A flexible consistent framework for modelling multiple interacting environmental responses to management in space and time.

Management of resources is often a large-scale task addressed using many small-scale interventions. The range of scales at which organisms respond to those interventions, along with the many outcomes which management aims to achieve can make determining the success of management complex. Environmental flow is an example of management where there is a recognized need for managers to demonstrate the impact of their actions by integrating different types of environmental responses. Here, we aim to support decision making in environmental management via the development of a new modelling framework (eFlowEval). It has the capacity to capture best-available knowledge, to scale it in space and time, explore interactions among species, compare scenarios, and account for uncertainty. Thus, it provides a basis for including multiple target groups in a common system. The framework is readily updatable as new information becomes available and can identify where data are insufficient to be scientifically robust. We demonstrate the eFlowEval framework using three very different environmental responses: 1) metabolism, which is a measure of the energy produced and then used in an ecosystem, 2) favorability for a bird species of interest (royal spoonbill Platalea regia), and 3) competing wetland plants (Centipeda cunninghamii and lippia Phyla canescens). These demonstrations illustrate the capability of the eFlowEval framework but the specific outputs shown here should not be used to assess environmental responses to management. Using these demonstrations, we illustrate the capacity of the eFlowEval framework to provide assessments across a range of scales (local to landscape) and from short time frames (weeks to months) to multi-year assessments. Further, we illustrate the ability to: i) scale responses from local to basin scales, ii) vary driver-response model types, iii) represent uncertainty, iv) compare scenarios, v) accommodate variable parameter values at different locations, and vi) incorporate spatial and temporal dependencies and dependencies among species. We also illustrate the framework's ability to capture inter- and intraspecific interactions and their impact in space and time. The eFlowEval framework extends the capacity of the component response models to provide novel modeling capabilities for management at scale. It allows for interactions among species or processes to be incorporated, as well as in space and time. A large degree of flexibility is offered by the framework, in terms of driver-response model types, input data, and aggregation methods. Thus, the eFlowEval framework provides a mechanism to enhance the transparency of environmental watering decision making, capture institutional knowledge, enhance adaptive management and undertake evaluation of the impact of environmental watering at a range of spatial and temporal scales.

Caloric restriction extends lifespan in a clonal plant.

When subjected to dietary caloric restriction (CR), individual animals often outlive well-fed conspecifics. Here, we address whether CR also extends lifespan in plants. Whereas caloric intake in animals comes from ingestion, in plants it derives from photosynthesis. Thus, factors that reduce photosynthesis, such as reduced light intensity, can induce CR. In two lab experiments investigating the aquatic macrophyte Lemna minor, we tracked hundreds of individuals longitudinally, with light intensity-and hence, CR-manipulated using neutral-density filters. In both experiments, CR dramatically increased lifespan through a process of temporal scaling. Moreover, the magnitude of lifespan extension accorded with the assumptions that (a) light intensity positively relates to photosynthesis following Michaelis-Menten kinetics, and (b) photosynthesis negatively relates to lifespan via a power law. Our results emphasize that CR-mediated lifespan extension applies to autotrophs as well as heterotrophs, and suggest that variation in light intensity has quantitatively predictable effects on plant aging trajectories.

Scaling procedures in climate science: Using temporal scaling to identify a paleoclimate analogue.

Using past episodes of climate change as a source of evidence to inform our projections about contemporary climate change requires establishing the extent to which episodes in the deep past are analogous to the current crisis. However, many scientists claim that contemporary rates of climate change (e.g., rates of carbon emissions or temperature change) are unprecedented, including compared to episodes in the deep past. If so, this would limit the utility of paleoclimate analogues. In this paper, I show how a data adjustment procedure called "temporal scaling," which must be applied to both contemporary and past rate data, complicates the claim that contemporary rates are truly unprecedented. On top of giving actionable recommendations to scientists, this paper advances the philosophical literature concerning the use of models that are known to be somewhat disanalogous to their target systems.

