Biology subjects

Steinfartz, S.

Publications and source records attributed to Steinfartz, S..

3 recordsLinked to original sources

bioRxiv2025

Experimental insights on the behaviour and development of Near Eastern Fire salamanders (Salamandra infraimmaculata) indicate general strategies to cope with aquatic predators

Fishes are key predators of amphibian larvae with often devastating consequences for their abundance and survival. In Israel, the Near Eastern Fire salamander (Salamandra infraimmaculata) reaches the southern limit of the geographic distribution of the whole genus and has to cope with xeric terrestrial habitats- and a scarcity of suitable sites for the deposition of larvae with permanent springs and streams are being rare. One such location is the Tel Dan National Park in the North of Israel, were, surprisingly salamander larvae and fishes coexist successfully in a system of larger and smaller streams. In three experiments we tested different anti-predator behaviour with salamanders from Tel Dan and other locations. We analysed whether gravid females avoid the presence of fish when depositing their larvae; if the presence of fish impacts larval development and if salamander larvae show active hiding behaviour when confronted with fish cues. While females from Tel Dan showed a bet-hedging strategy in site selection irrespective of fish presence, larval developmental rate was reduced in the presence of fish probably due to a lower feeding activity. Lastly, larvae from different sites displayed hiding behaviour in the presence of fish, irrespective whether they naturally coexist with fish or not. We infer from our results and existing studies that S. infraimmaculata has general rather than site-specific adaptations to cope with the presence of fish. We conclude that the general adaptations are essential to sustain this species in a range of extreme habitats at the southern limit of the s distribution.

animal behavior and cognition↗

bioRxiv2024

Iguanas from above: Citizen scientists provide reliable counts of endangered Galapagos marine iguanas from drone imagery

Population surveys are vital for wildlife management, yet traditional methods often demand excessive time and resources, leading to data gaps for many species. Modern technologies such as drones can facilitate field surveys but may also increase data analysis challenges. Citizen Science (CS) can address this issue by engaging non-specialists for data collection and analysis. We evaluated CS for population monitoring using the endangered Galapagos marine iguana as a case study, assessing online volunteers ability to detect and count animals in aerial images. Comparing against a Gold Standard dataset of expert counts in 4345 images, we explored optimal aggregation methods from CS inputs, considering image quality and filtering data from infrequent and anonymous participants. During three phases of our project -- hosted on the Zooniverse platform -- over 13,000 volunteers made 1,375,201 classifications from 57,838 aerial images; each being independently classified 20 (phases 1 & 2) or 30 (phase 3) times. Volunteers achieved 68% to 94% accuracy in detecting iguanas, with more false negatives than false positives. Image quality strongly influenced accuracy; by excluding data from suboptimal pilot-phase images, volunteers counted with 90% to 92% of accuracy. For detecting presence or absence of iguanas, the commonly used majority vote aggregation approach (where the answer selected is that given by the majority of individual inputs) produced less accurate results than when a minimum threshold of five (from the 20/30 independent classifications) was used. For counting iguanas, HDBSCAN clustering yielded the best results. Excluding inputs from anonymous and inexperienced volunteers decreased accuracy. We conclude that online volunteers can accurately identify and count marine iguanas from drone images, though a tendency to underestimate warrants further consideration. CS-based data analysis is faster than manual counting but still resource-intensive, underscoring the need to develop a Machine Learning approach.

zoology↗

bioRxiv2023

Life on the edge: a new toolbox for population-level climate change vulnerability assessments

O_LIGlobal change is impacting biodiversity across all habitats on earth. New selection pressures from changing climatic conditions and other anthropogenic activities are creating heterogeneous ecological and evolutionary responses across many species geographic ranges. Yet we currently lack standardised and reproducible tools to effectively predict the resulting patterns in species vulnerability to declines or range changes. C_LIO_LIWe developed an informatic toolbox that integrates ecological, environmental and genomic data and analyses (environmental dissimilarity, species distribution models, landscape connectivity, neutral and adaptive genetic diversity and genotype-environment associations) to estimate population vulnerability. In our toolbox, functions and data structures are coded in a standardised way so that it is applicable to any species or geographic region where appropriate data are available, for example individual or population sampling and genomic datasets (e.g. RAD-seq, ddRAD-seq, whole genome sequencing data) representing environmental variation across the species geographic range. C_LIO_LIWe apply our toolbox to a georeferenced genomic dataset for the East African spiny reed frog (Afrixalus fornasini) to predict population vulnerability, as well as demonstrating that range loss projections based on adaptive variation can be accurately reproduced using data for two European bat species (Myotis escalerai, and M. crypticus). C_LIO_LIOur framework sets the stage for large scale, multi-species genomic datasets to be leveraged in a novel climate change vulnerability framework to quantify intraspecific differences in genetic diversity, local adaptation, range shifts and population vulnerability based on exposure, sensitivity, and range shift potential. C_LI

evolutionary biology↗