Seasonality, density dependence, and spatial population synchrony.

Studies of spatial population synchrony constitute a central approach for understanding the drivers of ecological dynamics. Recently, identifying the ecological impacts of climate change has emerged as a new important focus in population synchrony studies. However, while it is well known that climatic seasonality and sequential density dependence influences local population dynamics, the role of season-specific density dependence in shaping large-scale population synchrony has not received attention. Here, we present a widely applicable analytical protocol that allows us to account for both season and geographic context-specific density dependence to better elucidate the relative roles of deterministic and stochastic sources of population synchrony, including the renowned Moran effect. We exemplify our protocol by analyzing time series of seasonal (spring and fall) abundance estimates of cyclic rodent populations, revealing that season-specific density dependence is a major component of population synchrony. By accounting for deterministic sources of synchrony (in particular season-specific density dependence), we are able to identify stochastic components. These stochastic components include mild winter weather events, which are expected to increase in frequency under climate warming in boreal and Arctic ecosystems. Interestingly, these weather effects act both directly and delayed on the vole populations, thus enhancing the Moran effect. Our study demonstrates how different drivers of population synchrony, presently altered by climate warming, can be disentangled based on seasonally sampled population time-series data and adequate population models.

Short-versus long-term effects of nitrogen addition and warming on soil nitrogen mineralization and leaching in a grass-dominated old field.

Increased atmospheric nitrogen (N) deposition and climate warming are both anticipated to influence the N dynamics of northern temperate ecosystems substantially over the next century. In field experiments with N addition and warming treatments, cumulative treatment effects can be important for explaining variation in treatment effects on N dynamics over time; however, comparisons between data collected in the early vs. later years potentially can be confounded with interactions between treatment effects and inter-annual variation in environmental conditions or other factors. We compared the short-term versus long-term effects of N addition and warming on net N mineralization and N leaching in a grass-dominated old field using in situ soil cores. We added new N addition and warming plots (3 years old) to an existing field experiment (16 years old), which enabled comparison of the treatment effects at both time scales while controlling for potential inter-annual variation in other factors. For net N mineralization, there was a significant interaction between plot age and N addition over the growing season, and for extractable inorganic N there was a significant interaction between plot age and warming over winter. In both cases, the directions of the treatment effects differed among old and new plots. Moreover, the responses in the new plots differed from the responses observed previously when the 16-year-old plots had been new. These results demonstrate how inter-annual variation in responses, independent from cumulative treatment effects, can play an important role in interpreting long-term effects on soil N cycling in global change field experiments.

Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling.

A deep learning architecture, denoted as CNNsLSTM, is proposed for hourly rainfall-runoff modeling in this study. The architecture involves a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory (LSTM) network. In the proposed framework, multiple layers of the CNN component process long-term hourly meteorological time series data, while the LSTM component handles short-term meteorological time series data and utilizes the extracted features from the 1D-CNN. In order to demonstrate the effectiveness of the proposed approach, it was implemented for hourly rainfall-runoff modeling in the Ishikari River watershed, Japan. A meteorological dataset, including precipitation, air temperature, evapotranspiration, longwave radiation, and shortwave radiation, was utilized as input. The results of the proposed approach (CNNsLSTM) were compared with those of previously proposed deep learning approaches used in hydrologic modeling, such as 1D-CNN, LSTM with only hourly inputs (LSTMwHour), a parallel architecture of 1D-CNN and LSTM (CNNpLSTM), and the LSTM architecture, which uses both daily and hourly input data (LSTMwDpH). Meteorological and runoff datasets were separated into training, validation, and test periods to train the deep learning model without overfitting, and evaluate the model with an independent dataset. The proposed approach clearly improved estimation accuracy compared to previously utilized deep learning approaches in rainfall = runoff modeling. In comparison with the observed flows, the median values of the Nash-Sutcliffe efficiency for the test period were 0.455-0.469 for 1D-CNN, 0.639-0.656 for CNNpLSTM, 0.745 for LSTMwHour, 0.831 for LSTMwDpH, and 0.865-0.873 for the proposed CNNsLSTM. Furthermore, the proposed CNNsLSTM reduced the median root mean square error (RMSE) of 1D-CNN by 50.2%-51.4%, CNNpLSTM by 37.4%-40.8%, LSTMwHour by 27.3%-29.5%, and LSTMwDpH by 10.6%-13.4%. Particularly, the proposed CNNsLSTM improved the estimations for high flows (≧75th percentile) and peak flows (≧95th percentile). The computational speed of LSTMwDpH is the fastest among the five architectures. Although the computation speed of CNNsLSTM is slower than LSTMwDpH's, it is still 6.9-7.9 times faster than that of LSTMwHour. Therefore, the proposed CNNsLSTM would be an effective approach for flood management and hydraulic structure design, mainly under climate change conditions that require estimating hourly river flows using meteorological datasets.