Influence of Recent Trial History on Interval Timing.

Interval timing is involved in a variety of cognitive behaviors such as associative learning and decision-making. While it has been shown that time estimation is adaptive to the temporal context, it remains unclear how interval timing behavior is influenced by recent trial history. Here we found that, in mice trained to perform a licking-based interval timing task, a decrease of inter-reinforcement interval in the previous trial rapidly shifted the time of anticipatory licking earlier. Optogenetic inactivation of the anterior lateral motor cortex (ALM), but not the medial prefrontal cortex, for a short time before reward delivery caused a decrease in the peak time of anticipatory licking in the next trial. Electrophysiological recordings from the ALM showed that the response profiles preceded by short and long inter-reinforcement intervals exhibited task-engagement-dependent temporal scaling. Thus, interval timing is adaptive to recent experience of the temporal interval, and ALM activity during time estimation reflects recent experience of interval.

Dynamic multi-dimensional scaling of 30+ year evolution of Chinese urban systems: Patterns and performance.

Understanding the co-evolution and organizational dynamics of urban properties (i.e., urban scaling) is the science base for pursuing synergies toward sustainable cities and society. The generalization of urban scaling theory yet requires more studies from various developmental regimes and across time. Here, we extend the universality proposition by exploring the evolution of longitudinal and transversal scaling of Chinese urban attributes between 1987 and 2018 using a global artificial impervious area (GAIA) remotely sensed dataset, harmonized night light data (NTL), and socioeconomic data, and revealed agreements and disagreements with theories. The superlinear relationship of urban area and population often considered as an indicator of wasting land resources (challenging the universality theory ßc = 2/3), is in fact the powerful impetus (capital raising) behind the concurrent superlinear expansion of socio-economic metabolisms (e.g., GDP, total wage) in a rapidly urbanizing country that has not yet reached equilibrium. Similarly, infrastructural variables associated with public services, such as hospitals and educational institutions, exhibited some deviations as well and were scaled linearly. However, the temporal narrowing of spatial deviations, such as the decline in urban land diseconomies of scale and the stabilization of economic output, clearly indicates the Chinese government's effort in charting urban systems toward balanced and sustainable development across the country. More importantly, the transversal sublinear scaling of areal-based socio-economic variables was inconsistent with the theoretical concept of increasing returns to scale, thus validating the view that a single measurement cannot unravel the intricate web of diverse urban attributes and urbanization. Our dynamic urban scaling analysis across space and through time in China provides new insights into the evolving nexus of urbanization, socioeconomic development, and national policies.

Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?

Timing underlies a variety of functions, from walking to perceiving causality. Neural timing models typically fall into one of two categories-"ramping" and "population-clock" theories. According to ramping models, individual neurons track time by gradually increasing or decreasing their activity as an event approaches. To time different intervals, ramping neurons adjust their slopes, ramping steeply for short intervals and vice versa. In contrast, according to "population-clock" models, multiple neurons track time as a group, and each neuron can fire nonlinearly. As each neuron changes its rate at each point in time, a distinct pattern of activity emerges across the population. To time different intervals, the brain learns the population patterns that coincide with key events. Both model categories have empirical support. However, they often differ in plausibility when applied to certain behavioral effects. Specifically, behavioral data indicate that the timing system has a rich computational capacity, allowing observers to spontaneously compute novel intervals from previously learned ones. In population-clock theories, population patterns map to time arbitrarily, making it difficult to explain how different patterns can be computationally combined. Ramping models are viewed as more plausible, assuming upstream circuits can set the slope of ramping neurons according to a given computation. Critically, recent studies suggest that neurons with nonlinear firing profiles often scale to time different intervals-compressing for shorter intervals and stretching for longer ones. This "temporal scaling" effect has led to a hybrid-theory where, like a population-clock model, population patterns encode time, yet like a ramping neuron adjusting its slope, the speed of each neuron's firing adapts to different intervals. Here, we argue that these "relative" population-clock models are as computationally plausible as ramping theories, viewing population-speed and ramp-slope adjustments as equivalent. Therefore, we view identifying these "speed-control" circuits as a key direction for evaluating how the timing system performs computations. Furthermore, temporal scaling highlights that a key distinction between different neural models is whether they propose an absolute or relative time-representation. However, we note that several behavioral studies suggest the brain processes both scales, cautioning against a dichotomy.

