Ethologists assemble - macrobehaviour needs you

The difficulty of integrating multiple theories, data and methods has slowed progress towards making unified inferences of ecological change generalizable across large spatial, temporal and taxonomic scales. However, recent progress towards a theoretical synthesis now provides a guiding framework for organizing and integrating all primary data and methods for spatiotemporal assemblage‐level inference in ecology. In this paper, we describe how recent theoretical developments can provide an organizing paradigm for linking advances in data collection and methodological frameworks across disparate ecological sub‐disciplines and across large spatial and temporal scales. First, we summarize the set of fundamental processes that determine change in multispecies assemblages across spatial and temporal scales by reviewing recent theoretical syntheses of community ecology. Second, we review recent advances in data and methods across the main sub‐disciplines concerned with ecological inference across large spatial, temporal and taxonomic scales, and organize them based on the primary fundamental processes they include, rather than the spatiotemporal scale of their inferences. Finally, we highlight how iteratively focusing on only one fundamental process at a time, but combining all relevant spatiotemporal data and methods, may reduce the conceptual challenges to integration among ecological sub‐disciplines. Moreover, we discuss a number of avenues for decreasing the practical barriers to integration among data and methods. We aim to reconcile the recent convergence of decades of thinking in community ecology and macroecology theory with the rapid progress in spatiotemporal approaches for assemblage‐level inference, at a time where a robust understanding of spatiotemporal change in ecological assemblages is more crucial than ever to conserve biodiversity.

It is difficult to obtain detailed information on the context of physical activity at large geographic scales, such as the entire United States, as well as over long periods of time, such as over years. MapMyFitness is a suite of interactive tools for individuals to track their workouts online or using global positioning system in their phones or other wireless trackers. This method article discusses the use of physical activity data tracked using MapMyFitness to examine patterns over space and time. An overview of MapMyFitness, including data tracked, user information, and geographic scope, is explored. We illustrate the utility of MapMyFitness data using tracked physical activity by users in Winston-Salem, NC, USA between 2006 and 2013. Types of physical activities tracked are described, as well as the percent of activities occurring in parks. Strengths of MapMyFitness data include objective data collection, low participant burden, extensive geographic scale, and longitudinal series. Limitations include generalizability, behavioral change as the result of technology use, and potential ethical considerations. MapMyFitness is a powerful tool to investigate patterns of physical activity across large geographic and temporal scales.

Biodiversity faces unprecedented threats from rapid global change1. Signals of biodiversity change come from time-series abundance datasets for thousands of species over large geographic and temporal scales. Analyses of these biodiversity datasets have pointed to varied trends in abundance, including increases and decreases. However, these analyses have not fully accounted for spatial, temporal and phylogenetic structures in the data. Here, using a new statistical framework, we show across ten high-profile biodiversity datasets2–11 that increases and decreases under existing approaches vanish once spatial, temporal and phylogenetic structures are accounted for. This is a consequence of existing approaches severely underestimating trend uncertainty and sometimes misestimating the trend direction. Under our revised average abundance trends that appropriately recognize uncertainty, we failed to observe a single increasing or decreasing trend at 95% credible intervals in our ten datasets. This emphasizes how little is known about biodiversity change across vast spatial and taxonomic scales. Despite this uncertainty at vast scales, we reveal improved local-scale prediction accuracy by accounting for spatial, temporal and phylogenetic structures. Improved prediction offers hope of estimating biodiversity change at policy-relevant scales, guiding adaptive conservation responses.

Understanding the responses of organisms to environmental change is critical to tackling the grand challenges of 21st century biology. Fields such as ecophysiology and ecology have embraced these challenges and ‘re-invented’ themselves in part by shifting the scale of scientific enquiry and utilising large-scale comparative approaches. Behavioural research has not yet realised this potential to the same extent. In this paper, we argue that adopting a trait-based approach at large spatial, temporal and taxonomic scales can advance the field of behavioural ecology and address emerging questions in biology. We surveyed the literature in relevant ecology and behaviour journals between 1981 and 2020 and found that ecological journals have changed markedly over time, specifically in their focus on understanding interspecific trait variation at broad taxonomic, spatial and temporal scales. This pattern is not apparent for animal behaviour, where intra-specific and often intra-population scale of scientific enquiry has mostly been the focus over the last four decades. We argue that behavioural plasticity can be a critical first response to environmental change that might buffer or even lower the risk of extinction. To estimate the capacity of populations or species to respond to change behaviourally, we propose a comparative approach- spatially, temporally or taxonomically- that systematically captures variation in key traits with broad implications for conservation and community ecology. Further, we provide guidance in the methods and resources required to apply a trait-based approach to animal behaviour.