Elephant rewilding affects landscape openness and fauna habitat across a 92-year period.

Trophic rewilding aims to promote biodiverse self-sustaining ecosystems through the restoration of ecologically important taxa and the trophic interactions and cascades they propagate. How rewilding effects manifest across broad temporal scales will determine ecosystem states; however, our understanding of post-rewilding dynamics across longer time periods is limited. Here we show that the restoration of a megaherbivore, the African savannah elephant (Loxodonta africana), promotes landscape openness (i.e., various measures of vegetation composition/complexity) and modifies fauna habitat and that these effects continue to manifest up to 92 years after reintroduction. We conducted a space-for-time floristic survey and assessment of 17 habitat attributes (e.g., floristic diversity and cover, ground wood, tree hollows) across five comparable nature reserves in South African savannah, where elephants were reintroduced between 1927 and 2003, finding that elephant reintroduction time was positively correlated with landscape openness and some habitat attributes (e.g., large-sized tree hollows) but negatively associated with others (e.g., large-sized coarse woody debris). We then indexed elephant site occurrence between 2006 and 2018 using telemetry data and found positive associations between site occurrence and woody plant densities. Taken alongside the longer-term space-for-time survey, this suggests that elephants are attracted to dense vegetation in the short term and that this behavior increases landscape openness in the long term. Our results suggest that trophic rewilding with elephants helps promote a semi-open ecosystem structure of high importance for African biodiversity. More generally, our results suggest that megafauna restoration represents a promising tool to curb Earth's recent ecological losses and highlights the importance of considering long-term ecological responses when designing and managing rewilding projects.

Seasonal and elevational variation in thermal ecology of the crevice-dwelling knob-scaled lizard Xenosaurus fractus from central-eastern Mexico.

There is strong covariation between the thermal physiology of ectothermic animals and their thermal environment. Spatial and temporal differences in the thermal environment across a species' range may result in changes in thermal preferences between populations of that species. Alternatively, thermoregulatory-based microhabitat selection can allow individuals to maintain similar body temperatures across a broad thermal gradient. Which strategy a species adopts is often dependent on taxon-specific levels of physiological conservatism or ecological context. Identifying which strategies species use in response to spatial and temporal variation in environmental temperatures requires empirical evidence, which then can support predictions as to how a species might respond to a changing climate. Here we present findings of our analyses of the thermal quality, thermoregulatory accuracy and efficiency for the lizard, Xenosaurus fractus, across an elevation-thermal gradient and over the temporal thermal variation associated with seasonal changes. Xenosaurus fractus is a strict crevice-dweller, a habitat that can buffer this lizard from extreme temperatures and is a thermal conformer (body temperatures reflect air and substrate temperatures). We found populations of this species differed in their thermal preferences along an elevation gradient and between seasons. Specifically, we found that habitat thermal quality, thermoregulatory accuracy and efficiency (all measures of how well the lizards' body temperatures compared to their preferred body temperatures) varied along thermal gradients and with season. Our findings indicate that this species has adapted to local conditions and shows seasonal flexibility in those spatial adaptations. Along with their strict crevice-dwelling habitat, these adaptations may provide some protection against a warming climate.

Scale-invariant sensitivity for multi-purpose water reservoirs management with temporal scale-dependent modeling.

