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Urban areas are rapidly expanding globally. The detection of settlement expansion can, however, be challenging due to the rapid rate of expansion, especially for informal settlements. This paper presents a solution in the form of an unsupervised autocorrelation-based approach. Temporal autocorrelation function (ACF) values derived from hyper-temporal Sentinel-1 imagery were calculated for all time lags using VV backscatter values. Various thresholds were applied to these ACF values in order to create urban change maps. Two different orbital combinations were tested over four informal settlement areas in South Africa. Promising results were achieved in the two of the study areas with mean normalized Matthews Correlation Coefficients (MCCn) of 0.79 and 0.78. A lower performance was obtained in the remaining two areas (mean MCCn of 0.61 and 0.65) due to unfavorable building orientations and low building densities. The first results also indicate that the most stable and optimal ACF-based threshold of 95 was achieved when using images from both relative orbits, thereby incorporating more incidence angles. The results demonstrate the capacity of ACF-based methods for detecting settlement expansion. Practically, this ACF-based method could be used to reduce the time and labor costs of detecting and mapping newly built settlements in developing regions. [ABSTRACT FROM AUTHOR]

Grassland resilience is influenced by climate change and human activities. However, little is known about how grassland resilience has changed, driven by climate change and human activities, on the Mongolian Plateau. We calculated grassland resilience on the Mongolian Plateau from 2000 to 2021 using the kernel normalized difference vegetation index (kNDVI), quantified the trends of grassland resilience using a newly proposed "critical slowing down" indicator with a machine learning algorithm, and compared the driving forces for these changes between Inner Mongolia and Mongolia. The findings of this study demonstrate that heightened levels of precipitation and reduced temperature contribute to the enhanced resilience of grassland ecosystems on the Mongolian Plateau. Conversely, the presence of grazing activities exhibits a detrimental effect on such resilience. In semi-arid regions, approximately 43% of grassland areas are undergoing a discernible decline in resilience. This decline is particularly pronounced in regions characterized by heightened levels of grazing intensity. In addition, resilience declined in 54% of areas with population growth compared with 32% in areas with population decline. Inner Mongolia, with its higher intensity of human activities, has a more serious decline in ecological resilience than Mongolia, indicating that further ecological restoration measures are needed. [ABSTRACT FROM AUTHOR]

Studies of foraging behaviour and respiratory physiology in breath‐holding divers often assume that each dive cycle (dive plus surface duration) is physiologically and ecologically independent within a series (or "bout") of sequential dives. We tested this assumption using time depth recorders and GPS data for more than 42,000 dives in 1289 bouts by 39 pairs of male and female European shags (Gulosus aristotelis) provisioning nestlings. We found distinct patterns of temporal autocorrelation over several dives within bouts, but this was driven mainly by consecutive dives of the same type, that is, runs of V‐shaped (presumably prey searching) versus U‐shaped (presumably active hunting) dives. We found no evidence of cumulative physiological effects (i.e. fatigue and/or lowered body temperature) across dives within a bout. However, within‐individual variation in dive behaviour revealed complex interactions. Longer bouts were associated with more V‐shaped dives, including more and longer runs of V‐shaped dives. Meanwhile, more U‐shaped dives and longer runs of U‐shaped dives acted as limiting factors to bout lengths, with longer bouts being associated with more U‐shaped dives only later in the bout. Interactions between bout length and body mass, and between dive order within the bout and body mass, also suggested various size‐specific patterns in the temporal distribution of U‐shaped dives. Long bouts and bouts ending in longer runs of V‐shaped dives were more likely to indicate the termination of foraging activity. However, neither dive type nor bout length predicted whether individuals subsequently (i) stayed to forage in the same location or (ii) moved to a new location to continue foraging within the same trip from the nest. European shags therefore showed temporal non‐independence across successive dive cycles and successive bouts of dives, likely as a result of temporal and spatial variation in prey availabilities rather than cumulative physiological effects that might contravene the assumptions in models of optimal dive behaviour. [ABSTRACT FROM AUTHOR]

To test whether radial variation of wood specific gravity (WSG) is controlled by tree age or tree size in teak (Tectona grandis L.f) plantation trees, opposing different-length pith-to-bark strips which represents the differential lateral growth rate was compared using mixed-effects model which considers the heterogeneity of variances and dependency in the data to gain insight into the stochastic processes that govern the wood formation process. Various models were tested in devising an appropriate radial WSG model. Models that accounted for serial correlation in WSG data performed better than the simple structure that assumes zero correlation between measurements. The autoregressive plus random tree effect structure performed better in describing the radial variation pattern. The variability of the data related to random fluctuations during tree development and the wood formation process is modeled by the autoregressive parameter revealing the intrinsic complexity of wood formation. Since they cannot be attributed to observed factors, models should consider temporal or serial correlations when assessing wood quality. The results revealed that tree age is a decisive factor in controlling the WSG of wood, while tree size is statistically less important. Furthermore, the core wood production period varies with the growth rate. It is shown that the core wood area decreased with slow growth. Findings presented here appear to provide the first demonstration of radial variation in WSG with respect to growth rate and age for planted teak growing in Ghana. [ABSTRACT FROM AUTHOR]

Introduction: Analysis of task fMRI studies is typically based on using ordinary least squares within a voxel- or vertex-wise linear regression framework known as the general linear model. This use produces estimates and standard errors of the regression coefficients representing amplitudes of task-induced activations. To produce valid statistical inferences, several key statistical assumptions must be met, including that of independent residuals. Since task fMRI residuals often exhibit temporal autocorrelation, it is common practice to perform "prewhitening" to mitigate that dependence. Prewhitening involves estimating the residual correlation structure and then applying a filter to induce residual temporal independence. While theoretically straightforward, a major challenge in prewhitening for fMRI data is accurately estimating the residual autocorrelation at each voxel or vertex of the brain. Assuming a global model for autocorrelation, which is the default in several standard fMRI software tools, may under- or over-whiten in certain areas and produce differential false positive control across the brain. The increasing popularity of multiband acquisitions with faster temporal resolution increases the challenge of effective prewhitening because more complex models are required to accurately capture the strength and structure of autocorrelation. These issues are becoming more critical now because of a trend toward subject-level analysis and inference. In group-average or group-difference analyses, the within-subject residual correlation structure is accounted for implicitly, so inadequate prewhitening is of little real consequence. For individual subject inference, however, accurate prewhitening is crucial to avoid inflated or spatially variable false positive rates. Methods: In this paper, we first thoroughly examine the patterns, sources and strength of residual autocorrelation in multiband task fMRI data. Second, we evaluate the ability of different autoregressive (AR) model-based prewhitening strategies to effectively mitigate autocorrelation and control false positives. We consider two main factors: the choice of AR model order and the level of spatial regularization of AR model coefficients, ranging from local smoothing to global averaging. We also consider determining the AR model order optimally at every vertex, but we do not observe an additional benefit of this over the use of higher-order AR models (e.g. (AR(6)). To overcome the computational challenge associated with spatially variable prewhitening, we developed a computationally efficient R implementation using parallelization and fast C++ backend code. This implementation is included in the open source R package BayesfMRI. Results: We find that residual autocorrelation exhibits marked spatial variance across the cortex and is influenced by many factors including the task being performed, the specific acquisition protocol, mis-modeling of the hemodynamic response function, unmodeled noise due to subject head motion, and systematic individual differences. We also find that local regularization is much more effective than global averaging at mitigating autocorrelation. While increasing the AR model order is also helpful, it has a lesser effect than allowing AR coefficients to vary spatially. We find that prewhitening with an AR(6) model with local regularization is effective at reducing or even eliminating autocorrelation and controlling false positives. Conclusion: Our analysis revealed dramatic spatial differences in autocorrelation across the cortex. This spatial topology is unique to each session, being influenced by the task being performed, the acquisition technique, various modeling choices, and individual differences. If not accounted for, these differences will result in differential false positive control and power across the cortex and across subjects. [ABSTRACT FROM AUTHOR]