The objects of language and thought establish the granularity at which cognition operates. Granularity can vary with respect to taxonomic classification, time, and space. One might suppose that basic cognitive operations such as judgments of similarity or mental imagery would be invariant over these changes in scale, but this appears not to be the case. This chapter reviews results from three domains that show how changes in grain lead to changes in the form of cognitive operations. In judgments about object features, small and medium taxonomic scales are heavily depending on an object’s parts, but large taxonomic scales are not. In perceiving events on a small temporal scale people pay close attention to actions on individual objects, but on a larger temporal scale they pay more attention to the particular objects involved. In spatial reasoning about small objects people tend to imagine objects being moved by an external force, but when reasoning about large environments they tend to imagine themselves moving within the environment. Thus, the computational form of cognitive operations appears to depend on the taxonomic, spatial, and temporal scale of the objects of those operations.

Conserving biodiversity in the face of expanding human degradation of ecosystems is facilitated by understanding the natural state of communities prior to the impact of anthropogenic disruptions. Reconstructing communities and ecosystems as they existed in the past requires data from the fossil record on their species composition, richness, and abundance. Fossil data are potentially different from data collected from living communities in their spatial, temporal, and taxonomic scales and these differences must be understood so that accurate comparisons can be made between past and present states of living communities. Fifty-four long-term ecological studies of a wide range of taxon groups (mammals, invertebrates, plants, corals) and habitat types (marine, terrestrial, freshwater) were surveyed from the published ecological literature to determine the range of spatial, temporal and taxonomic scales at which data are commonly collected in ecological research. Long-term ecological studies encompass spatial scales from 50m2 to 100,000km2 and temporal scales from 5 to 100 years. Most studies resolve taxa to the species level and count individuals, although plant and coral studies sometimes quantify species by percent cover. All taxon groups and habitat types were studied across a wide range of spatial and temporal scales. Whether or not data from fossils can be collected and analysed at scales comparable to data from living communities depends on the type of organism, as well as the taphonomic circumstances of preservation, accumulation and deposition. Marine invertebrates can be sampled at comparable spatial and taxonomic scales to living invertebrates, but time averaging degrades the temporal resolution of the fossil deposits. Vertebrate fossils provide data at comparable taxonomic scales with some reduction in spatial and temporal resolution relative to live data. Plant fossils and pollen are capable of being sampled at temporal resolutions comparable to modern ecological studies, but pollen data are prone to spatial averaging and have much poorer taxonomic resolution than censuses of living communities. It is important to be mindful of the limitations that scale mismatches produce in the ability to use fossil data to resolve ecological events and to compare the details of ecological composition and structure between the present and the past.

A synthesis is underway between ecology and evolution, partly brought about by the realization that evolutionary change can take place on ecological timescales (Hairston et al., 2005; Whitham et al., 2006; Carroll et al., 2007). This synthesis attempts to understand the dynamic interplay of ecological and evolutionary processes that results from natural or anthropogenic selective forces (Lankau & Strauss, 2007). Moreover, this synthesis represents an integration of several ‘genes to ecosystems’ approaches, including ‘ecological stochiometry’, ‘community genetics’ (Whitham et al., 2006) and ‘niche construction’. United under the framework of ‘eco-evolutionary dynamics’, these ideas seek to link genetic and phenotypic variation to population dynamics, biodiversity and ecosystem function, and place these disciplines in a dynamic evolutionary framework (i.e. understanding the ecological consequences of evolutionary processes and the evolutionary consequences of ecological interactions). This is not an easy endeavor because any such synthesis needs to be broadly multidisciplinary and integrative (Whitham et al., 2006). And yet the potential pay offs are large given that genetic variation across plant and animal systems can have extended consequences at the population, community and ecosystem levels. These consequences can come in the form of the vital rates of survival, reproduction and migration, as well as arthropod and aquatic macroinvertebrate diversity, soil microbial communities, trophic interactions, carbon storage, soil nitrogen availability, dissolved organic nitrogen and production of primary producers (Whitham et al., 2006; Bailey et al., 2009; Ezard et al., 2009; Harmon et al., 2009; Johnson et al., 2009; Palkovacs et al., 2009; Post & Palkovacs, 2009). The effects of genetic or phenotypic variation are not limited to single systems or to ecologically important species (i.e. keystone species, dominant species, foundation species, ecosystem engineers), although these are excellent places to start looking. Instead, genetic variation seems to have effects that are broadly distributed across plant and animal systems - and these effects can be similar in magnitude to those of nonevolutionary ecological variables, such as climate, species invasion and habitat quality (Hairston et al., 2005; Bailey et al., 2009; Ezard et al., 2009; Palkovacs et al., 2009; Post & Palkovacs, 2009).

