Increased spatial and temporal autocorrelation of temperature under climate change

Physiologically informed organismal climatologies reveal unexpected spatiotemporal trends in temperature

Body temperature is universally recognized as a dominant driver of biological performance. Although the critical distinction between the temperature of an organism and its surrounding habitat has long been recognized, it remains common practice to assume that trends in air temperature-collected via remote sensing or weather stations-are diagnostic of trends in animal temperature and thus of spatiotemporal patterns of physiological stress and mortality risk. Here, by analysing long-term trends recorded by biomimetic temperature sensors designed to emulate intertidal mussel temperature across the US Pacific Coast, we show that trends in maximal organismal temperature ('organismal climatologies') during aerial exposure can differ substantially from those exhibited by co-located environmental data products. Specifically, using linear regression to compare maximal organismal and environmental (air temperature) climatologies, we show that not only are the magnitudes of body and air temperature markedly different, as expected, but so are their temporal trends at both local and biogeographic scales, with some sites showing significant decadal-scale increases in organismal temperature despite reductions in air temperature, or vice versa. The idiosyncratic relationship between the spatiotemporal patterns of organismal and air temperatures suggests that environmental climatology cannot be statistically corrected to serve as an accurate proxy for organismal climatology. Finally, using quantile regression, we show that spatiotemporal trends vary across the distribution of organismal temperature, with extremes shifting in different directions and at different rates than average metrics. Overall, our results highlight the importance of quantifying changes in the entire distribution of temperature to better predict biological performance and dispel the notion that raw or 'corrected' environmental (and specially air temperature) climatologies can be used to predict organismal temperature trends. Hence, despite their widespread coverage and availability, the severe limitations of environmental climatologies suggest that their role in conservation and management policy should be carefully considered.

Macroalgal microbiome biogeography is shaped by environmental drivers rather than geographical distance

BACKGROUND AND AIMS

Contrasting patterns of host and microbiome biogeography can provide insight into the drivers of microbial community assembly. Distance-decay relationships are a classic biogeographical pattern shaped by interactions between selective and non-selective processes. Joint biogeography of microbiomes and their hosts is of increasing interest owing to the potential for microbiome-facilitated adaptation.

METHODS

In this study, we examine the coupled biogeography of the model macroalga Durvillaea and its microbiome using a combination of genotyping by sequencing (host) and 16S rRNA amplicon sequencing (microbiome). Alongside these approaches, we use environmental data to characterize the relationship between the microbiome, the host, and the environment.

KEY RESULTS

We show that although the host and microbiome exhibit shared biogeographical structure, these arise from different processes, with host biogeography showing classic signs of geographical distance decay, but with the microbiome showing environmental distance decay. Examination of microbial subcommunities, defined by abundance, revealed that the abundance of microbes is linked to environmental selection. As microbes become less common, the dominant ecological processes shift away from selective processes and towards neutral processes. Contrary to expectations, we found that ecological drift does not promote structuring of the microbiome.

CONCLUSIONS

Our results suggest that although host macroalgae exhibit a relatively 'typical' biogeographical pattern of declining similarity with increasing geographical distance, the microbiome is more variable and is shaped primarily by environmental conditions. Our findings suggest that the Baas Becking hypothesis of 'everything is everywhere, the environment selects' might be a useful hypothesis to understand the biogeography of macroalgal microbiomes. As environmental conditions change in response to anthropogenic influences, the processes structuring the microbiome of macroalgae might shift, whereas those governing the host biogeography are less likely to change. As a result, increasingly decoupled host-microbe biogeography might be observed in response to such human influences.

Processes governing species richness in communities exposed to temporal environmental stochasticity: A review and synthesis of modelling approaches

Research into the processes governing species richness has often assumed that the environment is fixed, whereas realistic environments are often characterised by random fluctuations over time. This temporal environmental stochasticity (TES) changes the demographic rates of species populations, with cascading effects on community dynamics and species richness. Theoretical and applied studies have used process-based mathematical models to determine how TES affects species richness, but under a variety of frameworks. Here, we critically review such studies to synthesise their findings and draw general conclusions. We first provide a broad mathematical framework encompassing the different ways in which TES has been modelled. We then review studies that have analysed models with TES under the assumption of negligible interspecific interactions, such that a community is conceptualised as the sum of independent species populations. These analyses have highlighted how TES can reduce species richness by increasing the frequency at which a species becomes rare and therefore prone to extinction. Next, we review studies that have relaxed the assumption of negligible interspecific interactions. To simplify the corresponding models and make them analytically tractable, such studies have used mean-field theory to derive fixed parameters representing the typical strength of interspecific interactions under TES. The resulting analyses have highlighted community-level effects that determine how TES affects species richness, for species that compete for a common limiting resource. With short temporal correlations of environmental conditions, a non-linear averaging effect of interspecific competition strength over time gives an increase in species richness. In contrast, with long temporal correlations of environmental conditions, strong selection favouring the fittest species between changes in environmental conditions results in a decrease in species richness. We compare such results with those from invasion analysis, which examines invasion growth rates (IGRs) instead of species richness directly. Qualitative differences sometimes arise because the IGR is the expected growth rate of a species when it is rare, which does not capture the variation around this mean or the probability of the species becoming rare. Our review elucidates key processes that have been found to mediate the negative and positive effects of TES on species richness, and by doing so highlights key areas for future research.