High-resolution temporal data (e.g., daily) is valuable for the decision-making of water resources management because it more accurately captures fine-scale processes and extremes than the coarse temporal data (e.g., weekly or monthly). However, many studies rarely consider this superior suitability for water resource modeling and management; instead, they often use whichever data is more readily available. So far, no comparative investigations have been conducted to determine if access to different time-scale data would change decision-maker perceptions or the rationality of decision making. This study proposes a framework for assessing the impact of different temporal scales on water resource management and the performance objective's sensitivity to uncertainties. We built the multi-objective operation models and operating rules of a water reservoir system based on daily, weekly, and monthly scales, respectively, using an evolution multi-objective direct policy search. The temporal scales of the input variables (i.e., streamflow) affect both the model structures and the output variables. In exploring these effects, we reevaluated the temporal scale-dependent operating rules under uncertain streamflow sets generated from synthetic hydrology. Finally, we obtained the output variable's sensitivities to the uncertain factors at different temporal scales using the distribution-based sensitivity analysis method. Our results show that water management based on too coarse resolution might give decision makers the wrong perception because the effect of actual extreme streamflow process on the performance objectives is ignored. The streamflow uncertainty is more influential than the uncertainty associated with operating rules. However, the sensitivities are characterized by temporal scale invariance, as the differences of the sensitivity between different temporal scales are not obvious over the uncertainties in streamflow and thresholds. These results show that water management should consider the resolution-dependent effect of temporal scales for balancing modeling complexity and computational cost.

Acute, diel, and annual temperature variability and the thermal biology of ectotherms.

Global warming is increasing mean temperatures and altering temperature variability at multiple temporal scales. To better understand the consequences of changes in thermal variability for ectotherms it is necessary to consider thermal variation at different time scales (i.e., acute, diel, and annual) and the responses of organisms within and across generations. Thermodynamics constrain acute responses to temperature, but within these constraints and over longer time periods, organisms have the scope to adaptively acclimate or evolve. Yet, hypotheses and predictions about responses to future warming tend not to explicitly consider the temporal scale at which temperature varies. Here, focusing on multicellular ectothermic animals, we argue that consideration of multiple processes and constraints associated with various timescales is necessary to better understand how altered thermal variability because of climate change will affect ectotherms.

Temporal scale of habitat selection for large carnivores: Balancing energetics, risk and finding prey.

When navigating heterogeneous landscapes, large carnivores must balance trade-offs between multiple goals, including minimizing energetic expenditure, maintaining access to hunting opportunities and avoiding potential risk from humans. The relative importance of these goals in driving carnivore movement likely changes across temporal scales, but our understanding of these dynamics remains limited. Here we quantified how drivers of movement and habitat selection changed with temporal grain for two large carnivore species living in human-dominated landscapes, providing insights into commonalities in carnivore movement strategies across regions. We used high-resolution GPS collar data and integrated step selection analyses to model movement and habitat selection for African lions Panthera leo in Laikipia, Kenya and pumas Puma concolor in the Santa Cruz Mountains of California across eight temporal grains, ranging from 5 min to 12 hr. Analyses considered landscape covariates that are related to energetics, resource acquisition and anthropogenic risk. For both species, topographic slope, which strongly influences energetic expenditure, drove habitat selection and movement patterns over fine temporal grains but was less important at longer temporal grains. In contrast, avoiding anthropogenic risk during the day, when risk was highest, was consistently important across grains, but the degree to which carnivores relaxed this avoidance at night was strongest for longer term movements. Lions and pumas modified their movement behaviour differently in response to anthropogenic features: lions sped up while near humans at fine temporal grains, while pumas slowed down in more developed areas at coarse temporal grains. Finally, pumas experienced a trade-off between energetically efficient movement and avoiding anthropogenic risk. Temporal grain is an important methodological consideration in habitat selection analyses, as drivers of both movement and habitat selection changed across temporal grain. Additionally, grain-dependent patterns can reflect meaningful behavioural processes, including how fitness-relevant goals influence behaviour over different periods of time. In applying multi-scale analysis to fine-resolution data, we showed that two large carnivore species in very different human-dominated landscapes balanced competing energetic and safety demands in largely similar ways. These commonalities suggest general strategies of landscape use across large carnivore species.