Temporal autocorrelation in environmental conditions influences population dynamics through its effects on vital rates. However, a comprehensive understanding of how and to what extent temporal autocorrelation shapes population dynamics is still lacking because most empirical studies have unrealistically assumed that environmental conditions are temporally independent. Mast seeding is a biological event characterized by highly fluctuating and synchronized seed production at the tree population scale as well as a marked negative temporal autocorrelation. In the current context of global change, mast seeding events are expected to become more frequent, leading to strengthened negative temporal autocorrelations and thereby amplified cyclicality in mast seeding dynamics. Theory predicts that population growth rates are maximized when the environmental cyclicality of consumer resources and their generation times are closely matched. To test this prediction, we took advantage of the long-term monitoring of a wild boar population, a widespread seed consumer species characterized by a short generation time (∼2 years). As expected, simulations indicated that its stochastic population growth rate increased as mast seeding dynamics became more negatively autocorrelated. Our findings demonstrate that accounting for temporal autocorrelations in environmental conditions relative to the generation time of the focal population is required, especially under conditions of global warming, where the cyclicality in resource dynamics is likely to change. [ABSTRACT FROM AUTHOR]

The storage effect is a general explanation for coexistence in a variable environment. Unfortunately, the storage effect is poorly understood, in part because the generality of the storage effect precludes an interpretation that is simultaneously simple, intuitive and correct. Here, we explicate the storage effect by dividing one of its key conditions—covariance between environment and competition—into two pieces, namely that there must be a strong causal relationship between environment and competition, and that the effects of the environment do not change too quickly. This finer‐grained definition can explain a number of previous results, including (1) that the storage effect promotes annual plant coexistence when the germination rate fluctuates, but not when the seed yield fluctuates, (2) that the storage effect is more likely to be induced by resource competition than the apparent competition, and (3) why the storage effect arises readily in models with either stage structure or environmental autocorrelation. Additionally, our expanded definition suggests two novel mechanisms by which the temporal storage effect can arise—transgenerational plasticity and causal chains of environmental variables—thus suggesting that the storage effect is a more common phenomenon than previously thought. [ABSTRACT FROM AUTHOR]

Many statistical models currently used in ecology and evolution account for covariances among random errors. Here, I address five points: (i) correlated random errors unite many types of statistical models, including spatial, phylogenetic and time‐series models; (ii) random errors are neither unpredictable nor mistakes; (iii) diagnostics for correlated random errors are not useful, but simulations are; (iv) model predictions can be made with random errors; and (v) can random errors be causal?These five points are illustrated by applying statistical models to analyse simulated spatial, phylogenetic and time‐series data. These three simulation studies are paired with three types of predictions that can be made using information from covariances among random errors: predictions for goodness‐of‐fit, interpolation, and forecasting.In the simulation studies, models incorporating covariances among random errors improve inference about the relationship between dependent and independent variables. They also imply the existence of unmeasured variables that generate the covariances among random errors. Understanding the covariances among random errors gives information about possible processes underlying the data.Random errors are caused by something. Therefore, to extract full information from data, covariances among random errors should not just be included in statistical models; they should also be studied in their own right. Data are hard won, and appropriate statistical analyses can make the most of them. [ABSTRACT FROM AUTHOR]

Transgenerational plasticity is the plastic response of offspring to ancestral environments. Theoretical studies and experimental evolution have demonstrated the importance of environmental predictability in driving the evolution of transgenerational plasticity. Nevertheless, the contribution of environmental predictability to the variation of transgenerational plasticity in nature remains unexplored. Here, we took advantage of the natural variation of Arabidopsis thaliana, and employed genotypes collected from different geographic locations to explore their transgenerational effects in response to drought and nutrient addition. We found that transgenerational plasticity depended on the offspring environment and the genotype. In particular, we found that genotypes with greater transgenerational plasticity in reproductive traits came from locations with greater temporal autocorrelation of annual temperature and precipitation. These results suggest that the evolutionary process shaping natural variation of transgenerational effect is not random, and the predictability of natural environments may contribute to the evolution of transgenerational effects. While the robustness of our study is limited by the number of genotypes analyzed, we hope this study provides an incentive to conduct a more comprehensive analysis towards understanding natural divergence of transgenerational plasticity. [ABSTRACT FROM AUTHOR]

Binary outcome models are frequently used in the social sciences and economics. However, such models are difficult to estimate with interdependent data structures, including spatial, temporal, and spatio-temporal autocorrelation because jointly determined error terms in the reduced-form specification are generally analytically intractable. To deal with this problem, simulation-based approaches have been proposed. However, these approaches (i) are computationally intensive and impractical for sizable datasets commonly used in contemporary research, and (ii) rarely address temporal interdependence. As a way forward, we demonstrate how to reduce the computational burden significantly by (i) introducing analytically-tractable pseudo maximum likelihood estimators for latent binary choice models that exhibit interdependence across space and time and by (ii) proposing an implementation strategy that increases computational efficiency considerably. Monte Carlo experiments show that our estimators recover the parameter values as good as commonly used estimation alternatives and require only a fraction of the computational cost. [ABSTRACT FROM AUTHOR]