Stabilizing effects of spatially heterogeneous disturbance via reduced spatial synchrony on a rocky shore community

Understanding how synchronous species fluctuations affect community stability is a main research topic in ecology. Yet experimental studies evaluating how changes in disturbance regimes affect the synchrony and stability of populations and communities remain rare. We hypothesized that spatially heterogeneous disturbances of moderate intensity would promote metacommunity stability by decreasing the spatial synchrony of species fluctuations. To test this hypothesis, we exposed rocky shore communities of algae and invertebrates to homogeneous and gradient-like spatial patterns of disturbance at two levels of intensity for 4 years and used synchrony networks to characterize community responses to these disturbances. The gradient-like disturbance at low intensity enhanced spatial β diversity compared to the other treatments and produced the most heterogeneous and least synchronized network, which was also the most stable in terms of population and community fluctuations. In contrast, homogeneous disturbance destabilized the community, enhancing spatial synchronization. Intense disturbances always reduced spatial β diversity, indicating that strong perturbations could destabilize communities via biotic homogenization regardless of their spatial structure. Our findings corroborated theoretical predictions, emphasizing the importance of spatially heterogeneous disturbances in promoting stability by amplifying asynchronous spatial and temporal fluctuations in population and community abundance. In contrast to other networks, synchrony networks are vulnerable to the removal of most peripheral nodes, which are less synchronized, but may contribute more to stability than other nodes by dampening large fluctuations in species abundance. Our findings suggest that climate change and direct anthropogenic disturbance can compromise the stability of ecological communities through combined effects on diversity and synchrony, as well as further affecting ecosystems through habitat loss.

Spatial synchrony cascades across ecosystem boundaries and up food webs via resource subsidies

Cross-ecosystem subsidies are critical to ecosystem structure and function, especially in recipient ecosystems where they are the primary source of organic matter to the food web. Subsidies are indicative of processes connecting ecosystems and can couple ecological dynamics across system boundaries. However, the degree to which such flows can induce cross-ecosystem cascades of spatial synchrony, the tendency for system fluctuations to be correlated across locations, is not well understood. Synchrony has destabilizing effects on ecosystems, adding to the importance of understanding spatiotemporal patterns of synchrony transmission. In order to understand whether and how spatial synchrony cascades across the marine-terrestrial boundary via resource subsidies, we studied the relationship between giant kelp forests on rocky nearshore reefs and sandy beach ecosystems that receive resource subsidies in the form of kelp wrack (detritus). We found that synchrony cascades from rocky reefs to sandy beaches, with spatiotemporal patterns mediated by fluctuations in live kelp biomass, wave action, and beach width. Moreover, wrack deposition synchronized local abundances of shorebirds that move among beaches seeking to forage on wrack-associated invertebrates, demonstrating that synchrony due to subsidies propagates across trophic levels in the recipient ecosystem. Synchronizing resource subsidies likely play an underappreciated role in the spatiotemporal structure, functioning, and stability of ecosystems.

Global temperature homogenization can obliterate temporal isolation in migratory animals with potential loss of population structure

Climate change is expected to increase the spatial autocorrelation of temperature, resulting in greater synchronization of climate variables worldwide. Possibly such 'homogenization of the world' leads to elevated risks of extinction and loss of biodiversity. In this study, we develop an empirical example on how increasing synchrony of global temperatures can affect population structure in migratory animals. We studied two subspecies of bar-tailed godwits Limosa lapponica breeding in tundra regions in Siberia: yamalensis in the west and taymyrensis further east and north. These subspecies share pre- and post-breeding stopover areas, thus being partially sympatric, but exhibiting temporal segregation. The latter is believed to facilitate reproductive isolation. Using satellite tracking data, we show that migration timing of both subspecies is correlated with the date of snowmelt in their respective breeding sites (later at the taymyrensis breeding range). Snow-cover satellite images demonstrate that the breeding ranges are on different climate trajectories and become more synchronized over time: between 1997 and 2020, the date of snowmelt advanced on average by 0.5 days/year in the taymyrensis breeding range, while it remained stable in the yamalensis breeding range. Previous findings showed how taymyrensis responded to earlier snowmelt by advancing arrival and clutch initiation. In the predicted absence of such advancements in yamalensis, we expect that the two populations will be synchronized by 2036-2040. Since bar-tailed godwits are social migrants, this raises the possibility of population exchange and prompts the question whether the two subspecies can maintain their geographic and morphological differences and population-specific migratory routines. The proposed scenario may apply to a wide range of (social) migrants as temporal segregation is crucial for promoting and maintaining reproductive isolation in many (partially sympatric) migratory populations. Homogenization of previously isolated populations could be an important consequence of increasing synchronized environments and hence climate change.