Aim To explore the influence of an emerging infectious disease (EID) affecting a prey species on the spatial patterns and temporal shifts in the diet of a predator over a large geographical scale. We reviewed studies on the diet of Bonelli’s eagles (Hieraaetus fasciatus) in order to determine the repercussions of the reduction in the density of its main prey, the rabbit (Oryctolagus cuniculus), caused by outbreaks of rabbit haemorrhagic disease (RHD) since 1988.Location Western continental Europe.Methods We compiled published and unpublished information on the diet of breeding Bonelli’s eagles from Portugal, Spain and France for a 39‐year study period (1968–2006). Nonparametric tests were used in order to analyse temporal shifts in diet composition and trophic diversity (H′) between the periods of ‘high’ (before outbreak of RHD) and ‘low’ rabbit density (after outbreak of RHD). A combination of hierarchical agglomerative clustering and non‐metric multidimensional scaling (NMDS) analyses were used to test for the existence of geographical patterns in the diet of Bonelli’s eagles in each period.Results The diet of the Bonelli’s eagle consisted of rabbit (28.5%), pigeons (24.0%), partridges (15.3%), ‘other birds’ (11.6%), ‘other mammals’ (7.1%), corvids (7.0%), and herptiles (6.4%). However, RHD had large consequences for its feeding ecology: the consumption of rabbits decreased by one‐third after the outbreak of RHD. Conversely, trophic diversity (H′) increased after outbreak of RHD. At the same time, the analyses showed clear geographical patterns in the diet of the Bonelli’s eagle before, but not after, RHD outbreak.Main conclusions Geographical patterns in the diet of the Bonelli’s eagle in western Europe seem to be driven mainly by spatio‐temporal variation in the abundance of rabbits and, to a lesser extent, by the local (territorial) environmental features conditioning the presence and density of alternative prey species. We show that an EID can disrupt predator–prey relationships at large spatial and temporal scales through a severe decline in the population of the main prey species. Hence we argue that strict guidelines should be drawn up to prevent human‐aided dissemination of ‘pathogen pollution’, which can threaten wildlife not only at the population and species level but also at the community and ecosystem scale.

Predictive species’ distribution models may answer ecological questions about habitat selection, co-occurrence of species and competition between them. We studied the habitat preferences and segregation of two sympatric species of declining sandgrouse, the black-bellied sandgrouse (Pterocles orientalis) and the pin-tailed sandgrouse (Pterocles alchata), during the breeding season. We developed predictive models that related sandgrouse presence to environmental variables at three different spatial levels: large geographical, landscape and microhabitat scales. At the large geographical scale, differences between sandgrouse distributions, in the Iberian Peninsula, seem to be explained mainly in terms of bioclimatology: pin-tailed sandgrouse appear to be a more thermophilous species and occupy warmer sites usually located in flatter areas. At the landscape spatial level, in those areas that exhibit environmental conditions allowing for both species’ co-existence at a large geographical scale, black-bellied sandgrouse appear to be more tolerant to environmental variation than pin-tailed sandgrouse. At the microhabitat level, however, differences between species could be related to different flocking behaviour as a consequence of different sensitivities to vegetation structure and predators. Thus, the observed spatial distribution patterns are the result of different ecological factors that operate at different spatial levels. Conservation guidelines for these species should therefore consider their habitat preferences at large geographical, landscape and microhabitat scales.