Recent advances in tagging and biologging technology have yielded unprecedented insights into wild animal physiology. However, time-series data from such wild tracking studies present numerous analytical challenges owing to their unique nature, often exhibiting strong autocorrelation within and among samples, low samples sizes and complicated random effect structures. Gleaning robust quantitative estimates from these physiological data, and, therefore, accurate insights into the life histories of the animals they pertain to, requires careful and thoughtful application of existing statistical tools. Using a combination of both simulated and real datasets, I highlight the key pitfalls associated with analysing physiological data from wild monitoring studies, and investigate issues of optimal study design, statistical power, and model precision and accuracy. I also recommend best practice approaches for dealing with their inherent limitations. This work will provide a concise, accessible roadmap for researchers looking to maximize the yield of information from complex and hard-won biologging datasets. [ABSTRACT FROM AUTHOR]

Cetaceans are iconic predators that serve as important indicators of marine ecosystem health. The Bremer Sub-Basin, south-western Australia, supports a diverse cetacean community including the largest documented aggregation of killer whales (Orcinus orca) in Australian waters. Knowledge of cetacean distributions is critical for managing the area's thriving ecotourism industry, yet is largely sporadic. Here we combined aerial with opportunistic ship-borne surveys during 2015–2017 to describe the occurrence of multiple cetacean species on a regional scale. We used generalised estimating equations to model variation in killer whale relative density as a function of both static and dynamic covariates, including seabed depth, slope, and chlorophyll a concentration, while accounting for autocorrelation. Encountered cetacean groups included: killer (n = 177), sperm (n = 69), long-finned pilot (n = 29), false killer (n = 2), and strap-toothed beaked (n = 1) whales, as well as bottlenose (n = 12) and common (n = 5) dolphins. Killer whale numbers peaked in areas of low temperatures and high primary productivity, likely due to seasonal upwelling of nutrient-rich waters supporting high prey biomass. The best predictive model highlighted potential killer whale 'hotspots' in the Henry, Hood, Pallinup and Bremer Canyons. This study demonstrates the value of abundance data from platforms of opportunity for marine planning and wildlife management in the open ocean. Every summer, a remote area 50 km off Bremer Bay, Western Australia, attracts many dozens of killer whales in what are currently the largest recorded numbers of the predator in Australian waters. New data collected aboard vessels show that this hotspot may be one of many found across an extensive network of underwater canyons where food is likely plentiful. Mapping whale aggregations is a key step in supporting and managing the region's thriving wildlife tourism industry. Photograph by Rebecca Wellard, Curtin University and Project ORCA. [ABSTRACT FROM AUTHOR]

Maternal environmental effects create lagged population responses to past environments. Although they are ubiquitous and vary in expression across taxa, it remains unclear if and how their presence alters competitive interactions in ecological communities.Here, we use a discrete‐time competition model to simulate how maternal effects alter competitive dynamics in fluctuating and constant environments. Further, we explore how omitting maternal effects alter estimates of known model parameters from observational time series data.Our simulations demonstrate that (i) maternal effects change competitive outcomes, regardless of whether competitors otherwise interact neutrally or exhibit non‐neutral competitive differences, (ii) the consequences of maternal effects for competitive outcomes are mediated by the temporal structure of environmental variation, (iii) even in constant conditions, competitive outcomes are influenced by species' maternal effects strategies, and (iv) in observational time series data, omitting maternal effects reduces variation explained by models and biases parameter estimates, including competition coefficients.Our findings demonstrate that the ecological consequences of maternal effects hinge on the competitive environment. Evolutionary biologists have long recognized that maternal effects can be an important but often overlooked strategy buffering populations from environmental change. We suggest that maternal effects are similarly critical to ecology and call for research into maternal effects as drivers of dynamics in populations and communities. [ABSTRACT FROM AUTHOR]

First‐order dynamic occupancy models (FODOMs) are a class of state‐space model in which the true state (occurrence) is observed imperfectly. An important assumption of FODOMs is that site dynamics only depend on the current state and that variations in dynamic processes are adequately captured with covariates or random effects. However, it is often difficult to understand and/or measure the covariates that generate ecological data, which are typically spatiotemporally correlated. Consequently, the non‐independent error structure of correlated data causes underestimation of parameter uncertainty and poor ecological inference. Here, we extend the FODOM framework with a second‐order Markov process to accommodate site memory when covariates are not available. Our modeling framework can be used to make reliable inference about site occupancy, colonization, extinction, turnover, and detection probabilities. We present a series of simulations to illustrate the data requirements and model performance. We then applied our modeling framework to 13 yr of data from an amphibian community in southern Arizona, USA. In this analysis, we found residual temporal autocorrelation of population processes for most species, even after accounting for long‐term drought dynamics. Our approach represents a valuable advance in obtaining inference on population dynamics, especially as they relate to metapopulations. [ABSTRACT FROM AUTHOR]

Aims: This study investigates how precipitation, temperature and seasonality (as a proxy of plant productivity) affect the temporal and spatial variability of soil CO2 efflux in two dry semiarid grasslands with different degrees of land degradation. Methods: We measured soil CO2 efflux over four years under plant, biological soil crust and bare soil patches and estimated annual soil carbon losses in both, a natural and a degraded grassland, by means of generalised additive mixed models considering temporal autocorrelation in the data. Results: Soil CO2 efflux ranged from 0.08 to 3.70 and from 0.10 to 3.01 μmol CΟ2 m−2 s−1 in the natural and degraded grasslands, respectively. Daily soil CO2 efflux was mostly affected by moisture in the degraded grassland (25.4%), while in the natural grassland was affected jointly by seasonality, temperature and moisture (27.5%). Overall, the highest soil carbon fluxes were measured in soils covered by biological soil crusts (1.24 ± 0.02 and 1.10 ± 0.02) and the lowest in bare soils (1.11 ± 0.02 and 0.82 ± 0.02 μmol CΟ2−2 s−1) in the natural and degraded sites, respectively. Cumulative soil carbon fluxes were mainly driven by temperature and previous precipitation (over three months). The highest soil carbon losses were estimated in the driest year (2009) and the lowest in the wettest (2010) with almost twice the amount of rainfall. The main difference between these years was the timing of the events that mostly occurred in the moments of maximum plant activity with optimum temperatures in spring in the dry year. Conclusions: Changes in precipitation patterns will affect soil carbon fluxes more than rainfall amount, particularly in degraded grasslands. Therefore, considering all climate drivers together with plant activity is essential to predict how climate change will affect soil biological processes in drylands. [ABSTRACT FROM AUTHOR]