Assessment of groundwater salinity using principal component analysis (PCA): a case study from Mewat (Nuh), Haryana, India.

In the present study, principal component analysis (PCA) is used to investigate the processes controlling groundwater salinity in the Mewat (Nuh) district, Haryana, India. Twenty groundwater samples were collected from salinity-affected areas in the March-April months of years 2018 and 2019 and were analyzed for chemical variables pH, EC, Ca2+, Mg2+, Na+, K+, [Formula: see text], Cl-, SO42-, [Formula: see text], TDS, and total hardness. Three principal components were selected based on the eigen value, which explains 79.58% and 85.08% of the total variation in the years 2018 and 2019, respectively. The first principal component (PC-1) is identified with salinity, the second principal component (PC-2) with alkalinity, and the third principal component (PC-3) described the pollution. When the yearly comparison was made, the samples collected in 2019 were found to have an increased salinity compared to 2018, which shows an increased vulnerability to the aquifer of Mewat on account of the decline in rainfall recharge. It was also evident that declining recharge also triggered the recharge from other sources; thus, the impact of pollution is more pronounced in 2019 compared to 2018.

Assortative mating in space and time: patterns and biases.

Despite the important roles of assortative mating for understanding evolutionary processes, our knowledge on the variation in assortative mating across populations and breeding periods has been overshadowed by the greater attention given to general patterns. Obtaining data on mating pairs are difficult for most species; therefore, researchers often group data from different populations or breeding periods, which can increase positive biases in detecting and estimating assortative mating. We used a meta-analytic approach to investigate the biases caused by spatially or temporally pooling data and the assortative mating consistency across populations and breeding periods. We describe assortative mating patterns across and within animal taxa. We performed a systematic review to search studies reporting measures of size-assortative mating (SAM). Grouping data from multiple populations and seasons incurred positive biases. Overall, assortative mating moderately exhibited low repeatability in space and time, but it was inconsistent for most taxa. After excluding pooled measures, the average estimate for assortative mating was moderate and positive. Thus, our findings demonstrate that pooling data can produce misleading results. We also highlight the importance of further investigation of hypotheses that explain spatial and temporal variation in assortative mating, after its detection. Our study reinforces the significance of investigating mating patterns at various spatial and temporal scales before drawing broad conclusions.

Seeing through the static: the temporal dimension of plant-animal mutualistic interactions.

Most studies of plant-animal mutualistic networks have come from a temporally static perspective. This approach has revealed general patterns in network structure, but limits our ability to understand the ecological and evolutionary processes that shape these networks and to predict the consequences of natural and human-driven disturbance on species interactions. We review the growing literature on temporal dynamics of plant-animal mutualistic networks including pollination, seed dispersal and ant defence mutualisms. We then discuss potential mechanisms underlying such variation in interactions, ranging from behavioural and physiological processes at the finest temporal scales to ecological and evolutionary processes at the broadest. We find that at the finest temporal scales (days, weeks, months) mutualistic interactions are highly dynamic, with considerable variation in network structure. At intermediate scales (years, decades), networks still exhibit high levels of temporal variation, but such variation appears to influence network properties only weakly. At the broadest temporal scales (many decades, centuries and beyond), continued shifts in interactions appear to reshape network structure, leading to dramatic community changes, including loss of species and function. Our review highlights the importance of considering the temporal dimension for understanding the ecology and evolution of complex webs of mutualistic interactions.

Antagonistic effects of long- and short-term environmental variation on species coexistence.