Pre-fertilization gamete thermal environment influences reproductive success, unmasking opposing sex-specific responses in Atlantic salmon (Salmo salar)

The environment gametes perform in just before fertilization is increasingly recognized to affect offspring fitness, yet the contributions of male and female gametes and their adaptive significance remain largely unexplored. Here, we investigated gametic thermal plasticity and its effects on hatching success and embryo performance in Atlantic salmon (Salmo salar). Eggs and sperm were incubated overnight at 2°C or 8°C, temperatures within the optimal thermal range of this species. Crosses between warm- and cold-incubated gametes were compared using a full-factorial design, with half of each clutch reared in cold temperatures and the other in warm temperatures. This allowed disentangling single-sex interaction effects when pre-fertilization temperature of gametes mismatched embryonic conditions. Pre-fertilization temperature influenced hatch timing and synchrony, and matching sperm and embryo temperatures resulted in earlier hatching. Warm incubation benefited eggs but harmed sperm, reducing the hatching success and, overall, gametic thermal plasticity did not enhance offspring fitness, indicating vulnerability to thermal changes. We highlight the sensitivity of male gametes to higher temperatures, and that gamete acclimation may not effectively buffer against deleterious effects of thermal fluctuations. From an applied angle, we propose the differential storage of male and female gametes as a tool to enhance sustainability within the hatcheries.

The role of seasonal migration in spatial population synchrony

Spatially synchronized population dynamics are common in nature, and understanding their causes is key for predicting species persistence. A main driver of synchrony between populations of the same species is shared environmental conditions, which cause populations closer together in space to be more synchronized than populations further from one another. Most theoretical and empirical understanding of this driver considers resident species. For migratory species, however, the degree of spatial autocorrelation in the environment may change across seasons and vary by their geographic location along the migratory route or on a nonbreeding ground, complicating the synchronizing effect of the environment. Migratory species show a variety of different strategies in how they disperse to and aggregate on nonbreeding grounds, ranging from completely shared nonbreeding grounds to multiple different ones. Depending on the sensitivity to environmental conditions off the breeding grounds, we can expect that migration and overwintering strategies will impact the extent and spatial pattern of population synchrony on the breeding grounds. Here, we use spatial population-dynamic modeling and simulations to investigate the relationship between seasonal environmental autocorrelation and migration characteristics. Our model shows that the effects of environmental autocorrelation experienced off the breeding ground on population synchrony depend on the number and size of nonbreeding grounds, and how populations migrate in relation to neighboring populations. When populations migrated to multiple nonbreeding grounds, spatial population synchrony increased with increasing environmental autocorrelation between nonbreeding grounds. Populations that migrated to the same place as near neighbors had higher synchrony at short distances than populations that migrated randomly. However, synchrony declined less across increasing distances for the random migration strategy. The differences in synchrony between migration strategies were most pronounced when the environmental autocorrelation between nonbreeding grounds was low. These results show the importance of considering migration when studying spatial population synchrony and predicting patterns of synchrony and population viability under global environmental change. Climate change and habitat loss and fragmentation may cause range shifts and changes in migratory strategies, as well as changes in the mean and spatial autocorrelation of the environment, which can alter the scale and patterns observed in spatial population synchrony.

Seasonality in Environment and Population Processes Alters Population Spatial Synchrony

AbstractPopulation spatial synchrony-the tendency for temporal population fluctuations to be correlated across locations-is common and important to metapopulation stability and persistence. One common cause of spatial synchrony, termed the Moran effect, occurs when populations respond to environmental fluctuations, such as weather, that are correlated over space. Although the degree of spatial synchrony in environmental fluctuations can differ between seasons and different population processes occur in different seasons, the impact on population spatial synchrony is uncertain because prior work has largely assumed that the spatial synchrony of environmental fluctuations and their effect on populations are consistent over annual sampling intervals. We used theoretical models to examine how seasonality in population processes and the spatial synchrony of environmental drivers affect population spatial synchrony. We found that population spatial synchrony can depend not only on the spatial synchrony of environmental drivers but also on the degree to which environmental fluctuations are correlated across seasons, locally, and across space. Moreover, measurements of synchrony from "snapshot" population censuses may not accurately reflect synchrony during other parts of the year. Together, these results show that neglecting seasonality in environmental conditions and population processes is consequential for understanding population spatial synchrony and its driving mechanisms.