Substantial environmental change can force a population onto a path towards extinction, but under some conditions, adaptation by natural selection can rescue the population and allow it to persist. This process, known as evolutionary rescue, is believed to be less likely to occur with greater magnitudes of random environmental fluctuations because environmental variation decreases expected population size, increases variance in population size and increases evolutionary lag. However, previous studies of evolutionary rescue in fluctuating environments have only considered scenarios in which evolutionary rescue was likely to occur. We extend these studies to assess how baseline extinction risk (which we manipulated via changes in the initial population size, degree of environmental change or mutation rate) influences the effects of environmental variation on evolutionary rescue following an abrupt environmental change. Using a combination of analytical models and stochastic simulations, we show that autocorrelated environmental variation hinders evolutionary rescue in low-extinction-risk scenarios but facilitates rescue in high-risk scenarios. In these high-risk cases, the chance of a run of good years counteracts the otherwise negative effects of environmental variation on evolutionary demography. These findings can inform the development of effective conservation practices that consider evolutionary responses to abrupt environmental changes. [ABSTRACT FROM AUTHOR]

One of the largest causes of fluctuations in the size and structure of populations is changes in the environment. In nature, these changes are often temporally autocorrelated and the strength and direction of the autocorrelation can affect population dynamics. These effects are mediated by complex, simultaneously occurring ecological and evolutionary processes, such as phenotypic plasticity and selection. Determining how these processes interact to affect responses of different life histories to autocorrelated environmental fluctuations is of paramount importance to infer which taxa are likely to go extinct or become invasive under global change. Here, we assessed the effect of autocorrelation in environmental states on the trait and population dynamics of different life histories using an evolutionary explicit individual- based modeling approach. We found that, in general, higher positive temporal autocorrelation caused more variation in population size. Fast life histories were more affected than slow ones as they were able to adapt more quickly to varying environmental optima and therefore experienced larger initial decreases in population size when optima changed. Including adaptive phenotypic plasticity buffered the effects of autocorrelation on population dynamics while nonadaptive plasticity amplified them, especially for slow life histories, which recovered less from maladaptation. This study highlights that integration of phenotypic plasticity, selection, and population dynamics is important to improve our understanding of how different life histories deal with autocorrelated climate variability. [ABSTRACT FROM AUTHOR]

Population dynamics are typically temporally autocorrelated: population sizes are positively or negatively correlated with past population sizes. Previous studies have found that positive temporal autocorrelation increases the risk of extinction due to 'inertia' that prolongs downward fluctuations in population size. However, temporal autocorrelation has not yet been analyzed at the level of life cycle transitions. We developed an R package, colorednoise, which creates stochastic matrix population projections with distinct temporal autocorrelation values for each matrix element. We used it to analyze long‐term demographic data on 25 populations from the COMADRE and COMPADRE databases and simulate their stochastic dynamics. We found a broad range of temporal autocorrelation across species, populations and life cycle stages. The number of stage‐classes in the matrix strongly affected the temporal autocorrelation of the growth rate. In the plant populations, reproduction transitions had more negative temporal autocorrelation than survival transitions, and matrices dominated by positive temporal autocorrelation had higher extinction risk, while in animal populations transition type was not associated with noise color. Our results indicate that temporal autocorrelation varies across life cycle transitions, even among populations of the same species. We present the colorednoise package for researchers to analyze the temporal autocorrelation of structured demographic rates. [ABSTRACT FROM AUTHOR]

The Allee threshold, the critical population density separating growth from decline in populations experiencing strong Allee effects, can vary over space and time but few empirical studies have examined this variation. A lack of geographically extensive, long-term studies on low density population dynamics makes studying variability in Allee effects difficult. We used North American gypsy moth population data from 1996 to 2016 to quantify Allee thresholds in 11 regions of the invasion front. Allee thresholds spanned a continuum from being undetectable due to strong population growth at all densities, to being unmeasurable because populations declined across all densities. The lag-1 temporal autocorrelation in Allee thresholds tended to be negative and spatial synchrony in Allee thresholds extended no further than adjacent regions. This work furthers understanding of spatiotemporal variation in Allee effects using extensive empirical data at the range edge of an invasive insect. [ABSTRACT FROM AUTHOR]

Objective: This article introduces Bayesian spatial–temporal modeling for social and health science research. We use the World Bank's World Development Indicators data on youth unemployment and HIV risk in Africa to illustrate the utility of the Bayesian paradigm in modeling space–time changes in outcomes. Method: Data on adolescents and young adults were collected in 36 African countries from 1991 to 2014. We examined associations between HIV risk and youth unemployment rates using 16 Bayesian Poisson models incorporating spatial and temporal autocorrelations. Results: The best fit to the data was the model with spatially uncorrelated heterogeneity, temporally correlated random-walk autocorrelation, and spatial–temporal interaction. HIV risk factors are spatially uncorrelated across 36 countries but temporally correlated (i.e., country and time interaction) over the data collection period. The relationship between HIV risk and unemployment rate is statistically nonsignificant because of large spatial–temporal variations. Conclusions: This article demonstrates the capacity of Bayesian modeling to incorporate spatial (neighborhood) and temporal (historical) information to reflect not only the influences of space and time but also their interactions on the phenomenon of interest. The Bayesian framework holds great promise for improving the dynamic targeting of interventions and strategies to achieve desired outcomes. [ABSTRACT FROM AUTHOR]

Site occupancy‐detection models (SODMs) are statistical models widely used for biodiversity surveys where imperfect detection of species occurs. For instance, SODMs are increasingly used to analyse environmental DNA (eDNA) data, taking into account the occurrence of both false‐positive and false‐negative errors. However, species occurrence data are often characterized by spatial and temporal autocorrelation, which might challenge the use of standard SODMs. Here we reviewed the literature of eDNA biodiversity surveys and found that most of studies do not take into account spatial or temporal autocorrelation. We then demonstrated how the analysis of data with spatial or temporal autocorrelation can be improved by using a conditionally autoregressive SODM, and show its application to environmental DNA data. We tested the autoregressive model on both simulated and real data sets, including chronosequences with different degrees of autocorrelation, and a spatial data set on a virtual landscape. Analyses of simulated data showed that autoregressive SODMs perform better than traditional SODMs in the estimation of key parameters such as true‐/false‐positive rates and show a better discrimination capacity (e.g., higher true skill statistics). The usefulness of autoregressive SODMs was particularly high in data sets with strong autocorrelation. When applied to real eDNA data sets (eDNA from lake sediment cores and freshwater), autoregressive SODM provided more precise estimation of true‐/false‐positive rates, resulting in more reasonable inference of occupancy states. Our results suggest that analyses of occurrence data, such as many applications of eDNA, can be largely improved by applying conditionally autoregressive specifications to SODMs. [ABSTRACT FROM AUTHOR]