Where Is Garlic Mustard? Understanding the Ecological Context for Invasions of Alliaria petiolata.

The invasive plant Alliaria petiolata (garlic mustard) has spread throughout forest understory and edge communities in much of North America, but its persistence, density, and impacts have varied across sites and time. Surveying the literature since 2008, we evaluated both previously proposed and new mechanisms for garlic mustard's invasion success and note how they interact and vary across ecological contexts. We analyzed how and where garlic mustard has been studied and found a lack of multisite and longitudinal studies, as well as regions that may be under- or overstudied, leading to poor representation for understanding and predicting future invasion dynamics. Inconsistencies in how sampling units are scaled and defined can also hamper our understanding of invasive species. We present new conceptual models for garlic mustard invasion from a macrosystems perspective, emphasizing the importance of synergies and feedbacks among mechanisms across spatial and temporal scales to produce variable ecological contexts.

Temporal scaling in C. elegans larval development.

SignificanceAn enduring mystery of development is how its timing is controlled, particularly for development after birth, where timing is highly flexible and depends on environmental conditions, such as food availability and diet. We followed timing of cell- and organism-level events in individual Caenorhabditis elegans larvae developing from hatching to adulthood, uncovering widespread variations in event timing, both between isogenic individuals in the same environment and when changing conditions and genotypes. However, in almost all cases, we found that events occurred at the same time, when time was rescaled by the duration of development measured in each individual. This observation of "temporal scaling" poses strong constraints on models to explain timing of larval development.

Spatial and Temporal Scaling of Microtubules and Mitotic Spindles.

During cell division, the mitotic spindle, a macromolecular structure primarily comprised of microtubules, drives chromosome alignment and partitioning between daughter cells. Mitotic spindles can sense cellular dimensions in order to adapt their length and mass to cell size. This scaling capacity is particularly remarkable during early embryo cleavage when cells divide rapidly in the absence of cell growth, thus leading to a reduction of cell volume at each division. Although mitotic spindle size scaling can occur over an order of magnitude in early embryos, in many species the duration of mitosis is relatively short, constant throughout early development and independent of cell size. Therefore, a key challenge for cells during embryo cleavage is not only to assemble a spindle of proper size, but also to do it in an appropriate time window which is compatible with embryo development. How spatial and temporal scaling of the mitotic spindle is achieved and coordinated with the duration of mitosis remains elusive. In this review, we will focus on the mechanisms that support mitotic spindle spatial and temporal scaling over a wide range of cell sizes and cellular contexts. We will present current models and propose alternative mechanisms allowing cells to spatially and temporally coordinate microtubule and mitotic spindle assembly.

A precise and adaptive neural mechanism for predictive temporal processing in the frontal cortex.

The theory of predictive processing posits that the brain computes expectations to process information predictively. Empirical evidence in support of this theory, however, is scarce and largely limited to sensory areas. Here, we report a precise and adaptive mechanism in the frontal cortex of non-human primates consistent with predictive processing of temporal events. We found that the speed of neural dynamics is precisely adjusted according to the average time of an expected stimulus. This speed adjustment, in turn, enables neurons to encode stimuli in terms of deviations from expectation. This lawful relationship was evident across multiple experiments and held true during learning: when temporal statistics underwent covert changes, neural responses underwent predictable changes that reflected the new mean. Together, these results highlight a precise mathematical relationship between temporal statistics in the environment and neural activity in the frontal cortex that may serve as a mechanism for predictive temporal processing.

Insect biomass decline scaled to species diversity: General patterns derived from a hoverfly community.