Short-term temperature fluctuations increase disease in a Daphnia-parasite infectious disease system

Climate change has profound effects on infectious disease dynamics, yet the impacts of increased short-term temperature fluctuations on disease spread remain poorly understood. We empirically tested the theoretical prediction that short-term thermal fluctuations suppress endemic infection prevalence at the pathogen's thermal optimum. This prediction follows from a mechanistic disease transmission model analyzed using stochastic simulations of the model parameterized with thermal performance curves (TPCs) from metabolic scaling theory and using nonlinear averaging, which predicts ecological outcomes consistent with Jensen's inequality (i.e., reduced performance around concave-down portions of a thermal response curve). Experimental observations of replicated epidemics of the microparasite Ordospora colligata in Daphnia magna populations indicate that temperature variability had the opposite effect of our theoretical predictions and instead increase endemic infection prevalence. This positive effect of temperature variability is qualitatively consistent with a published hypothesis that parasites may acclimate more rapidly to fluctuating temperatures than their hosts; however, incorporating hypothetical effects of delayed host acclimation into the mechanistic transmission model did not fully account for the observed pattern. The experimental data indicate that shifts in the distribution of infection burden underlie the positive effect of temperature fluctuations on endemic prevalence. The increase in disease risk associated with climate fluctuations may therefore result from disease processes interacting across scales, particularly within-host dynamics, that are not captured by combining standard transmission models with metabolic scaling theory.

Life-history speed, population disappearances and noise-induced ratchet effects

Nature is replete with variation in the body sizes, reproductive output and generation times of species that produce life-history responses known to vary from small and fast to large and slow. Although researchers recognize that life-history speed likely dictates fundamental processes in consumer-resource interactions like productivity and stability, theoretical work remains incomplete in this critical area. Here, we examine the role of life-history speed on consumer-resource interactions by using a well-used mathematical approach that manipulates the speed of the consumer's growth rate in a consumer-resource interaction. Importantly, this approach holds the isocline geometry intact, allowing us to assess the impacts of altered life-history speed on stability (coefficient of variation, CV) without changing the underlying qualitative dynamics. Although slowing life history can be initially stabilizing, we find that in stochastic settings slowing ultimately drives highly destabilizing population disappearances, especially under reddened noise. Our results suggest that human-driven reddening of noise may decrease species stability because the autocorrelation of red noise enlarges the period and magnitude of perturbations, overwhelming a species' natural compensatory responses via a ratchet-like effect. This ratchet-like effect then pushes species' population dynamics far away from equilibria, which can lead to precipitous local extinction.

The color of environmental noise in river networks

Despite its far-reaching implications for conservation and natural resource management, little is known about the color of environmental noise, or the structure of temporal autocorrelation in random environmental variation, in streams and rivers. Here, we analyze the geography, drivers, and timescale-dependence of noise color in streamflow across the U.S. hydrography, using streamflow time series from 7504 gages. We find that daily and annual flows are dominated by red and white spectra respectively, and spatial variation in noise color is explained by a combination of geographic, hydroclimatic, and anthropogenic variables. Noise color at the daily scale is influenced by stream network position, and land use and water management explain around one third of the spatial variation in noise color irrespective of the timescale considered. Our results highlight the peculiarities of environmental variation regimes in riverine systems, and reveal a strong human fingerprint on the stochastic patterns of streamflow variation in river networks.

Effects of thermal fluctuations on biological processes: a meta-analysis of experiments manipulating thermal variability

Thermal variability is a key driver of ecological processes, affecting organisms and populations across multiple temporal scales. Despite the ubiquity of variation, biologists lack a quantitative synthesis of the observed ecological consequences of thermal variability across a wide range of taxa, phenotypic traits and experimental designs. Here, we conduct a meta-analysis to investigate how properties of organisms, their experienced thermal regime and whether thermal variability is experienced in either the past (prior to an assay) or present (during the assay) affect performance relative to the performance of organisms experiencing constant thermal environments. Our results-which draw upon 1712 effect sizes from 75 studies-indicate that the effects of thermal variability are not unidirectional and become more negative as mean temperature and fluctuation range increase. Exposure to variation in the past decreases performance to a greater extent than variation experienced in the present and increases the costs to performance more than diminishing benefits across a broad set of empirical studies. Further, we identify life-history attributes that predictably modify the ecological response to variation. Our findings demonstrate that effects of thermal variability on performance are context-dependent, yet negative outcomes may be heightened in warmer, more variable climates.

Looking to the past to understand the future: linking evolutionary modes of response with functional and life history traits in variable environments

In a variable world, plants must have strategies to deal with environmental conditions as they change. Understanding these strategies is critical since climate change not only affects mean conditions but also affects variability and predictability of those conditions. Doing so requires identifying how functional and life history traits interact throughout the life cycle to drive responses, as well as exploring how past variability will shape future responses. Here, I highlight relevant life history theory for predicting strategies in relation to the nature of environmental variability, relate theory to empirical studies integrating functional and life history traits to understand responses, and identify key areas for future research that will facilitate the application of this understanding toward predicting responses to climate change.