Abstract: The dynamics of foundation species in ecosystems are key to the fate of many species. Kelp forests are foundation species in temperate ocean ecosystems and contribute to carbon storage, macronutrient dynamics, primary production and biodiversity of a myriad of associated species. Downward trends in their abundance globally have been of concern. We analysed 26 years of aerial censuses (1989–2015) of two canopy kelp species in Washington State (USA) waters. We compared these modern censuses with censuses in 1911 and 1912 to determine the persistence of kelp cover over the past century. Using Autoregressive Integrated Moving Average (ARIMA) models, we compared kelp dynamics with likely environmental drivers, including local environmental variables and ocean indices for this region. Kelp remains at historic levels in many areas, although some eastern populations in proximity to greater human populations are the exception to this pattern. Over the last 26 years, kelp abundance showed high spatial autocorrelation in western areas of Straits of Juan de Fuca, with more variable populations in the annual species and eastward towards Puget Sound. Both species covaried positively in their abundance throughout most of the study area, suggesting that environmental factors rather than competition, drove their dynamics. The population dynamics of these kelp species showed that the abundance 1 year previously was an important predictor, and cyclic dynamics were not indicated using ARIMA models. Kelp abundance correlated inversely with the Pacific Decadal Oscillation and the Oceanic Nino Index, and positively with the North Pacific Gyre Oscillation, indicating that large‐scale processes associated with colder seawater temperatures were associated with greater relative abundance of kelp. Synthesis. Kelp beds in the northern California Current Large Marine Ecosystem have mostly remained persistent over the past century and over many kilometres, but some areas may have decreased in abundance. The sensitivity of these populations to indices of ocean climate, our demonstration that a historic 93‐year sea surface temperature record (Race Rocks, Canada) showed a 0.72°C increase and the classification of some areas as high variability‐low abundance, suggest that the viability of these foundational species remain a concern into the future. [ABSTRACT FROM AUTHOR]

As global environmental conditions continue to change at an unprecedented rate, many species will experience increases in natural and anthropogenic stress. Generally speaking, selection is expected to favor adaptations that reduce the negative impact of environmental stress (i.e., stress tolerance). However, natural environmental variables typically fluctuate, exhibiting various degrees of temporal autocorrelation, known as environmental colors, which may complicate evolutionary responses to stress. Here we combine experiments and theory to show that temporal environmental autocorrelation can determine long-term evolutionary responses to stress without affecting the total amount of stress experienced over time. Experimental evolution of RNA virus lineages in differing environmental autocorrelation treatments agreed closely with predictions from our theoretical models that stress tolerance is favored in less autocorrelated (whiter) environments but disfavored in more autocorrelated (redder) environments. This is explained by an interaction between environmental autocorrelation and a phenotypic trade-off between stress tolerance and reproductive ability. The degree to which environmental autocorrelation influences evolutionary trajectories depends on the shape of this trade-off as well as the relative level of tolerance exhibited by novel mutants. These results suggest that long-term evolutionary dynamics depend not only on the overall strength of selection but also on the way that selection is distributed over time. [ABSTRACT FROM AUTHOR]

We investigated correlates of long-term temporal variation in the beta diversity of macrophytes, sedentary fish, and migratory fish communities in the Upper Paraná River floodplain. Two metrics of among-site variation in community composition were calculated in up to 45 sampling periods over 12 years for each biological group. We then tested the following beta diversity correlates: richness and proportion of non-native species, ecosystem productivity proxies, environmental heterogeneity, and hydrological regime proxies. Despite the uncertainty regarding the best model, we found that environmental heterogeneity was the most consistent predictor of beta diversity variation. Non-native species (richness or proportional abundance), productivity, and hydrology were not consistently correlated with beta diversity. However, models results suggest that the likely intensification of threats caused by oligotrophication, non-native species spread, and damming may trigger the effects of these predictors. Thus, we suggest that continuation of the long-term ecological study in the Upper Paraná River floodplain is key to our better understanding of the role of these processes in beta diversity variation. [ABSTRACT FROM AUTHOR]

Transgenerational environmental effects can trigger strong phenotypic variation. However, it is unclear how cues from different preceding generations interact. Also, little is known about the genetic variation for these life history traits., Here, we present the effects of grandparental and parental mild heat, and their combination, on four traits of the third-generation phenotype of 14 Arabidopsis thaliana genotypes. We tested for correlations of these effects with climate and constructed a conceptual model to identify the environmental conditions that favour the parental effect on flowering time., We observed strong evidence for genotype-specific transgenerational effects. On average, A. thaliana accustomed to mild heat produced more seeds after two generations. Parental effects overruled grandparental effects in all traits except reproductive biomass. Flowering was generally accelerated by all transgenerational effects. Notably, the parental effect triggered earliest flowering in genotypes adapted to dry summers. Accordingly, this parental effect was favoured in the model when early summer heat terminated the growing season and environments were correlated across generations., Our results suggest that A. thaliana can partly accustom to mild heat over two generations and genotype-specific parental effects show non-random evolutionary divergence across populations that may support climate change adaptation in the Mediterranean. [ABSTRACT FROM AUTHOR]

Temporal data describe processes and phenomena that evolve over time. In many real-world applications temporal data are characterized by temporal autocorrelation, which expresses the dependence of time-stamped data over a certain a time lag. Often such processes and phenomena are characterized by evolving complex entities, which we can represent with evolving networks of data. In this scenario, a task that deserves attention is regression inference in temporal network data. In this paper, we investigate how to improve the predictive inference on network data by accommodating temporal autocorrelation of the historical data in the learning process of the prediction models. Historical data is a type of temporal data where most part of the elements has been already stored. In practice, we study how to explicitly consider the influence of data of a network observed in the past, to enhance the prediction on the same network observed at the present. The proposed approach relies on a model ensemble built with individual predictors learned on historical network data. The predictors are trained from summary networks, which synthesize the effect of the autocorrelation in distinct sequences of network observations. Summary networks are identified with a sliding window model. Finally, the model ensemble combines together the predictors with a weighting schema, which reflects the degree of influence of a predictor with respect to the network observed at the present. So, we aim at accommodating the temporal autocorrelation both in the data and in the prediction model. Empirical evaluation demonstrates that the proposed approach can boost regression performance in real-world network data. [ABSTRACT FROM AUTHOR]