Assessing the impact of environmental fluctuations on species coexistence is critical for understanding biodiversity loss and the ecological impacts of climate change. Yet determining how properties like the intensity, frequency or duration of environmental fluctuations influence species coexistence remains challenging, presumably because previous studies have focused on indefinite coexistence. Here, we model the impact of environmental fluctuations at different temporal scales on species coexistence over a finite time period by employing the concepts of time-windowed averaging and performance curves to incorporate temporal niche differences within a stochastic Lotka-Volterra model. We discover that short- and long-term environmental variability has contrasting effects on transient species coexistence, such that short-term variation favours species coexistence, whereas long-term variation promotes competitive exclusion. This dichotomy occurs because small samples (e.g. environmental changes over long time periods) are more likely to show large deviations from the expected mean and are more difficult to predict than large samples (e.g. environmental changes over short time periods), as described in the central limit theorem. Consequently, we show that the complex set of relationships among environmental fluctuations and species coexistence found in previous studies can all be synthesized within a general framework by explicitly considering both long- and short-term environmental variation.

Is It the Timing? Short-Term Mobility and Coital Frequency in Agbogbloshie, Ghana.

Short-term mobility is often associated with increased sexual risk behavior. Mobile individuals often have higher rates of sexual risk behavior compared to non-mobile individuals, but the reasons why are not clear. Using monthly retrospective panel data from 202 men and 282 women in Agbogbloshie, Ghana, we tested whether short-term mobility was associated with changes in coital frequency, and whether the association was due to the act of travel in the given month (e.g., enabling higher risk behavior), the reason for travel, or an individual's travel propensity at other times in the year. Overnight travel specifically to visit family or friends, or for education, health, or other reasons, was associated with increased coital frequency for men. However, men with higher travel propensities had lower overall coital frequency and the act of traveling enabled more sex only for the most frequent male travelers. Men who seldom traveled had much higher coital frequency, but the act of traveling was not associated with additional sex acts. For women, travel for education, health, or other reasons increased coital frequency. Occasional female travelers had slightly more sex acts compared to non-mobile women, and the act of traveling for these women was associated with slight increases in coital frequency, supporting the enabling hypothesis. Highly mobile women had fewer sex acts per month on average. Our findings suggest that mobility characteristics measured on a broad temporal scale, as well as the reason for mobility, are important to understand the relationship between short-term mobility and sexual behavior.

Accurate forest projections require long-term wood decay experiments because plant trait effects change through time.

Whether global change will drive changing forests from net carbon (C) sinks to sources relates to how quickly deadwood decomposes. Because complete wood mineralization takes years, most experiments focus on how traits, environments and decomposer communities interact as wood decay begins. Few experiments last long enough to test whether drivers change with decay rates through time, with unknown consequences for scaling short-term results up to long-term forest ecosystem projections. Using a 7 year experiment that captured complete mineralization among 21 temperate tree species, we demonstrate that trait effects fade with advancing decay. However, wood density and vessel diameter, which may influence permeability, control how decay rates change through time. Denser wood loses mass more slowly at first but more quickly with advancing decay, which resolves ambiguity about the after-life consequences of this key plant functional trait by demonstrating that its effect on decay depends on experiment duration and sampling frequency. Only long-term data and a time-varying model yielded accurate predictions of both mass loss in a concurrent experiment and naturally recruited deadwood structure in a 32-year-old forest plot. Given the importance of forests in the carbon cycle, and the pivotal role for wood decay, accurate ecosystem projections are critical and they require experiments that go beyond enumerating potential mechanisms by identifying the temporal scale for their effects.

The case for research integration, from genomics to remote sensing, to understand biodiversity change and functional dynamics in the world's lakes.

