No abstract

Biodiversity conservation requires reliable species assessments and rigorously designed surveys. However, determining the survey effort required to reliably detect population change can be challenging for rare, cryptic and elusive species. We used a tropical bromeliad-dwelling frog as a model system to explore a cost-effective sampling design that optimizes the chances of detecting a population decline. Relatively few sampling visits were needed to estimate occupancy and detectability with good precision, and to detect a 30% change in occupancy with 80% power. Detectability was influenced by observer expertise, which therefore also had an effect on the sampling design – less experienced observers require more sampling visits to detect the species. Even when the sampling design provides precise parameter estimates, only moderate to large changes in occupancy will be detected with reliable power. Detecting a population change of 15% or less requires a large number of sites to be surveyed, which might be unachievable for range-restricted species occurring at relatively few sites. Unless there is high initial occupancy, rare and cryptic species will be particularly challenging when it comes to detecting small population changes. This may be a particular issue for long-term monitoring of amphibians which often display low detectability and wide natural fluctuations.

Although the number of studies discerning the impact of climate change on ecological systems continues to increase, there has been relatively little sharing of the lessons learnt when accumulating this evidence. At a recent workshop entitled ‘Using climate data in ecological research’ held at the UK Met Office, ecologists and climate scientists came together to discuss the robust analysis of climate data in ecology. The discussions identified three common pitfalls encountered by ecologists: 1) selection of inappropriate spatial resolutions for analysis; 2) improper use of publically available data or code; and 3) insufficient representation of the uncertainties behind the adopted approach. Here, we discuss how these pitfalls can be avoided, before suggesting ways that both ecology and climate science can move forward. Our main recommendation is that ecologists and climate scientists collaborate more closely, on grant proposals and scientific publications, and informally through online media and workshops. More sharing of data and code (e.g. via online repositories), lessons and guidance would help to reconcile differing approaches to the robust handling of data. We call on ecologists to think critically about which aspects of the climate are relevant to their study system, and to acknowledge and actively explore uncertainty in all types of climate data. And we call on climate scientists to make simple estimates of uncertainty available to the wider research community. Through steps such as these, we will improve our ability to robustly attribute observed ecological changes to climate or other factors, while providing the sort of influential, comprehensive analyses that efforts to mitigate and adapt to climate change so urgently require

Microhabitat use is an important component of anuran behavior in both the tadpole and the adult stages. It is potentially influenced by phylogeny and extant ecological factors acting as selective pressures, such as predation, competition, or physical habitat properties. We aimed to test whether patterns of microhabitat use vary among species, habitats and sites, and how much of this variation can be explained by phylogenetic relatedness. We collected data on microhabitat use at five different sites, where we obtained a total of 4,230 records of individual tadpoles of 34 species in 15 genera and 7 families, and a total of 1,163 records of adult individuals of 39 species in 16 genera and 8 families. Mantel tests conducted to relate species dissimilarities in microhabitat use and phylogenetic relatedness indicated a weak but significant relationship for adult anurans, and no relationship for tadpoles. Our results suggest that microhabitat use is a plastic and variable trait, overcoming phylogenetic signal in tadpoles. In adult anurans, very little of the variation in microhabitat use can be explained by phylogenetic relatedness. Microhabitat use is not a good predictor of phylogeny, but it may be a very interesting subject to study natural selection and adaptation.

Surveys of rare or cryptic species may miss individuals or populations that are actually present. Despite the increasing use of environmental DNA (eDNA) analysis to survey species in ponds, rivers, and lakes, very few studies have attempted to use eDNA for the detection of species using very small water bodies such as those accumulated within plants. Our aim was to investigate the feasibility of an eDNA sampling method for detecting Crossodactylodes itambe, an endemic bromeliad-dwelling frog from a remote location in Brazil. We collected water samples from 19 bromeliads for which we had observational data from direct visual surveys. We compared occupancy estimated from direct observations with the results from quantitative real-time PCR based eDNA assays. For observational surveys, we used a single season occupancy model. We applied a novel Bayesian occupancy model to estimate occupancy from eDNA samples, as well as false positives and false negatives at different stages of the workflow. eDNA from bromeliad tanks provided reliable estimates, with very low error levels and improved detection when compared to detectability from direct observation. Estimated occupancies using eDNA and visual survey methods were similar. The method is feasible for species restricted to small water bodies and exposed to direct UV radiation, and particularly useful to survey remote locations and confirm species presence. eDNA analysis provides a viable alternative to destructive sampling of bromeliads or direct observation methods that require logistically challenging repeated observations. Therefore, eDNA methods may be widely applicable to sampling programmes of other amphibians that live in plants.