The impact of developments in weather forecasting is measured using forecast verification, but many developments, though useful, have impacts of less than 0.5% on medium-range forecast scores. Chaotic variability in the quality of individual forecasts is so large that it can be hard to achieve statistical significance when comparing these 'smaller' developments to a control. For example, with 60 separate forecasts and requiring a 95% confidence level, a change in quality of the day-5 forecast needs to be larger than 1% to be statistically significant using a Student's t-test. The first aim of this study is simply to illustrate the importance of significance testing in forecast verification, and to point out the surprisingly large sample sizes that are required to attain significance. The second aim is to see how reliable are current approaches to significance testing, following suspicion that apparently significant results may actually have been generated by chaotic variability. An independent realisation of the null hypothesis can be created using a forecast experiment containing a purely numerical perturbation, and comparing it to a control. With 1885 paired differences from about 2.5 yr of testing, an alternative significance test can be constructed that makes no statistical assumptions about the data. This is used to experimentally test the validity of the normal statistical framework for forecast scores, and it shows that the naive application of Student's t-test does generate too many false positives (i.e. false rejections of the null hypothesis). A known issue is temporal autocorrelation in forecast scores, which can be corrected by an inflation in the size of confidence range, but typical inflation factors, such as those based on an AR(1) model, are not big enough and they are affected by sampling uncertainty. Further, the importance of statistical multiplicity has not been appreciated, and this becomes particularly dangerous when many experiments are compared together. For example, across three forecast experiments, there could be roughly a 1 in 2 chance of getting a false positive. However, if correctly adjusted for autocorrelation, and when the effects of multiplicity are properly treated using a Šidák correction, the t-test is a reliable way of finding the significance of changes in forecast scores. [ABSTRACT FROM AUTHOR]

Multiple factors complicate the analysis of animal telemetry location data. Recent advancements address issues such as temporal autocorrelation and telemetry measurement error, but additional challenges remain. Difficulties introduced by complicated error structures or barriers to animal movement can weaken inference. We propose an approach for obtaining resource selection inference from animal location data that accounts for complicated error structures, movement constraints, and temporally autocorrelated observations. We specify a model for telemetry data observed with error conditional on unobserved true locations that reflects prior knowledge about constraints in the animal movement process. The observed telemetry data are modeled using a flexible distribution that accommodates extreme errors and complicated error structures. Although constraints to movement are often viewed as a nuisance, we use constraints to simultaneously estimate and account for telemetry error. We apply the model to simulated data, showing that it outperforms common ad hoc approaches used when confronted with measurement error and movement constraints. We then apply our framework to an Argos satellite telemetry data set on harbor seals (Phoca vitulina) in the Gulf of Alaska, a species that is constrained to move within the marine environment and adjacent coastlines. [ABSTRACT FROM AUTHOR]

Activity budget data are essential for determining behavioral responses to physiological and ecological variables. Yet, few studies are available to investigate the robustness, accuracy, and biases of the methods used to estimate activity budgets for cetaceans. In this study, we compare activity budgets of 55 adult female bottlenose dolphins in Shark Bay, Australia derived from two methods: surveys (n = 6,903) and focal follows (n = 1,185, totaling 2,721 h of observation). Activity budgets estimated from survey data differed in all behavioral states compared to focal follow data. However, when controlling for temporal autocorrelation, only time spent socializing and time spent traveling remained disparate between the methods. To control for biases associated with assigning group-level behavior to individuals, we also compared survey and focal follow activity budgets for lone females. Here we found differences between methods in time spent foraging and traveling regardless of whether we controlled for temporal autocorrelation, which suggests detection biases likely play a role in explaining differences in activity budget estimates between the two methodologies. Our results suggest that surveys are less representative of individual-level activity budgets, and thus, when individual-level knowledge about behavior is needed, focal follows are preferred. [ABSTRACT FROM AUTHOR]

The aim of the study was to determine the characteristics of temporal and space-time autocorrelation of pollen counts of Alnus, Betula, and Corylus in the air of eight cities in Poland. Daily average pollen concentrations were monitored over 8 years (2001-2005 and 2009-2011) using Hirst-designed volumetric spore traps. The spatial and temporal coherence of data was investigated using the autocorrelation and cross-correlation functions. The calculation and mathematical modelling of 61 correlograms were performed for up to 25 days back. The study revealed an association between temporal variations in Alnus, Betula, and Corylus pollen counts in Poland and three main groups of factors such as: (1) air mass exchange after the passage of a single weather front (30-40 % of pollen count variation); (2) long-lasting factors (50-60 %); and (3) random factors, including diurnal variations and measurements errors (10 %). These results can help to improve the quality of forecasting models. [ABSTRACT FROM AUTHOR]

Modeling population dynamics while accounting for imperfect detection is essential to monitoring programs. Distance sampling allows estimating population size while accounting for imperfect detection, but existing methods do not allow for estimation of demographic parameters. We develop a model that uses temporal correlation in abundance arising from underlying population dynamics to estimate demographic parameters from repeated distance sampling surveys. Using a simulation study motivated by designing a monitoring program for Island Scrub-Jays (Aphelocoma insularis), we investigated the power of this model to detect population trends. We generated temporally autocorrelated abundance and distance sampling data over six surveys, using population rates of change of 0.95 and 0.90. We fit the data generating Markovian model and a mis-specified model with a log-linear time effect on abundance, and derived post hoc trend estimates from a model estimating abundance for each survey separately. We performed these analyses for varying numbers of survey points. Power to detect population changes was consistently greater under the Markov model than under the alternatives, particularly for reduced numbers of survey points. The model can readily be extended to more complex demographic processes than considered in our simulations. This novel framework can be widely adopted for wildlife population monitoring. [ABSTRACT FROM AUTHOR]

Tropical timber production forests have the potential to harbour many species, but landscape-level management strategies for minimizing extinctions are lacking., To develop relevant conservation guidelines, we investigated how harvest plans with different spatiotemporal patterns affected the persistence of sessile species with different traits and preharvest characteristics. We confronted this problem via an individual-based simulation model., We explored several harvest plans with different levels of spatiotemporal aggregation, but equivalent total harvest area, and found that extinction probabilities for each plan varied with species traits and predisturbance characteristics. Most notably, plans with large contiguous harvest units yielded particularly high extinction probabilities for dispersal-limited habitat specialists with clustered preharvest distributions. Differences between plans were small for some types of species, but highly aggregated plans yielded the highest extinction rates for all extinction-prone groups., Synthesis and applications. Our simulations suggest that reducing the size of contiguous harvest units, even while total harvest area remains constant, may reduce extinction rates in tropical production forests (assuming that road-related threats can be effectively managed). Our findings have important implications for tropical conservation efforts and also provide general insight into the compositional effects of disturbances with different spatiotemporal characteristics. [ABSTRACT FROM AUTHOR]