Reports of declines in biomass of flying insects have alarmed the world in recent years. However, how biomass declines reflect biodiversity loss is still an open question. Here, we analyze the abundance (19,604 individuals) of 162 hoverfly species (Diptera: Syrphidae), at six locations in German nature reserves in 1989 and 2014, and generalize the results with a model varying decline rates of common vs. rare species. We show isometric decline rates between total insect biomass and total hoverfly abundance and a scale-dependent decline in hoverfly species richness, ranging between -23% over the season to -82% at the daily level. We constructed a theoretical null model to explore how strong declines in total abundance translate to changing rank-abundance curves, species persistence, and diversity measures. Observed persistence rates were disproportionately lower than expected for species of intermediate abundance, while the rarest species showed decline and appearance rates consistent with random expectation. Our results suggest that large insect biomass declines are predictive of insect diversity declines. Under current threats, even the more common species are in peril, calling for a reevaluation of hazards and conservation strategies that traditionally target already rare and endangered species only.

The past and future roles of competition and habitat in the range-wide occupancy dynamics of Northern Spotted Owls.

Slow ecological processes challenge conservation. Short-term variability can obscure the importance of slower processes that may ultimately determine the state of a system. Furthermore, management actions with slow responses can be hard to justify. One response to slow processes is to explicitly concentrate analysis on state dynamics. Here, we focus on identifying drivers of Northern Spotted Owl (Strix occidentalis caurina) territorial occupancy dynamics across 11 study areas spanning their geographic range and forecasting response to potential management actions. Competition with Barred Owls (Strix varia) has increased Spotted Owl territory extinction probabilities across all study areas and driven recent declines in Spotted Owl populations. Without management intervention, the Northern Spotted Owl subspecies will be extirpated from parts of its current range within decades. In the short term, Barred Owl removal can be effective. Over longer time spans, however, maintaining or improving habitat conditions can help promote the persistence of northern spotted owl populations. In most study areas, habitat effects on expected Northern Spotted Owl territorial occupancy are actually greater than the effects of competition from Barred Owls. This study suggests how intensive management actions (removal of a competitor) with rapid results can complement a slower management action (i.e., promoting forest succession).

Contemporary evolution and scaling of 32 major cities in China.

Most of the planet's population currently lives in urban areas, and urban land expansion is one of the most dramatic forms of land conversion. Understanding how cities evolve temporally, spatially, and organizationally in a rapidly urbanizing world is critical for sustainable development. However, few studies have examined the coevolution of urban attributes in time and space simultaneously and the adequacy of power law scaling across cities and through time, particularly in countries that have experienced abrupt, widespread, political and economic changes. Here, we show the temporal coevolution of multiple physical, demographic, socioeconomic, and environmental attributes in individual cities, and the cross-city scaling of urban attributes at six time points (i.e., 1978, 1990, 1995, 2000, 2005, and 2010) in 32 major Chinese cities. We found that power law scaling could adequately characterize both the cross-city scaling of urban attributes across cities and the longitudinal scaling describing the temporal coevolution of urban attributes within individual cities. The cross-city scaling properties demonstrated substantial changes over time signifying evolved social and economic forces. A key finding was that the cross-city linear or superlinear scaling of urban area with population contradicts the theoretical sublinear power law scaling proposed between infrastructure and population. Furthermore, the cross-city scaling between area and population transitioned from linear to superlinear over time, and the superlinear scaling in recent times suggests decreased infrastructure efficiency. Our results demonstrate a diseconomy of scale in urban areal expansion that indicates a significant waste of land resources in the urbanization process. Future planning efforts should focus on policies that increase urban land use efficiency before continuing expansion.

Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks.

Much of the information the brain processes and stores is temporal in nature-a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds-we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli.

Root architecture simulation improves the inference from seedling root phenotyping towards mature root systems.