Exploring links between climatic predictability and the evolution of within- and transgenerational plasticity

In variable environments, phenotypic plasticity can increase fitness by providing tight environment-phenotype matching. However, adaptive plasticity is expected to evolve only when the future selective environment can be predicted based on the prevailing conditions. That is, the juvenile environment should be predictive of the adult environment (within-generation plasticity) or the parental environment should be predictive of the offspring environment (transgenerational plasticity). Moreover, the environmental predictability can also shape transient responses such as stress response in an adaptive direction. Here, we test links between environmental predictability and the evolution of adaptive plasticity by combining time series analyses and a common garden experiment using temperature as a stressor in a temperate butterfly (Melitaea cinxia). Time series analyses revealed that across season fluctuations in temperature over 48 years are overall predictable. However, within the growing season, temperature fluctuations showed high heterogeneity across years with low autocorrelations and the timing of temperature peaks were asynchronous. Most life-history traits showed strong within-generation plasticity for temperature and traits such as body size and growth rate broke the temperature-size rule. Evidence for transgenerational plasticity, however, was weak and detected for only two traits each in an adaptive and non-adaptive direction. We suggest that the low predictability of temperature fluctuations within the growing season likely disfavors the evolution of adaptive transgenerational plasticity but instead favors strong within-generation plasticity.

Plasticity's role in adaptive evolution depends on environmental change components

To forecast extinction risks of natural populations under climate change and direct human impacts, an integrative understanding of both phenotypic plasticity and adaptive evolution is essential. To date, the evidence for whether, when, and how much plasticity facilitates adaptive responses in changing environments is contradictory. We argue that explicitly considering three key environmental change components - rate of change, variance, and temporal autocorrelation - affords a unifying framework of the impact of plasticity on adaptive evolution. These environmental components each distinctively effect evolutionary and ecological processes underpinning population viability. Using this framework, we develop expectations regarding the interplay between plasticity and adaptive evolution in natural populations. This framework has the potential to improve predictions of population viability in a changing world.

Coastal upwelling generates cryptic temperature refugia

Understanding the effects of climate-mediated environmental variation on the distribution of organisms is critically important in an era of global change. We used wavelet analysis to quantify the spatiotemporal (co)variation in daily water temperature for predicting the distribution of cryptic refugia across 16 intertidal sites that were characterized as 'no', 'weak' or 'strong' upwelling and spanned 2000 km of the European Atlantic Coast. Sites experiencing weak upwelling exhibited high synchrony in temperature but low levels of co-variability at monthly to weekly timescales, whereas the opposite was true for sites experiencing strong upwelling. This suggests upwelling generates temporal thermal refugia that can promote organismal performance by both supplying colder water that mitigates thermal stress during hot Summer months and ensuring high levels of fine-scale variation in temperature that reduce the duration of thermal extremes. Additionally, pairwise correlograms based on the Pearson-product moment correlation coefficient and wavelet coherence revealed scale dependent trends in temperature fluctuations across space, with a rapid decay in strong upwelling sites at monthly and weekly timescales. This suggests upwelling also generates spatial thermal refugia that can 'rescue' populations from unfavorable conditions at local and regional scales. Overall, this study highlights the importance of identifying cryptic spatiotemporal refugia that emerge from fine-scale environmental variation to map potential patterns of organismal performance in a rapidly changing world.

Disturbance and nutrients synchronise kelp forests across scales through interacting Moran effects

Spatial synchrony is a ubiquitous and important feature of population dynamics, but many aspects of this phenomenon are not well understood. In particular, it is largely unknown how multiple environmental drivers interact to determine synchrony via Moran effects, and how these impacts vary across spatial and temporal scales. Using new wavelet statistical techniques, we characterised synchrony in populations of giant kelp Macrocystis pyrifera, a widely distributed marine foundation species, and related synchrony to variation in oceanographic conditions across 33 years (1987-2019) and >900 km of coastline in California, USA. We discovered that disturbance (storm-driven waves) and resources (seawater nutrients)-underpinned by climatic variability-act individually and interactively to produce synchrony in giant kelp across geography and timescales. Our findings demonstrate that understanding and predicting synchrony, and thus the regional stability of populations, relies on resolving the synergistic and antagonistic Moran effects of multiple environmental drivers acting on different timescales.

