Summer is here in London, which means it’s time for our July issue! Featuring methods for documenting predator hunting behaviour, identifying plant species, monitoring small nocturnal mammal species & many more!
Read on to discover our featured articles, specially selected by Senior Editor Bob O’Hara, plus find out more about the Applications and Practical Tools articles we have in this issue.
Protein quantification in ecological studies Protein quantification is a routine procedure in ecological studies, despite the inherent limitations of well-acknowledged protein determination methods which have been largely overlooked by ecologists. Thus, Zaguri et al. wanted to bridge this knowledge gap in order to improve the way ecologists quantify proteins and interpret findings. They surveyed the ecological literature to determine how and why ecologists quantify proteins, and to determine whether different quantification methods produce comparable results across taxa.
The Purr-fect Catch Characterising variation in predator behaviour and, specifically, quantifying kill rates is fundamental for parameterising predator–prey and food web models. Yet, current methods for recording kill rates of free-ranging predators, particularly those that consume small-bodied prey, present a number of associated challenges. Here, Studd et al. deployed custom-adapted acoustic recorders and tri-axial accelerometers on free-ranging Canada lynx to assess the capacity of biologging devices to continuously document individual hunting behaviour, including prey selection and kill rates, on a predator that specialises on small prey.
Instrumental Variable Methods in Structural Equation Models Instrumental variable regression (RegIV) provides a means for detecting and correcting parameter bias in causal models, but little attention has been paid to the fact that IV methods can also be implemented within structural equation models (SEMIV). In this review article, James B. Grace presents the motivations, requirements and basic procedures for using SEMIV.
Device effects in animal tracking studies The use of bio-logging devices to track animal movement continues to grow as technological advances and device miniaturisation allow researchers to study animal behaviour in unprecedented detail. Balanced against the remarkable data that bio-loggers can provide is a need to understand the impact of devices on animal behaviour and welfare. One aspect lacking in many studies is assessment of the statistical power of tests of device effects. Here, Cleasby et al. address this issue by providing an overview of the statistical power, as well as the Type M (magnitude) and Type S (sign) error rate, of tests of device effects within the avian tracking literature across a range of assumed effect sizes.
classecol (Open Access) Human perceptions of nature, once the domain of the social sciences, are now an important part of environmental research. However, the data and tools to tackle this research are lacking or difficult to apply. Here, Johnson et al. present a collection of text classifier models to identify text relevant to the broad topics of hunting and nature, describing whether opinions are pro- or against-hunting, or show interest, concern or dislike of nature. These classifiers, alongside an array of other text processing and analysis functions, are presented in the form of an R package classecol.
Flora Incognita (Open Access) Being able to identify plant species is an important factor for understanding biodiversity and its change due to natural and anthropogenic drivers. Here, Mäder et al. present Flora Incognita, a free app for Android, iOS and Harmony OS devices that allows users to interactively identify plant species and capture their observations. Specifically developed deep learning algorithms, trained on an extensive repository of plant observations, classify plant images with yet unprecedented accuracy. By using this technology in a context-adaptive and interactive identification process, users are now able to reliably identify plants regardless of their botanical knowledge level.
A motion-detection based camera trap for small nocturnal mammals (free access) Camera traps are useful for monitoring wildlife populations, but traps may not always trigger when targeting small, nocturnal species. Motion-detection techniques have advantages over time-lapse and heat-triggered traps, but need to be deployed to maximize signal-to-noise ratio. As part of a study of flying squirrels (Glaucomys) in urban environments, Klemens et al. developed motion-detecting camera traps using a raspberry pi microcomputer and camera and a 940 nm IR illuminator on a tree-mounted wooden platform. The traps performed much better than commercial camera traps, with a low latency and the data collected having a high signal-to-noise ratio.
Low cost, long-term monitoring of forest canopy dynamics In-situ, high-frequency and long-term monitoring of forest
canopy development is vital for improved understanding in many fields of ecology. Although there are well-established commercial instruments measuring light interception and digital hemispherical photography methods, the equipment cost and lack of field robustness make these instruments not suitable for long-term unattended measurements. Here, Wilkinson et al. present a low-cost Raspberry Pi camera system that is robust and suitable for the acquisition of long-term, high-frequency forest canopy images. The low cost of each camera means multiple systems could be used to provide the required spatial sampling. They also provide an open-source R script for post-processing these images to provide a semi-automated processing chain, easily adaptable for use at other forest sites.
The Snake on the Cover
This issue’s cover image shows an Asian green pitviper (Trimeresurus sabahi) hanging from a tree branch in the Malaysian rainforest. The picture succeeds in capturing the topographical complexity of the scaly surface of snake skin. In their article, Martinez et al. examine the diversity and ecological correlates of skin surface topography in snakes to showcase the applicability of their newly developed software, called QuSTo. The application allows users to quantify surface topography from profiles obtained from 2D and 3D scans. QuSTo is free, open-source, user-friendly and easily adaptable for specific analysis requirements. Moreover, it is compatible with 3D data obtained with different scanning techniques and can be used to examine the surface properties of virtually any animal and plant species for both fundamental and applied biological and bioinspired research. Photo credit: ©Roberto García-Roa.