Root phenotyping provides trait information for plant breeding. A shortcoming of high-throughput root phenotyping is the limitation to seedling plants and failure to make inferences on mature root systems. We suggest root system architecture (RSA) models to predict mature root traits and overcome the inference problem. Sixteen pea genotypes were phenotyped in (i) seedling (Petri dishes) and (ii) mature (sand-filled columns) root phenotyping platforms. The RSA model RootBox was parameterized with seedling traits to simulate the fully developed root systems. Measured and modelled root length, first-order lateral number, and root distribution were compared to determine key traits for model-based prediction. No direct relationship in root traits (tap, lateral length, interbranch distance) was evident between phenotyping systems. RootBox significantly improved the inference over phenotyping platforms. Seedling plant tap and lateral root elongation rates and interbranch distance were sufficient model parameters to predict genotype ranking in total root length with an RSpearman of 0.83. Parameterization including uneven lateral spacing via a scaling function substantially improved the prediction of architectures underlying the differently sized root systems. We conclude that RSA models can solve the inference problem of seedling root phenotyping. RSA models should be included in the phenotyping pipeline to provide reliable information on mature root systems to breeding research.

Dissociating movement from movement timing in the rat primary motor cortex.

Neural encoding of the passage of time to produce temporally precise movements remains an open question. Neurons in several brain regions across different experimental contexts encode estimates of temporal intervals by scaling their activity in proportion to the interval duration. In motor cortex the degree to which this scaled activity relies upon afferent feedback and is guided by motor output remains unclear. Using a neural reward paradigm to dissociate neural activity from motor output before and after complete spinal transection, we show that temporally scaled activity occurs in the rat hindlimb motor cortex in the absence of motor output and after transection. Context-dependent changes in the encoding are plastic, reversible, and re-established following injury. Therefore, in the absence of motor output and despite a loss of afferent feedback, thought necessary for timed movements, the rat motor cortex displays scaled activity during a broad range of temporally demanding tasks similar to that identified in other brain regions.

A review of current methodologies for regional evapotranspiration estimation from remotely sensed data.

An overview of the commonly applied evapotranspiration (ET) models using remotely sensed data is given to provide insight into the estimation of ET on a regional scale from satellite data. Generally, these models vary greatly in inputs, main assumptions and accuracy of results, etc. Besides the generally used remotely sensed multi-spectral data from visible to thermal infrared bands, most remotely sensed ET models, from simplified equations models to the more complex physically based two-source energy balance models, must rely to a certain degree on ground-based auxiliary measurements in order to derive the turbulent heat fluxes on a regional scale. We discuss the main inputs, assumptions, theories, advantages and drawbacks of each model. Moreover, approaches to the extrapolation of instantaneous ET to the daily values are also briefly presented. In the final part, both associated problems and future trends regarding these remotely sensed ET models were analyzed to objectively show the limitations and promising aspects of the estimation of regional ET based on remotely sensed data and ground-based measurements.

MOLECULAR AND GEOLOGIC EVIDENCE OF SHARED HISTORY BETWEEN HERMIT CRABS AND THE SYMBIOTIC GENUS HYDRACTINIA.

The paleobiogeographic histories of three North Atlantic hermit crab lineages were compared with a single-copy DNA-DNA hybridization phylogeny of their symbiotic hydroid genus Hydractinia to test hypotheses of shared history between these host and symbiont lineages. A survey of the geologic literature suggests that two vicariance events in the Quaternary are responsible for existing range disjunctions of the host hermit crab lineages. The Hydractinia phylogeny revealed two distinct clades, one with a primarily northern and the other with a primarily southern distribution. In two of three cases, hydroids associated with closely related hermits on both sides of the range disjunction appear as sister taxa in the phylogeny. A linear scaling between a measure of hydroid sequence divergence and independent geologic estimates of the timing of the vicariant events believed to have established the hermit crab range disjunctions is consistent with the claim of temporal coincidence of cladogenic and vicariance events. These findings provide evidence for shared history of symbiotic associations in two of the three cases.