life expectancy in parrots. This correlation was best explained by a direct effect of relative brain size. Notably, we found no effects of developmental time, clutch size or age at first reproduction. Our results suggest that selection for enhanced cognitive abilities in parrots has in turn promoted longer lifespans.

Social network architecture and the tempo of cumulative cultural evolution.

The ability to build upon previous knowledge-cumulative cultural evolution-is a hallmark of human societies. While cumulative cultural evolution depends on the interaction between social systems, cognition and the environment, there is increasing evidence that cumulative cultural evolution is facilitated by larger and more structured societies. However, such effects may be interlinked with patterns of social wiring, thus the relative importance of social network architecture as an additional factor shaping cumulative cultural evolution remains unclear. By simulating innovation and diffusion of cultural traits in populations with stereotyped social structures, we disentangle the relative contributions of network architecture from those of population size and connectivity. We demonstrate that while more structured networks, such as those found in multilevel societies, can promote the recombination of cultural traits into high-value products, they also hinder spread and make products more likely to go extinct. We find that transmission mechanisms are therefore critical in determining the outcomes of cumulative cultural evolution. Our results highlight the complex interaction between population size, structure and transmission mechanisms, with important implications for future research.