Frank Pennekamp

The ecological forecast horizon, and examples of its uses and determinants

Forecasts of ecological dynamics in changing environments are increasingly important, and are available for a plethora of variables, such as species abundance and distribution, community structure and ecosystem processes. There is, however, a general absence of knowledge about how far into the future, or other dimensions (space, temperature, phylogenetic distance), useful ecological forecasts can be made, and about how features of ecological systems relate to these distances. The ecological forecast horizon is the dimensional distance for which useful forecasts can be made. Five case studies illustrate the influence of various sources of uncertainty (e.g. parameter uncertainty, environmental variation, demographic stochasticity and evolution), level of ecological organisation (e.g. population or community), and organismal properties (e.g. body size or number of trophic links) on temporal, spatial and phylogenetic forecast horizons. Insights from these case studies demonstrate that the ecological forecast horizon is a flexible and powerful tool for researching and communicating ecological predictability. It also has potential for motivating and guiding agenda setting for ecological forecasting research and development.

Big answers from small worlds: a user's guide for protist microcosms as a model system in ecology and evolution

Laboratory microcosm experiments using protists as model organisms have a long tradition and are widely used to investigate general concepts in population biology, community ecology and evolutionary biology. Many variables of interest are measured in order to study processes and patterns at different spatiotemporal scales and across all levels of biological organization. This includes measurements of body size, mobility or abundance, in order to understand population dynamics, dispersal behaviour and ecosystem processes. Also, a variety of manipulations are employed, such as temperature changes or varying connectivity in spatial microcosm networks. Past studies, however, have used varying methods for maintenance, measurement, and manipulation, which hinders across-study comparisons and meta-analyses, and the added value they bring. Furthermore, application of techniques such as flow cytometry, image and video analyses, and in situ environmental probes provide novel and improved opportunities to quantify variables of interest at unprecedented precision and temporal resolution. Here, we take the first step towards a standardization of well-established and novel methods and techniques within the field of protist microcosm experiments. We provide a comprehensive overview of maintenance, measurement and manipulation methods. An extensive supplement contains detailed protocols of all methods, and these protocols also exist in a community updateable online repository. We envision that such a synthesis and standardization of methods will overcome shortcomings and challenges faced by past studies and also promote activities such as meta-analyses and distributed experiments conducted simultaneously across many different laboratories at a global scale.

Implementing image analysis in laboratory‐based experimental systems for ecology and evolution: a hands‐on guide

1. Experimental laboratory systems (ELS) are widely applied research tools to test theoretical predictions in ecology and evolution. Combining ELS with automated image analysis could significantly boost information acquisition due to the ease at which abundance and morphological data is collected. Despite the advantages of image analysis, the technology has not been fully adopted yet, presumably due to the difficulties of technical implementation. 2. The tools needed to integrate image analysis in ELS are nowadays readily available: digital camera equipment is purchased at limited costs and free software solutions which allow sophisticated image processing and analysis exist. Here, we give a concise description how to integrate these pieces into a largely automated image analysis workflow. We provide researchers with necessary background information on the principles of image analysis,explaining how to standardize image acquisition and how to validate the results to reduce bias. 3. Three cross-platform and open-source software solutions for image analysis are compared: ImageJ, the EB-Image package in R, and Python with the SciPy/scikit image libraries. The relative strengths and limitations of each solution are compared and discussed. In addition, a set of test images and three scripts are provided in the Online Supplementary Material to illustrate the use of image analysis and help biologists to implement image analysis in their own systems. 4. To demonstrate the reliability and versatility of a validated image analysis workflow, we introduce our own Tetrahymena thermophila ELS. Then, examples from evolutionary ecology are provided showing the advantages of image analysis to study different ecological questions, aiming at both the population and individual level. 5. Experimental laboratory systems that integrate the advantages of image analysis extend their application and versatility compared with regular ELS. Such improvements are necessary to understand complex processes such as eco-evolutionary feedbacks, community dynamics and individual behaviour in ELS.

Dispersal propensity in Tetrahymena thermophila ciliates - a reaction norm perspective.

Dispersal and phenotypic plasticity are two main ways for species to deal with rapid changes of their environments. Understanding how genotypes (G), environments (E), and their interaction (genotype and environment; G × E) each affects dispersal propensity is therefore instrumental for predicting the ecological and evolutionary responses of species under global change. Here we used an actively dispersing ciliate to quantify the contributions of G, E, and G × E on dispersal propensity, exposing 44 different genotypes to three different environmental contexts (densities in isogenotype populations). Moreover, we assessed the condition dependence of dispersal, that is, whether dispersal is related to morphological, physiological, or behavioral traits. We found that genotypes showed marked differences in dispersal propensity and that dispersal is plastically adjusted to density, with the overall trend for genotypes to exhibit negative density‐dependent dispersal. A small, but significant G × E interaction indicates genetic variability in plasticity and therefore some potential for dispersal plasticity to evolve. We also show evidence consistent with condition‐dependent dispersal suggesting that genotypes also vary in how individual condition is linked to dispersal under different environmental contexts thereby generating complex dispersal behavior due to only three variables (genes, environment, and individual condition).

