Estimation of breeding probability can make monitoring data more revealing: a case study of amphibians

Building pondscapes for amphibian metapopulations

The success of ponds constructed to restore ecological infrastructure for pond-breeding amphibians and benefit aquatic biodiversity depends on where and how they are built. We studied effects of pond and landscape characteristics, including connectivity, on metapopulation dynamics of 12 amphibian species in Switzerland. To understand the determinants of long-term occupancy (here summarized as incidence), environmental effects on both colonization and persistence should be considered. We fitted dynamic occupancy models to 20 years of monitoring data on a pond construction program to quantify effects of pond and landscape characteristics and different connectivity metrics on colonization and persistence probabilities in constructed ponds. Connectivity to existing populations explained dynamics better than structural connectivity metrics, and simple metrics (distance to the nearest neighbor population, population density) were useful surrogates for dispersal kernel-weighted metrics commonly used in metapopulation theory. Population connectivity mediated the persistence of conservation target species in new ponds, suggesting source-sink dynamics in newly established populations. Population density captured this effect well and could be used by practitioners for site selection. Ponds created where there were 2-4 occupied ponds within a radius of ∼0.5 km had >3.5 times higher incidence of target species (median) than isolated ponds. Species had individual preferences regarding pond characteristics, but breeding sites with larger (≥100 m2) total water surface area, that temporarily dried, and that were in surroundings with maximally 50% forest benefitted multiple target species. Pond diversity will foster amphibian diversity at the landscape scale.

Aquatic Soundscape Recordings Reveal Diverse Vocalizations and Nocturnal Activity of an Endangered Frog

AbstractAutonomous sensors provide opportunities to observe organisms across spatial and temporal scales that humans cannot directly observe. By processing large data streams from autonomous sensors with deep learning methods, researchers can make novel and important natural history discoveries. In this study, we combine automated acoustic monitoring with deep learning models to observe breeding-associated activity in the endangered Sierra Nevada yellow-legged frog (Rana sierrae), a behavior that current surveys do not measure. By deploying inexpensive hydrophones and developing a deep learning model to recognize breeding-associated vocalizations, we discover three undocumented R. sierrae vocalization types and find an unexpected temporal pattern of nocturnal breeding-associated vocal activity. This study exemplifies how the combination of autonomous sensor data and deep learning can shed new light on species' natural history, especially during times or in locations where human observation is limited or impossible.

Where Land and Water Meet: Making Amphibian Breeding Sites Attractive for Amphibians

The protection of wetlands is a cornerstone in the conservation of pond-breeding amphibians. Because protected wetlands are rarely natural areas, but are often man-made, at least in Europe, it is important that they are well managed to fulfill their intended function. Appropriate management requires knowledge of the ecology of the species, particularly habitat requirements. Here, we combine species monitoring data and habitat mapping data in an analysis where our goal was to describe the factors that determine the occupancy of amphibian species in federally protected amphibian breeding sites. As expected, every species had its own habitat requirements, often a combination of both a terrestrial and aquatic habitat (i.e., landscape complementation). In most species, occupancy was strongly positively affected with the amount of aquatic habitat, but predicted occupancy probabilities were low because the amount of aquatic habitat was low in most sites. The area or proportion of ruderal vegetation also had positive effects on multiple species, while other types of terrestrial habitat (e.g., meadows) led to low occupancy probabilities. The total area of the protected breeding sites was never included in a final model and connectivity was important only for one species (Triturus cristatus). The latter finding implies that the quality of the landscape between breeding sizes is more important than distance per se, while the former implies that the area of some specific habitats within breeding sites is crucial for high occupancies. Thus, increasing the amount of aquatic habitats and likewise terrestrial habitats within protected areas would make them more likely to achieve their conservation objectives. Our study is an example of how the joint analysis of monitoring data and habitat data (based on mapping in the field) can lead to evidence-based suggestions on how to improve conservation practice.

Estimation of breeding probability can make monitoring data more revealing: a case study of amphibians

Monitoring programs serve to detect trends in the distribution and abundance of species. To do so, monitoring programs often use static state variables. Dynamic state variables that describe population dynamics might be more valuable because they allow for a mechanistic understanding of the processes that lead to population trends. We fit multistate occupancy models to data from a country-wide multispecies amphibian occupancy monitoring program and estimated occupancy and breeding probabilities. If breeding probabilities are determinants of occupancy dynamics, then they may serve in monitoring programs as state variables that describe dynamic processes. The results showed that breeding probabilities were low and that a large proportion of the populations had to be considered to be non-breeding populations (i.e., populations where adults are present but no breeding occurs). For some species, the majority of populations were non-breeding populations. We found that non-breeding populations have lower persistence probabilities than populations where breeding occurs. Breeding probabilities may thus explain trends in occupancy but they might also explain other ecological phenomena, such as the success of invasive species, which had high breeding probabilities. Signs of breeding, i.e., the presence of eggs and larvae, were often hard to detect. Importantly, non-breeding populations also had low detection probabilities, perhaps because they had lower abundances. We suggest that monitoring programs should invest more in the detection of life history stages indicative of breeding, and also into the detection of non-breeding populations. We conclude that breeding probability should be used as a state variable in monitoring programs because it can lead to deeper insights into the processes driving occupancy dynamics.