This article reviews four area-level linear mixed models that borrow strength by exploiting the possible correlation among the neighboring areas or/and past time periods. Its main goal is to study if there are efficiency gains when a spatial dependence or/and a temporal autocorrelation among random-area effects are included into the models. The Fay–Herriot estimator is used as benchmark. A design-based simulation study based on real data collected from a longitudinal survey conducted by a statistical office is presented. Our results show that models that explore both spatial and chronological association considerably improve the efficiency of small area estimates. [ABSTRACT FROM AUTHOR]

Population ecologists often use telemetry to estimate the probability that an animal is available for sampling (availability probability). Such estimates are paramount for generating reliable estimates of animal abundance and are sometimes of biological interest in their own right (e.g. for characterizing pinniped haul-out behaviour or landbird singing frequency)., We consider the estimation of availability probability from telemetry data when there are missing records. When records are missing-completely-at-random (i.e. not influenced by whether an animal is available for sampling or other covariates), approaches that censor missing records result in unbiased estimates of availability probability. However, censoring such records can result in bias when data are not missing-completely-at-random., We present a novel Bayesian temporal availability model that can be used to explicitly account for the process by which missing data arise. Our approach couples an underlying model for the partially observed availability process together with an observation model that allows state-dependent probabilities of missingness. This approach provides relatively unbiased estimates of availability probability even in the face of violations of the missing-at-random assumption. A new R package, TempOcc, is introduced to automate estimation., We demonstrate the utility of our approach by analysing both simulated data and hourly satellite telemetry records for 157 ice-associated seals in the Bering Sea, for which 27% of records were missing. In this case, we were interested in generating unbiased estimates of haul-out probabilities. Such probabilities are necessary when estimating abundance from aerial survey counts and assessing possible changes in seal phenology., Our analysis indicates that the missing-at-random assumption is indeed violated in telemetry studies of phocid seals in the Bering Sea. However, estimates of availability probability do not depend critically on this assumption., We recommend that ecologists routinely consider the implications of violating the missing-at-random assumption when estimating availability probabilities. Where possible, the modelling framework articulated in this study can be used to diagnose and correct for violations of the missing-at-random assumption. [ABSTRACT FROM AUTHOR]

Ecologists have long studied the effects of disturbance on species diversity. More recently, researchers have become interested in understanding how the various aspects of disturbance interact to influence community diversity. While the effects of temporal autocorrelation have also received some attention, the potential for manipulating disturbance autocorrelation to achieve management goals has not been theoretically explored., We consider the interactions between temporal autocorrelation of disturbance occurrence and disturbance intensity at varying disturbance frequencies. Using an annual plant model, we show that when intensity and frequency are kept constant, changing the temporal autocorrelation of disturbance occurrence can also affect competitive outcomes. Additionally, we show that when species coexist, the degree of autocorrelation can affect which species reaches higher densities., We describe several examples (including prescribed burning, grazing and mowing) that outline how manipulation of temporal autocorrelation may be used to achieve conservation and eradication goals at no additional cost., Synthesis and applications. Our results provide important insights into, and have potential application to, land management and conservation. While changing the intensity and frequency of human-induced disturbances can be costly, adjusting the temporal autocorrelation of disturbance occurrence may be considered a 'no-cost manipulation'. In instances where a land manager lacks the funds or resources to manipulate other aspects of disturbance, such as intensity and frequency, changing the temporal autocorrelation may provide an effective, economical alternative. [ABSTRACT FROM AUTHOR]

The relative importance of environmental colour for extinction risk compared with other aspects of environmental noise (mean and interannual variability) is poorly understood. Such knowledge is currently relevant, as climate change can cause the mean, variability and temporal autocorrelation of environmental variables to change. Here, we predict that the extinction risk of a shorebird population increases with the colour of a key environmental variable: winter temperature. However, the effect is weak compared with the impact of changes in the mean and interannual variability of temperature. Extinction risk was largely insensitive to noise colour, because demographic rates are poor in tracking the colour of the environment. We show that three mechanisms--which probably act in many species--can cause poor environmental tracking: (i) demographic rates that depend nonlinearly on environmental variables filter the noise colour, (ii) demographic rates typically depend on several environmental signals that do not change colour synchronously, and (iii) demographic stochasticity whitens the colour of demographic rates at low population size. We argue that the common practice of assuming perfect environmental tracking may result in overemphasizing the importance of noise colour for extinction risk. Consequently, ignoring environmental autocorrelation in population viability analysis could be less problematic than generally thought. [ABSTRACT FROM AUTHOR]

The article investigates the connection of spatial distribution, climate and soil moisture-holding capacity in plague epizootics in black-tailed prairie dogs, Cynomys ludovicianus, in northeastern Colorado. The study uses regression model that was parameterized using data from 1981 to 2003 and validated with data from 2004 to 2005. The study suggests that plague occurrence depends upon direct climatic effects on flea vectors and the plague pathogen.

The probability of, and time to, fixation of a mutation in a population has traditionally been studied by the classic Wright-Fisher model where population size is constant. Recent theoretical expansions have covered fluctuating populations in various ways but have not incorporated models of how the environment fluctuates in combination with different levels of density-compensation affecting fecundity. We tested the hypothesis that the probability of, and time to, fixation of neutral, advantageous and deleterious mutations is dependent on how the environment fluctuates over time, and on the level of density-compensation. We found that fixation probabilities and times were dependent on the pattern of autocorrelation of carrying capacity over time and interacted with density-compensation. The pattern found was most pronounced at small population sizes. The patterns differed greatly depending on whether the mutation was neutral, advantageous, or disadvantageous. The results indicate that the degree of mismatch between carrying capacity and population size is a key factor, rather than population size per se, and that effective population sizes can be very low also when the census population size is far above the carrying capacity. This study highlights the need for explicit population dynamic models and models for environmental fluctuations for the understanding of the dynamics of genes in populations. [ABSTRACT FROM AUTHOR]

Ecology Letters (2010) 13: 1182–1197 Explaining variation in population growth rates is fundamental to predicting population dynamics and population responses to environmental change. In this study, we used matrix population models, which link birth, growth and survival to population growth rate, to examine how and why population growth rates vary within and among 50 terrestrial plant species. Population growth rates were more similar within species than among species; with phylogeny having a minimal influence on among-species variation. Most population growth rates decreased over the observation period and were negatively autocorrelated between years; that is, higher than average population growth rates tended to be followed by lower than average population growth rates. Population growth rates varied more through time than space; this temporal variation was due mostly to variation in post-seedling survival and for a subset of species was partly explained by response to environmental factors, such as fire and herbivory. Stochastic population growth rates departed from mean matrix population growth rate for temporally autocorrelated environments. Our findings indicate that demographic data and models of closely related plant species cannot necessarily be used to make recommendations for conservation or control, and that post-seedling survival and the sequence of environmental conditions are critical for determining plant population growth rate. [ABSTRACT FROM AUTHOR]

The article presents the study conducted by the researchers regarding the available variation in detecting Florida Grasshopper Sparrow. The study aims to determine the impact of the sources of variation in the singing probability of the birds using the multi-day acoustic recordings. The two important sources are the changes in the singing probability with short-term differences and time of day. The result also shows that monitoring needs to consider the peak singing activity of the Florida Grasshopper Sparrows.

