Deep learning analysis of nest camera video recordings reveals temperature-sensitive incubation behavior in the purple martin (Progne subis)

Systematic review of avian hatching failure and implications for conservation

Avian hatching failure is a widespread phenomenon, affecting around 10% of all eggs that are laid and not lost to predation, damage, or desertion. Our understanding of hatching failure is limited in terms of both its underpinning mechanisms and its occurrence across different populations. It is widely acknowledged that rates of hatching failure are higher in threatened species and in populations maintained in captivity compared to wild, non-threatened species, but these differences have rarely been quantified and any broader patterns remain unexplored. To examine the associations between threat status, management interventions, and hatching failure across populations we conducted a phylogenetically controlled multilevel meta-analysis across 231 studies and 241 species of birds. Our data set included both threatened (Critically Endangered, Endangered, and Vulnerable) and non-threatened (Near Threatened and Least Concern) species across wild and captive populations, as well as 'wild managed' ('free-living') populations. We found the mean overall rate of hatching failure across all populations to be 16.79%, with the hatching failure rate of wild, non-threatened species being 12.40%. We found that populations of threatened species experienced significantly higher mean hatching failure than populations of non-threatened species. Different levels of management were also associated with different rates of hatching failure, with wild populations experiencing the lowest rate of hatching failure, followed by wild managed populations, and populations in captivity experiencing the highest rate. Similarly, populations that were subject to the specific management interventions of artificial incubation, supplementary feeding, and artificial nest provision displayed significantly higher rates of hatching failure than populations without these interventions. The driver of this correlation between hatching failure and management remains unclear, but could be an indirect result of threatened species being more likely to have lower hatching success and also being more likely to be subject to management, indicating that conservation efforts are fittingly being focused towards the species potentially most at risk from extinction. This is the most comprehensive comparative analysis of avian hatching failure that has been conducted to date, and the first to quantify explicitly how threat status and management are associated with the rate of hatching failure in a population. We discuss the implications of our results, focusing on their potential applications to conservation. Although we identified several factors clearly associated with variation in hatching failure, a significant amount of heterogeneity was not explained by our meta-analytical model, indicating that other factors influencing hatching failure were not included here. We discuss what these factors might be and suggest avenues for further research. Finally, we discuss the inconsistency in how hatching failure is defined and reported within the literature, and propose a standardised definition to be used in future studies which will enable better comparison across populations and ensure that the most accurate information is used to support management decisions.

The importance of population contextual data for large-scale biomonitoring using an apex predator: The Tawny Owl (Strix aluco)

Top predators are often used as sentinel species in contaminant monitoring due to their exposure and vulnerability to persistent, bioaccumulative and, in some cases, biomagnificable contaminants. Some of their ecological traits can vary in space and time, and are known to influence the contamination levels and therefore information on ecological traits should be used as contextual data for correct interpretation of large-scale contaminant spatial patterns. These traits can explain spatiotemporal variation in contaminant exposure (traits such as diet and dispersal distances) or contaminant impacts (traits such as population trend and clutch size). The aim of our research was to review the spatial variation in selected contextual parameters in the Tawny Owl (Strix aluco), a species identified by the COST Action European Raptor Biomonitoring Facility as one of the most suitable candidates for pan-European biomonitoring. A considerable variation in availability of published and unpublished contextual data across Europe was found, with diet being the most extensively studied trait. We demonstrate that the Tawny Owl is a suitable biomonitor at local scale but also that taking spatial variation of other contextual data (e.g. diet) into account is necessary. We found spatial gaps in knowledge about the species ecology and biology in Southern Europe, along with gaps in certain population parameters (e.g. population trends) in several countries. Based on our findings, we proposed a minimal recommended scheme for monitoring of population contextual data as one of the first steps towards a pan-European monitoring scheme using the Tawny Owl.

Support for a relationship between demography and modeled habitat suitability is scale dependent for the purple martin Progne subis

Species distribution models (SDMs) estimate habitat suitability for species in geographic space. They are extensively used in conservation under the assumption that there is a positive relationship between habitat suitability and species success and stability. Given the difficulties in obtaining demographic data across a species' range, this assumption is rarely tested. Here we provide a range-wide test of this relationship for the eastern subspecies of purple martin Progne subis subis. We build a well-supported SDM for the breeding range of the purple martin, and pair it with an unparalleled demographic dataset of nest success and local and regional abundance data for the species to test the proposed link between habitat suitability and fecundity and demography. We find a positive relationship between regional abundance and habitat suitability but no relationship between local abundance or fecundity and habitat suitability. Our data suggest that local success is driven largely by biotic and stochastic factors and raise the possibility that purple martins are experiencing a time lag in their distribution. More broadly our results call for caution in how we interpret SDMs and do not support the assumption that areas of high habitat suitability are the best areas for species persistence.

Applications of machine learning in behavioral ecology: Quantifying avian incubation behavior and nest conditions in relation to environmental temperature

In the age of machine learning, building programs that take advantage of the speed and specificity of algorithm development can greatly aid efforts to quantify and interpret changes in animal behavior in response to abiotic environmental factors, like temperature. For both endotherms and ectotherms, temperature can affect everything from daily energy budgets to nesting behaviors. For instance, in birds environmental temperature plays a key role in shaping parental incubation behavior and temperatures experienced by embryos. Recent research indicates that temperatures experienced by embryos affect viability and are important in shaping fitness-related traits in young birds, sparking renewed interest in relationships among environmental factors, parental incubation behavior, and incubation temperature. Incubation behavior of birds can be monitored non-invasively by placing thermal probes into the nest and analyzing temperature fluctuations that occur as parents attend and leave the nest (on- and off-bouts, respectively). When other measures of temperature (e.g., ambient air or operative temperature) are collected simultaneously with incubation temperature it is possible to compare shifts in behavior with environmental changes. To improve analysis of incubation behavior using these large thermal data sets we developed a program, NestIQ, that uses machine learning to guide parameter optimization allowing it to track the behavior of diverse species. NestIQ's algorithm was tested using six species incubating in lab or field scenarios, that exhibit unique incubation patterns. This stand-alone and open source software is operated through a graphical user interface (i.e., no user programming is required) that provides important behavioral and thermal output statistics. Further, measures of environmental temperature can be imported alongside nest temperature into the program, which then reports various attributes of environmental temperature during shifts in parental behavior. This program will improve the ability of avian ecologists to interpret a critical parental care behavior that can be used across diverse incubation scenarios and species. Although specifically designed for quantifying avian incubation, NestIQ has the potential for broader applications, including basking and nesting behaviors of non-avian reptiles in relation to ambient temperature.