The August roadside survey is a population index used to monitor trends in productivity and population status of ring‐necked pheasant ( Phasianus colchicus ) in several states of the United States. Inter‐annual population changes from roadside surveys have occasionally implied biologically implausible outcomes, hinting at survey bias and constrained utility of the index under certain environmental conditions. Research has shown correlations between environmental conditions and ring‐necked pheasant detections but range‐wide rigorous assessment of factors influencing detection probabilities has never been considered. We sought to evaluate environmental factors influencing detection probability of ring‐necked pheasant broods across a large geographic scale, where August roadside surveys are an important monitoring tool. State wildlife resource agencies conducted 1,000 August roadside surveys on 174 unique route‐by‐year combinations in 11 states during 2019–2021. We used a single‐species N‐mixture model in a Bayesian framework to examine factors influencing detection probability of broods. Wind speed and cloud cover negatively influenced detection probability of pheasant broods. Dewpoint depression, a proxy for morning dew conditions where higher values indicate less dew, had a significant negative effect on detection probability for pheasant broods. Soil moisture had a positive effect on the detection probability of pheasant broods. Observed variation in conditions across routes, among the years in our study, and across the geographic scale covered could constrain direct comparisons of uncorrected counts. Survey methodology can be adjusted to target mornings with few clouds, low winds, and favorable dew conditions to increase detection probability and consistency among surveys. However, soil moisture may be difficult to methodologically control for over larger scales of space and time. Posterior estimates from our model may be used to gauge intra‐ and inter‐seasonal variation in conditions for detecting pheasant broods, which could improve inference from long‐term population monitoring, especially across large spatial and temporal scales.

AimEcological patterns and process change across spatial, temporal and taxonomic scales. This confounds comparisons between modern and fossil communities, which are sampled across very different scales, especially temporal ones. We use a recent bone dataset (i.e., “death assemblages”) from a modern ecosystem to explore spatial, temporal and taxonomic scaling in biodiversity assessments. Our ultimate goal is to create a model based on these scaling relationships to facilitate meaningful comparisons between modern and fossil communities.LocationAmboseli National Park, southern Kenya.Time periodMid‐1960 s to present day.Major taxa studiedLarge mammals (>1 kg).MethodsWe implemented a random placement null model and used model selection methods to investigate how species richness at Amboseli scales as a function of time and area [i.e., the species–time–area relationship (STAR) model]. We then analysed how the model coefficients change at different taxonomic scales (i.e., genus, family, order).ResultsIn agreement with previous studies, we find species richness scales positively with time and area but with a negative interaction between the two. Rates of richness turnover decrease as taxonomic scale increases.Main conclusionsWe hypothesize that decreasing rates of turnover with increasing spatial and/or temporal scale are caused by taking progressively larger samples from a species pool that is changing at a slower rate relative to turnover at the scale of sampling. Because increasing area and time are simply alternative ways of uncovering the species pool, increased time‐averaging of communities results in a more spatially averaged ecological signal. Increasing taxonomic scale causes turnover rates to decrease because of how lower‐level taxa are aggregated into coarser, higher‐level ones. The STAR model presents a framework for extrapolating and comparing richness between small‐scale modern and large‐scale fossil communities, as well as a means to understand the general processes involved with changing scale.

Interpretations of morphologic radiations and macroevolutionary patterns are dependent on a priori choices of taxonomic and geographic scales of study. The results of disparity analysis at varying taxonomic (species and genus) and geographic (regional, biofacies, and community) scales are examined in a study of Ordovician though Early Silurian crinoids. Using discrete morphologic characters, we examined the disparity of 421 crinoids from 65 Laurentian biofacies. Crinoid disparity differs when analyzed at the regional and biofacies levels. Regardless of fluctuations in regional crinoid disparity, average within-biofacies disparity was static throughout the Ordovician, deviating only during the Silurian because of the proliferation of the morphologically aberrant myelodactylid crinoids. The choice of taxonomic level does not have an effect at the biofacies level. However, at the regional level, the two taxonomic scales (genus and species) can produce different results because of variation in the number of species per genus through time and the amount of morphologic variation within individual genera. Weighting disparity by abundance provides a metric combining morphology and community structure. Average weighted disparity at the community level showed patterns similar to that of the biofacies-level disparity curve, but this metric has a greater degree of variation between biofacies. Biofacies with a low ratio of weighted to unweighted disparity display the distinctive community structure (based on aerosol filtration theory) that is often reported in crinoid assemblages.