Freshwater ecosystems are heavily impacted by multiple stressors, and a freshwater biodiversity crisis is underway. This realization has prompted calls to integrate global freshwater ecosystem data, including traditional taxonomic and newer types of data (e.g., eDNA, remote sensing), to more comprehensively assess change among systems, regions, and organism groups. We argue that data integration should be done, not only with the important purpose of filling gaps in spatial, temporal, and organismal representation, but also with a more ambitious goal: to study fundamental cross-scale biological phenomena. Such knowledge is critical for discerning and projecting ecosystem functional dynamics, a realm of study where generalizations may be more tractable than those relying on taxonomic specificity. Integration could take us beyond cataloging biodiversity losses, and toward predicting ecosystem change more broadly. Fundamental biology questions should be central to integrative, interdisciplinary research on causal ecological mechanisms, combining traditional measures and more novel methods at the leading edge of the biological sciences. We propose a conceptual framework supporting this vision, identifying key questions and uncertainties associated with realizing this research potential. Our framework includes five interdisciplinary "complementarities." First, research approaches may provide comparative complementarity when they offer separate realizations of the same focal phenomenon. Second, for translational complementarity, data from one research approach is used to translate that from another, facilitating new inferences. Thirdly, causal complementarity arises when combining approaches allows us to "fill in" cause-effect relationships. Fourth, contextual complementarity is realized when together research methodologies establish the wider ecological and spatiotemporal context within which focal biological responses occur. Finally, integration may allow us to cross inferential scales through scaling complementarity. Explicitly identifying the modes and purposes of integrating research approaches, and reaching across disciplines to establish appropriate collaboration will allow researchers to address major biological questions that are more than the sum of the parts.

Drivers of the structure of plant-hummingbird interaction networks at multiple temporal scales.

In semi-arid environments, the marked contrast in temperature and precipitation over the year strongly shapes ecological communities. The composition of species and their ecological interactions within a community may vary greatly over time. Although intra-annual variations are often studied, empirical information on how plant-bird relationships are structured within and among years, and how their drivers may change over time are still limited. In this study, we analyzed the temporal dynamics of the structure of plant-hummingbird interaction networks by evaluating changes in species richness, diversity of interactions, modularity, network specialization, nestedness, and ß-diversity of interactions throughout four years in a Mexican xeric shrubland landscape. We also evaluated if the relative importance of abundance, phenology, morphology, and nectar sugar content consistently explains the frequency of pairwise interactions between plants and hummingbirds across different years. We found that species richness, diversity of interactions, nestedness, and network specialization did vary within and among years. We also observed that the ß-diversity of interactions was high among years and was mostly associated with species turnover (i.e., changes in species composition), with a minor contribution of interaction rewiring (i.e., shifting partner species at different times). Finally, the temporal co-occurrence of hummingbird and plant species among months was the best predictor of the frequency of pairwise interactions, and this pattern was consistent within and among years. Our study underscores the importance of considering the temporal scale to understand how changes in species phenologies, and the resulting temporal co-occurrences influence the structure of interaction networks.

Variations in transport of suspended sediment and associated elements induced by rainfall and agricultural cycle in a Mediterranean agroforestry catchment.

Soil erosion and fine particle exports are two of the major concerns of soil nutrient loss and water quality decrease nowadays. In Mediterranean mountainous environments, agricultural practices during different cropland stages likely increase sediment supplies and the export of fertilisers and pesticides out into the drainage system. In this study, we attempt to evaluate the soil response to different agricultural practices implemented during the agricultural cycle by monitoring the bare soil cropland area through the use of remote sensing and applying the sediment fingerprinting technique together with the newly consensus-based tracer selection method. To this purpose, 128 source samples were distributed over the four main land use/land covers and geomorphic elements existing in the study area. To analyse the spatio-temporal variability of source contributions, three sampling stations were established along the catchment and collected during two hydrological years. The consensus method was used to show the individual messages of each tracer, revealing non-conservative and dissenting tracers, followed by a discriminant function analysis (DFA) to select the best set of tracers for each mixture. Overall, the unmixing model outputs displayed channel bank and agriculture as the main contributing sources for all the seasonal campaigns. Nevertheless, the agricultural contribution was higher during the periods when the soil surface in croplands had no plant cover protection. Certain elements such as As, Co, Li, Mn, Zn and 238U were above source ranges in the sediment mixtures. The enriched elements showed higher content in the sediment mixtures during sowing and after harvest periods. Besides, an enrichment of phosphorous during both agricultural practices periods points out to agricultural activities as the main cause of sediment and elements export to streams. Thus, in the subcatchment with less bare soil cropland area, the agriculture source contributed with the lowest percentages. Our results support the protection of croplands, especially in periods of vegetation cover absence to prevent the loss of fertile soil and the export of potential pollutants to downstream water bodies.