The spatial and temporal variation in population sizes of animal colonies are rarely studied simultaneously. I examined factors determining colony size (number of nests) for 23 colonies from the only breeding population of rook Corvus frugilegus in Spain over 7 years. Population sizes within colonies were highly predictable over time, with autocorrelations up to a distance (lag) of 6 years. Autoregressive mixed models were used to explain colony size as a function of environmental factors, while controlling for temporal autocorrelation. These factors included refuse tips, widely used as food resource, and a derived variable that incorporated the two factors most often related to avian colony size (inter-colony competition and foraging habitat around colonies). Autoregressive models provided a better fit to the data than models which did not consider temporal autocorrelation. The information-theoretic (AICc-based) approach revealed uncertainty in the selection of the best model explaining colony-size, but relatively strong support for certain variables. The highest weights of evidence were for year ( ω i = 0.90) and the number of competitors per unit of foraging habitat (i.e., derived variable; ω i = 0.63), showing that the size of rook colonies in Spain was negatively affected by inter-colony competition relative to the foraging habitat surrounding the colonies. This variable measured within a 6-km radius from the colonies had ~30 times higher weight of evidence (more plausible) than the same variable measured within 3 km, indicating that food limitation may occur outside the breeding period. Sizes of colonies tended to decrease when distance between the colony and the nearest refuse tip increased. There was some evidence supporting the idea that the effect of the number of competitors per unit of foraging habitat on colony size varied from year to year, but statistical power was weak. These findings suggest that variation in number of rook nests within colonies reflects spatial and temporal heterogeneity of net food via both inter-colony competition and foraging habitat around the colony. [ABSTRACT FROM AUTHOR]

Computer models of flow and transport processes in critical or vulnerable reaches of rivers have become essential tools in assessing the long-term sustainable management of river basins. A generic long-standing source of uncertainty in the output from these models arises from the difficulty in estimating flow rates at the upstream boundary of the reach. When the process being modeled relies on an accurate description of the autocorrelation in the flow, such as in sediment transport, and predictions are required for future changed climatic conditions, this difficulty becomes acute. One currently popular solution is to combine stochastic weather generators with catchment hydrology models to simulate hydrographs. This paper offers an alternative solution, which bypasses the complexities of this approach and the extra uncertainties that arise from imposing restrictive predefined model structures. The proposed method uses an autoregressive multinomial logit model to link temperature and precipitation catchment data to flow data of the River Tees in the northeast United Kingdom. This model accounts for temporal autocorrelation by using the previous day's observation and other variables as explanatory variables. Incorporating basin area average temperature and precipitation allows climate change predictions from regional climate models to be used when predicting flows. The successful application of the method is demonstrated for the River Tees in northeast United Kingdom. [ABSTRACT FROM AUTHOR]

1. Patch dynamics is a new, potentially unifying mechanism for the explanation of tree-grass coexistence in savannas. In this scale-explicit paradigm, savannas consist of patches in which a cyclical succession between woody and grassy dominance proceeds spatially asynchronously. The growing ecological and economic problem of shrub encroachment is a natural transient phase in this cycle. 2. An important step towards understanding patterns at the landscape scale is achieved by investigating mechanisms at a smaller scale. We developed the spatially explicit individual-based simulation model SATCHMO to test the null hypothesis that cyclical succession cannot emerge from a realistic patch scale simulation model of the population dynamics of savanna woody species. 3. We calculated the partial temporal autocorrelation coefficient for 100 simulated time series of shrub cover over 500 years for time lags of up to 200 years to establish the existence and duration of successional cycles. We found a significant positive autocorrelation indicating the existence of cycles with a typical duration of about 33 years. 4. The shrub size frequency distributions over the course of a cycle showed shifts from dominance of small shrub sizes towards larger sizes during the increasing phase of a cycle and the reverse in the declining phase. This supports the three phase explanation as follows: (i) an initial phase when spatially and temporally overlapping favourable conditions lead to mass recruitment of shrubs; (ii) a build-up phase when the shrub cohort grows; and (iii) a break-down phase when increased competition due to crowding and unfavourable conditions lead to the break-down of the shrub cohort. The frequency distribution of shrub age at death over 10 simulations was also in agreement with this explanation. 5. We investigated the relationship between shrub cover, annual precipitation and time-lagged shrub cover to identify the driver of the cyclical successions. More than 90% of the variation in shrub cover was explained by shrub cover of the previous year, precipitation, and their interaction. 6. With the demonstration of precipitation-driven cyclical succession at the patch scale, we show that the mechanistic, temporal component of patch dynamics can be used to explain tree-grass coexistence in semi-arid savannas. [ABSTRACT FROM AUTHOR]

Soil moisture plays a key role in water, carbon and energy exchanges between the land surface and the atmosphere. Therefore, a better representation of this variable in the Land-Surface Models (LSMs) used in climate modelling could significantly reduce the uncertainties associated with future climate predictions. In this study, the ESA-CCI soil moisture (SM) combined product (v4.2) has been confronted to the simulated top-first layers/cms of the ORCHIDEE LSM (the continental part of the IPSL Earth System Model) for the years 2008-2016, to evaluate its potential to improve the model using data assimilation techniques. The ESA-CCI data are first rescaled to match the climatology of the model and the signal representative depth is selected. Results are found to be relatively consistent over the first 20 cm of the model. Strong correlations found between the model and the ESA-CCI product show that ORCHIDEE can adequately reproduce the observed SM dynamics. As well as considering two different atmospheric forcings to drive the model, we consider two different model parameterizations related to the soil resistance to evaporation. The correlation metric is shown to be more sensitive to the choice of meteorological forcing than to the choice of model parameterization. Therefore, the metric is not optimal in highlighting structural deficiencies in the model. In contrast, the temporal autocorrelation metric is shown to be more sensitive to this model parameterization, making the metric a potential candidate for future data assimilation experiments. [ABSTRACT FROM AUTHOR]