Australian farm dams are becoming less reliable water sources under climate change

Worldwide food production is under ever-increasing demand. Meanwhile, climate change is disrupting rainfall and evaporation patterns, making agriculture freshwater supplies more uncertain. IPCC models predict an increased variability in rainfall and temperature over most of the globe under climate change. Yet, the effects of climate variability on water security remain poorly resolved. Here we used satellite images and deep-learning convolutional neural networks to analyse the impacts of annual averages, seasonality, climate anomaly, and temporal autocorrelation (or climate reddening) for rain and temperature on the water levels of >100,000 Australian farm dams across 55 years. We found that the risk of empty farm dams increased with warmer annual temperatures, lower yearly rainfall, stronger seasonality, reduced climate anomalies, and higher temporal autocorrelation. We used this information to develop a predictive model and estimate the likelihood of water limitations in farm dams between 1965 and 2050 using historical data and Coupled Model Intercomparison Project Phase 5 (CMIP5) at two climate change scenarios. Results showed that the frequency of empty water reserves has increased 2.5-fold since 1965 and will continue to increase across most (91%) of Australia. We estimated a 37% decline in rural areas with year-round water supplies between 1965 (457,076 km²) and 2050 (285,998 km²). Our continental-scale assessment documents complex temporal and spatial impacts of climate change on agricultural water security, with ramifications for society, economy, and the environment.

Temporally auto-correlated predator attacks structure ecological communities

For species primarily regulated by a common predator, the P* rule of Holt & Lawton (Holt & Lawton, 1993. Am. Nat.142, 623–645. (doi:10.1086/285561)) predicts that the prey species that supports the highest mean predator density (P*) excludes the other prey species. This prediction is re-examined in the presence of temporal fluctuations in the vital rates of the interacting species including predator attack rates. When the fluctuations in predator attack rates are temporally uncorrelated, the P* rule still holds even when the other vital rates are temporally auto-correlated. However, when temporal auto-correlations in attack rates are positive but not too strong, the prey species can coexist due to the emergence of a positive covariance between predator density and prey vulnerability. This coexistence mechanism is similar to the storage effect for species regulated by a common resource. Negative or strongly positive auto-correlations in attack rates generate a negative covariance between predator density and prey vulnerability and a stochastic priority effect can emerge: with non-zero probability either prey species is excluded. These results highlight how temporally auto-correlated species’ interaction rates impact the structure and dynamics of ecological communities.

An evolutionary perspective on stress responses, damage and repair

Variation in stress responses has been investigated in relation to environmental factors, species ecology, life history and fitness. Moreover, mechanistic studies have unravelled molecular mechanisms of how acute and chronic stress responses cause physiological impacts ('damage'), and how this damage can be repaired. However, it is not yet understood how the fitness effects of damage and repair influence stress response evolution. Here we study the evolution of hormone levels as a function of stressor occurrence, damage and the efficiency of repair. We hypothesise that the evolution of stress responses depends on the fitness consequences of damage and the ability to repair that damage. To obtain some general insights, we model a simplified scenario in which an organism repeatedly encounters a stressor with a certain frequency and predictability (temporal autocorrelation). The organism can defend itself by mounting a stress response (elevated hormone level), but this causes damage that takes time to repair. We identify optimal strategies in this scenario and then investigate how those strategies respond to acute and chronic exposures to the stressor. We find that for higher repair rates, baseline and peak hormone levels are higher. This typically means that the organism experiences higher levels of damage, which it can afford because that damage is repaired more quickly, but for very high repair rates the damage does not build up. With increasing predictability of the stressor, stress responses are sustained for longer, because the animal expects the stressor to persist, and thus damage builds up. This can result in very high (and potentially fatal) levels of damage when organisms are exposed to chronic stressors to which they are not evolutionarily adapted. Overall, our results highlight that at least three factors need to be considered jointly to advance our understanding of how stress physiology has evolved: (i) temporal dynamics of stressor occurrence; (ii) relative mortality risk imposed by the stressor itself versus damage caused by the stress response; and (iii) the efficiency of repair mechanisms.

Tail-dependent spatial synchrony arises from nonlinear driver-response relationships

Spatial synchrony may be tail-dependent, that is, stronger when populations are abundant than scarce, or vice-versa. Here, 'tail-dependent' follows from distributions having a lower tail consisting of relatively low values and an upper tail of relatively high values. We present a general theory of how the distribution and correlation structure of an environmental driver translates into tail-dependent spatial synchrony through a non-linear response, and examine empirical evidence for theoretical predictions in giant kelp along the California coastline. In sheltered areas, kelp declines synchronously (lower-tail dependence) when waves are relatively intense, because waves below a certain height do little damage to kelp. Conversely, in exposed areas, kelp is synchronised primarily by periods of calmness that cause shared recovery (upper-tail dependence). We find evidence for geographies of tail dependence in synchrony, which helps structure regional population resilience: areas where population declines are asynchronous may be more resilient to disturbance because remnant populations facilitate reestablishment.