We questioned the different interpretations of ecological sexual segregation from a novel perspective, i.e., by carrying out diverse temporal and spatial scale analyses within a long-term study (1984–2003). Thus we combined spatial (small/large) and temporal (small/large) scale analyses to identify the factors generating sexual segregation in fallow deer in San Rossore, Italy. The study site was divided into an eastern sector characterized by human disturbance (DS) and a western undisturbed sector (US). According to census data, human presence increased in DS from 1984, and while females gradually abandoned it, males remained—thus supporting the predation risk hypothesis (large spatial and temporal scale)—and actually increased their presence in DS, where they seemingly benefited from a lower female density. This supported the indirect competition hypothesis. The analysis of data on a large temporal and small spatial scale confirmed that intersexual competition, in particular for grass, was higher in a crowded pasture in US. Observations by means of radio-telemetry of 23 adult females and 25 adult males (1997–2001, reduced temporal and large spatial scale) showed that large scale segregation was relevant during the day and disappeared at night, when disturbance was absent and also the females reached DS. This also supported the predation risk hypothesis. Moreover, sexes showed different habitat choices inside DS at night, thus supporting the forage selection hypothesis (small spatial and temporal scale). In conclusion, failure to address the whole set of combinations of spatial and temporal scale analyses would have led to monocausal explanations of ecological sexual segregation.

SummaryMacrophysiology is the investigation of variation in physiological traits over large geographic and temporal scales and the ecological implications of this variation. It has now been undertaken, as a defined field, for a decade.Here, we overview its conceptual foundations, methodological approaches and insights, together with challenges the field is facing currently.Macrophysiology builds on approaches that investigate the ecological and evolutionary significance of physiological trait variation and feedbacks in these processes. One of its key strengths is its ability to provide a basis for examining interactions among the intraspecific, interspecific and assemblage levels.Macrophysiology is distinct from and typically concerns larger spatial and temporal scales than conservation physiology, whereas it is in several respects similar to, but antecedes, functional biogeography. Contrary to some claims, macrophysiology is not concerned only with the implications for geographic ranges of physiological trait variation.Several insights, which would not otherwise have been achieved, have arisen from the field, notably the understanding of variation in global patterns of upper and lower lethal temperature limits and organism performance, which have important implications for forecasting the impacts of climate change.Ten major challenges are identified for the field of macroecology, including better integration of approaches and information for plants and animals. Nonetheless, the prospects for macrophysiology as a significant way to understand our world remain bright.

Measurement of suspended particulate matter concentration (SPMC) spanning large time and geographical scales have become a matter of growing importance in recent decades. At many places worldwide, complex observation platforms have been installed to capture temporal and spatial variability over scales ranging from cm (turbulent regimes) to whole basins. Long-term in situ measurements of SPMC involve one or more optical and acoustical sensors and, as the ground truth reference, gravimetric measurements of filtered water samples. The estimation of SPMC from optical and acoustical proxies generally results from the combination of a number of independent calibration measurements, as well as regression or inverse models. Direct or indirect measurements of SPMC are inherently associated with a number of uncertainties along the whole operation chain, the autonomous field deployment, to the analyses necessary for converting the observed proxy values of optical and acoustical signals to SPMC. Controlling uncertainties will become an important issue when the observational input comprises systems of sensors spanning large spatial and temporal scales. This will be especially relevant for detecting trends in the data with unambiguous statistical significance, separating anthropogenic impact from natural variations, or evaluating numerical models over a broad ensemble of different conditions using validated field data.The aim of the study is to present and discuss the benefits and limitations of using optical and acoustical backscatter sensors to acquire long-term observations of SPMC. Additionally, this study will formulate recommendations on how to best acquire quality-assured SPMC data sets, based on the challenges and uncertainties associated with those long-term observations. The main sources of error as well as the means to quantify and reduce the uncertainties associated with SPMC measurements are also illustrated.