Scale Both Confounds and Informs Characterization of Species Coexistence in Empirical Systems.

Identifying stable coexistence in empirical systems is notoriously difficult. Here, we show how spatiotemporal structure and complex system dynamics can confound two commonly used stability metrics in empirical contexts: response to perturbation and invasion rate when rare. We use these metrics to characterize stable coexistence across a range of spatial and temporal scales for five simulated models in which the ability of species to coexist in the long term is known a priori and for an empirical old field successional time series. We term the resulting multivariate distribution of metrics a "stability fingerprint." In accordance with a wide range of classic and recent studies, our results demonstrate that no combination of empirically tractable metrics or measurements is guaranteed to "correctly" characterize coexistence. However, we also find that heuristic information from the stability fingerprint can be used to broadly characterize dynamic behavior and identify circumstances under which particular combinations of species are likely to persist. Moreover, stability fingerprints appear to be particularly well suited for matching potential theoretical models to observed dynamics. These findings suggest that it may be prudent to shift the focus of empirical stability analysis away from quantifying single measures of stability and toward more heuristic, multivariate characterizations of community dynamics.

Species-specific and temporal scale-dependent responses of birds to drought.

Global climate change is increasing the frequency and intensity of weather extremes, including severe droughts in many regions. Drought can impact organisms by inhibiting reproduction, reducing survival and abundance, and forcing range shifts. For birds, considering temporal scale by averaging drought-related variables over different time lengths (i.e., temporal grains) captures different hydrologic attributes which may uniquely influence food supplies, vegetation greenness/structure, and other factors affecting populations. However, studies examining drought impacts on birds often assess a single temporal grain without considering that different species have different life histories that likely determine the temporal grain of their drought response. Furthermore, while drought is known to influence bird abundance and drive between-year range shifts, less understood is whether it causes within-range changes in species distributions. Our objectives were to (a) determine which temporal grain of drought (if any) is most related to bird presence/absence and whether this response is species specific; and (b) assess whether drought alters bird distributions by quantifying probability of local colonization and extinction as a function of drought intensity. We used North American Breeding Bird Survey data collected over 16 years, generalized linear mixed models, and dynamic occupancy models to meet these objectives. Different bird species responded to drought at different temporal grains, with most showing the strongest signal at annual or near-annual grains. For all drought-responsive species, increased drought intensity at any temporal grain always correlated with decreased occupancy. Additionally, colonization/extinction analyses indicated that one species, the dickcissel (Spiza americana), is more likely to colonize novel areas within the southern/core portion of its range during drought. Considering drought at different temporal grains, along with hydrologic attributes captured by each grain, may better reveal mechanisms behind drought impacts on birds and other organisms, and therefore improve understanding of how global climate change impacts species and the landscapes they inhabit.

Mapping ecosystem services provided by wetlands at multiple spatiotemporal scales: A case study in Quebec, Canada.

Wetlands are affected by climate and anthropogenic changes, which influence the ecosystem services (ES) they provide. This study presents a spatially explicit quantification of wetland ESs. The study site is the Yamaska river watershed located in Quebec, Canada. The proposed approach includes four main steps: (1) statistical selection of function indicators (FI) to build a composite ecosystem service indicator (ESI); (2) temporal land use mapping for past (1984), recent (2011) and future scenarios (2050); (3) mapping and quantification of FIs and ESIs at all temporal and spatial scales; and (4) synthesis of multispatial and multitemporal information using a diagram representation. Results present the spatiotemporal evolution of the ES on maintaining habitat provided by wetlands in the studied watershed. The historical characterization shows a general degradation of this service on the entire study area for the last 30 years. The proposed approach can target priority sectors in which this service has deteriorated or is lacking. Future scenarios show the urgency to act in order to preserve current intact areas, because even the optimistic scenario indicates that the studied ES would not return to its 1984 state. The proposed approach allows a spatiotemporal mapping of ESs combined with a visualization of their ecological, social, and economic components in a context of territorial management scenarios. This multi-scale method is reproducible, robust and can be replicated for other ESs in different territories.