Spatial autocorrelation of local patch extinctions drives recovery dynamics in metacommunities

Human activities put ecosystems under increasing pressure, often resulting in local extinctions. However, it is unclear how local extinctions affect regional processes, such as the distribution of diversity in space, especially if extinctions show spatial patterns, such as being clustered. Therefore, it is crucial to investigate extinctions and their consequences in a spatially explicit framework. Using highly controlled microcosm experiments and theoretical models, we ask here how the number and spatial autocorrelation of extinctions interactively affect metacommunity dynamics. We found that local patch extinctions increased local diversity (α-diversity) and inter-patch diversity (β-diversity) by delaying the exclusion of inferior competitors. Importantly, recolonization dynamics depended more strongly on the spatial distribution than on the number of patch extinctions: clustered local patch extinctions resulted in slower recovery, lower α-diversity and higher β-diversity. Our results highlight that the spatial distribution of perturbations should be taken into account when studying and managing spatially structured communities.

Timescale analyses of fluctuations in coexisting populations of a native and invasive tree squirrel

Competition from invasive species is an increasing threat to biodiversity. In Southern California, the western gray squirrel (Sciurus griseus, WGS) is facing competition from the fox squirrel (Sciurus niger, FS), an invasive congener.

We used spectral methods to analyze 140 consecutive monthly censuses of WGS and FS within a 11.3 ha section of the California Botanic Garden. Variation in the numbers for both species and their synchrony was distributed across long timescales (>15 months).

After filtering out annual changes, concurrent mean monthly temperatures from nearby Ontario Airport yielded a spectrum with a large semi‐annual peak and significant spectral power at long timescales (>28 months). The cospectrum between WGS numbers and temperature revealed a significant negative correlation at long timescales (>35 months). Cospectra also revealed significant negative correlations with temperature at a six‐month timescale for both WGS and FS.

Simulations from a model of two competing species indicate that the risk of extinction for the weaker competitor increases quickly as environmental noise shifts from short to long timescales.

We analyzed the timescales of fluctuations in detrended mean annual temperatures for the time period 1915–2014 from 1218 locations across the continental USA. In the last two decades, significant shifts from short to long timescales have occurred, from <3 years to 4–6 years.

Our results indicate that (i) population fluctuations in co‐occurring native and invasive tree squirrels are synchronous, occur over long timescales, and may be driven by fluctuations in environmental conditions; (ii) long timescale population fluctuations increase the risk of extinction in competing species, especially for the inferior competitor; and (iii) the timescales of interannual environmental fluctuations may be increasing from recent historical values. These results have broad implications for the impact of climate change on the maintenance of biodiversity.

Contributions of mean temperature and temperature variation to population stability and community diversity

Future climate changes are predicted to not only increase global temperatures but also alter temporal variation in temperature. As thermal tolerances form an important component of a species’ niche, changes to the temperature regime have the capacity to negatively impact species, and therefore, the diversity of the communities they inhabit. In this study, we used protist microcosms to assess how mean temperature, as well as temporal variation in temperature, affected diversity. Communities consisted of seven species in a multitrophic food web. Each ecosystem was inoculated with the same abundances of each species at the start of the experiment, and species densities, Hill's numbers (based on Shannon diversity), the number of extinctions, and the probability the microcosm contained predators were all calculated at the end of the experiment. To assess how mean temperature and temperature fluctuations affect stability, we also measured population densities through time. We found that increased temporal variation in temperature increased final densities, increased Hill's numbers (at low mean temperatures), decreased rates of extinctions, and increased the probability that predators survived till the end of the experiment. Mean temperatures did not significantly affect either the number of extinctions or the probability of predators, but did reduce the positive effect of increased temporal variation in temperature on overall diversity. Our results indicate that climatic changes have the potential to impact the composition of ecological communities by altering multiple components of temperature regimes. However, given that some climate forecasts are predicting increased mean temperatures and reduced variability, our finding that increased mean temperature and reduced temporal variation are both generally associated with negative consequences is somewhat concerning.

Climate change and plant reproduction: trends and drivers of mast seeding change

Climate change is reshaping global vegetation through its impacts on plant mortality, but recruitment creates the next generation of plants and will determine the structure and composition of future communities. Recruitment depends on mean seed production, but also on the interannual variability and among-plant synchrony in seed production, the phenomenon known as mast seeding. Thus, predicting the long-term response of global vegetation dynamics to climate change requires understanding the response of masting to changing climate. Recently, data and methods have become available allowing the first assessments of long-term changes in masting. Reviewing the literature, we evaluate evidence for a fingerprint of climate change on mast seeding and discuss the drivers and impacts of these changes. We divide our discussion into the main characteristics of mast seeding: interannual variation, synchrony, temporal autocorrelation and mast frequency. Data indicate that masting patterns are changing but the direction of that change varies, likely reflecting the diversity of proximate factors underlying masting across taxa. Experiments to understand the proximate mechanisms underlying masting, in combination with the analysis of long-term datasets, will enable us to understand this observed variability in the response of masting. This will allow us to predict future shifts in masting patterns, and consequently ecosystem impacts of climate change via its impacts on masting. This article is part of the theme issue 'The ecology and evolution of synchronized seed production in plants'.