BEMOVI, software for extracting behavior and morphology from videos, illustrated with analyses of microbes.

Microbes are critical components of ecosystems and provide vital services (e.g., photosynthesis, decomposition, nutrient recycling). From the diverse roles microbes play in natural ecosystems, high levels of functional diversity result. Quantifying this diversity is challenging, because it is weakly associated with morphological differentiation. In addition, the small size of microbes hinders morphological and behavioral measurements at the individual level, as well as interactions between individuals. Advances in microbial community genetics and genomics, flow cytometry and digital analysis of still images are promising approaches. They miss out, however, on a very important aspect of populations and communities: the behavior of individuals. Video analysis complements these methods by providing in addition to abundance and trait measurements, detailed behavioral information, capturing dynamic processes such as movement, and hence has the potential to describe the interactions between individuals. We introduce BEMOVI, a package using the R and ImageJ software, to extract abundance, morphology, and movement data for tens to thousands of individuals in a video. Through a set of functions BEMOVI identifies individuals present in a video, reconstructs their movement trajectories through space and time, and merges this information into a single database. BEMOVI is a modular set of functions, which can be customized to allow for peculiarities of the videos to be analyzed, in terms of organisms features (e.g., morphology or movement) and how they can be distinguished from the background. We illustrate the validity and accuracy of the method with an example on experimental multispecies communities of aquatic protists. We show high correspondence between manual and automatic counts and illustrate how simultaneous time series of abundance, morphology, and behavior are obtained from BEMOVI. We further demonstrate how the trait data can be used with machine learning to automatically classify individuals into species and that information on movement behavior improves the predictive ability.

Habitat requirements and dispersal ability of the Spanish Fritillary (Euphydryas desfontainii) in southern Portugal: Evidence-based conservation suggestions for an endangered taxon

A high level of plant and insect diversity, and more specifically high butterfly diversity characterizes the Mediterranean Basin. However, alarming negative trends have been reported for butterfly populations in that region emphasizing the urgent need to better understand the drivers of their population declines. Habitat specialists of grasslands are strongly affected, mainly by land use change and climate change. Thorough assessments of habitat requirements and dispersal abilities are crucial to establish appropriate conservation measures to counter these threats. Here, we investigate the ecological requirements and dispersal ability of Euphydryas desfontainii, one of Portugal’s rarest butterflies, to develop targeted conservation strategies. The assessment of habitat requirements showed differences between occupied and unoccupied patches in terms of host plant abundance and area. Mark–release–recapture data were used to model demographic parameters: survival rates decreased linearly over the flight period and recruitment followed a parabolic curve with separate peaks for males and females. The movement data were fitted to an inverse power function and used to predict the probability of long-distance dispersal. The obtained probabilities were compared to related checkerspot butterflies and interpreted regarding the structural connectivity of the investigated habitat network. We suggest focusing on the preservation of remaining habitat patches, whilst monitoring and safeguarding that their vegetation structure does provide sufficiently diversified microclimates in order to best conserve E.desfontainii populations.

The larval ecology of the butterfly Euphydryas desfontainii (Lepidoptera: Nymphalidae) in SW-Portugal: food plant quantity and quality as main predictors of habitat quality

Corresponding to theory, the persistence of metapopulations in fragmented landscapes depends on the area of suitable habitat patches and their degree of isolation, mediating the individual exchange between habitats. More recently, habitat quality has been highlighted as being equally important. We therefore assess the role of habitat area, isolation and quality for the occupancy of larval stages of the regionally threatened butterfly Euphydryas desfontainii occurring in grassland habitats comprising the host plant Dipsascus comosus. We put a special focus on habitat quality which was determined on two spatial scales: the landscape (among patches) and the within-patch level. On the landscape level, occupancy of caterpillars was determined by a presence-absence analysis at 28 host plant patches. On the within-patch level, oviposition site selection was studied by comparing 159 host plants with egg clutches to a random sample of 253 unoccupied host plants within six habitat patches. The occupancy of caterpillars and presence of egg clutches on host plants was then related to several predictors such as patch size and isolation on the landscape level and host plant characteristics and immediate surroundings on the within patch level. On the landscape level, only host plant abundance was related to the presence of caterpillars, while size and isolation did not differ between occupied and unoccupied patches. However, the weak discrimination of larval stages among patches changed on the within-patch level: here, several microclimatic predictors such as sunshine hours and topography, host plant morphology and phenology as well as further potential host plants in the immediate surroundings of the plant chosen for oviposition strongly determined the presence of egg clutches. We strongly suggest promoting the presence of the host plant in topographically and structurally rich habitat patches to offer potential for microclimatic compensation for a species considered threatened by climate change.

Characterizing change points and continuous transitions in movement behaviours using wavelet decomposition

Summary Individual behaviour, that is, the reaction of an organism to internal state, conspecifics and individuals of other species as well as the environment, is a crucial building block of their ecology. Modern tracking techniques produce high-frequency observations of spatial positions of animals and accompanying speed and tortuosity measurements. However, inferring behavioural modes from movement trajectories remains a challenge. Changes in behavioural modes occur at different temporal and spatial scales and may take two forms: abrupt, representing distinct change points; or continuous, representing smooth transitions between movement modes. The multi-scale nature of these behavioural changes necessitates development of methods that can pinpoint behavioural states across spatial and temporal scales. We propose a novel segmentation method based on the discrete wavelet transform (DWT), where the movement signal is decomposed into low-frequency approximation and high-frequency detail sub-bands to screen for behavioural changes at multiple scales. Approximation sub-bands characterizes broad changes by taking the continuous variations between behavioural modes into account, whereas detail sub-bands are employed to detect abrupt, finer scale change points. We tested the ability of our method to identify behavioural modes in simulated trajectories by comparing it to three state-of-the-art methods from the literature. We further validated the method using an annotated dataset of turkey vultures (Cathartes aura) relating extracted segments to the expert knowledge of migratory vs. non-migratory patterns. Our results show that the proposed DWT segmentation is more versatile than other segmentation methods, as it can be applied to different movement parameters, performs better or equally well on the simulated data, and correctly identifies behavioural modes identified by the experts. It is hence a valuable addition to the toolbox of land managers and conservation practitioners to understand the behavioural patterns expressed by animals in natural and human-dominated landscapes.

Developing and Integrating Advanced Movement Features Improves Automated Classification of Ciliate Species

Recent advances in tracking technologies such as GPS or video tracking systems describe the movement paths of individuals in unprecedented details and are increasingly used in different fields, including ecology. However, extracting information from raw movement data requires advanced analysis techniques, for instance to infer behaviors expressed during a certain period of the recorded trajectory, or gender or species identity in case data is obtained from remote tracking. In this paper, we address how different movement features affect the ability to automatically classify the species identity, using a dataset of unicellular microbes (i.e., ciliates). Previously, morphological attributes and simple movement metrics, such as speed, were used for classifying ciliate species. Here, we demonstrate that adding advanced movement features, in particular such based on discrete wavelet transform, to morphological features can improve classification. These results may have practical applications in automated monitoring of waste water facilities as well as environmental monitoring of aquatic systems.

Dynamic species classification of microorganisms across time, abiotic and biotic environments—A sliding window approach

The development of video-based monitoring methods allows for rapid, dynamic and accurate monitoring of individuals or communities, compared to slower traditional methods, with far reaching ecological and evolutionary applications. Large amounts of data are generated using video-based methods, which can be effectively processed using machine learning (ML) algorithms into meaningful ecological information. ML uses user defined classes (e.g. species), derived from a subset (i.e. training data) of video-observed quantitative features (e.g. phenotypic variation), to infer classes in subsequent observations. However, phenotypic variation often changes due to environmental conditions, which may lead to poor classification, if environmentally induced variation in phenotypes is not accounted for. Here we describe a framework for classifying species under changing environmental conditions based on the random forest classification. A sliding window approach was developed that restricts temporal and environmentally conditions to improve the classification. We tested our approach by applying the classification framework to experimental data. The experiment used a set of six ciliate species to monitor changes in community structure and behavior over hundreds of generations, in dozens of species combinations and across a temperature gradient. Differences in biotic and abiotic conditions caused simplistic classification approaches to be unsuccessful. In contrast, the sliding window approach allowed classification to be highly successful, as phenotypic differences driven by environmental change, could be captured by the classifier. Importantly, classification using the random forest algorithm showed comparable success when validated against traditional, slower, manual identification. Our framework allows for reliable classification in dynamic environments, and may help to improve strategies for long-term monitoring of species in changing environments. Our classification pipeline can be applied in fields assessing species community dynamics, such as eco-toxicology, ecology and evolutionary ecology.