Widespread changes in surface temperature persistence under climate change

Climate change has been and will be accompanied by widespread changes in surface temperature. It is clear that these changes include global-wide increases in mean surface temperature and changes in temperature variance that are more regionally-dependent^1-3. It is less clear whether they also include changes in the persistence of surface temperature. This is important as the effects of weather events on ecosystems and society depend critically on the length of the event. Here we provide an extensive survey of the response of surface temperature persistence to climate change over the twenty-first century from the output of 150 simulations run on four different Earth system models, and from simulations run on simplified models with varying representations of radiative processes and large-scale dynamics. Together, the results indicate that climate change simulations are marked by widespread changes in surface temperature persistence that are generally most robust over ocean areas and arise due to a seemingly broad range of physical processes. The findings point to both the robustness of widespread changes in persistence under climate change, and the critical need to better understand, simulate and constrain such changes.

Effects of stochasticity on the length and behaviour of ecological transients

There is a growing recognition that ecological systems can spend extended periods of time far away from an asymptotic state, and that ecological understanding will therefore require a deeper appreciation for how long ecological transients arise. Recent work has defined classes of deterministic mechanisms that can lead to long transients. Given the ubiquity of stochasticity in ecological systems, a similar systematic treatment of transients that includes the influence of stochasticity is important. Stochasticity can of course promote the appearance of transient dynamics by preventing systems from settling permanently near their asymptotic state, but stochasticity also interacts with deterministic features to create qualitatively new dynamics. As such, stochasticity may shorten, extend or fundamentally change a system's transient dynamics. Here, we describe a general framework that is developing for understanding the range of possible outcomes when random processes impact the dynamics of ecological systems over realistic time scales. We emphasize that we can understand the ways in which stochasticity can either extend or reduce the lifetime of transients by studying the interactions between the stochastic and deterministic processes present, and we summarize both the current state of knowledge and avenues for future advances.

Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment

Most natural environments exhibit a substantial component of random variation, with a degree of temporal autocorrelation that defines the color of environmental noise. Such environmental fluctuations cause random fluctuations in natural selection, affecting the predictability of evolution. But despite long-standing theoretical interest in population genetics in stochastic environments, there is a dearth of empirical estimation of underlying parameters of this theory. More importantly, it is still an open question whether evolution in fluctuating environments can be predicted indirectly using simpler measures, which combine environmental time series with population estimates in constant environments. Here we address these questions by using an automated experimental evolution approach. We used a liquid-handling robot to expose over a hundred lines of the micro-alga Dunaliella salina to randomly fluctuating salinity over a continuous range, with controlled mean, variance, and autocorrelation. We then tracked the frequencies of two competing strains through amplicon sequencing of nuclear and choloroplastic barcode sequences. We show that the magnitude of environmental fluctuations (determined by their variance), but also their predictability (determined by their autocorrelation), had large impacts on the average selection coefficient. The variance in frequency change, which quantifies randomness in population genetics, was substantially higher in a fluctuating environment. The reaction norm of selection coefficients against constant salinity yielded accurate predictions for the mean selection coefficient in a fluctuating environment. This selection reaction norm was in turn well predicted by environmental tolerance curves, with population growth rate against salinity. However, both the selection reaction norm and tolerance curves underestimated the variance in selection caused by random environmental fluctuations. Overall, our results provide exceptional insights into the prospects for understanding and predicting genetic evolution in randomly fluctuating environments.

Being able to predict evolution under natural selection is important for many applied fields of biology, ranging from agriculture to medicine or conservation. However, this endeavor is complicated by factors that inherently limit our ability to predict the future, such as random fluctuations in the environment. Population genetic theory indicates that probabilistic predictions can still be made in this context, but the extent to which this holds empirically, and whether these predictions can be based on simple measurements, are still open questions. Making progress on answering these questions can be achieved by capitalizing on experiments where the environment is precisely controlled over many generations. Here, we used a pipetting robot to generate random time series of salinities with controlled patterns of fluctuations, which we imposed on a microalga, Dunaliella salina. Tracking the frequencies of two genotypes in a mixture by sequencing two short barcode sequences, we were able to show how patterns of fluctuating selection relate to the fluctuating environment. Interestingly, parts of these responses, but not all, could be predicted by simpler measurements in constant environments, allowing precise characterization the limits and prospects for predicting evolution in fluctuating environments.

Scaling up biodiversity-ecosystem functioning relationships: the role of environmental heterogeneity in space and time

The biodiversity and ecosystem functioning (BEF) relationship is expected to be scale-dependent. The autocorrelation of environmental heterogeneity is hypothesized to explain this scale dependence because it influences how quickly biodiversity accumulates over space or time. However, this link has yet to be demonstrated in a formal model. Here, we use a Lotka-Volterra competition model to simulate community dynamics when environmental conditions vary across either space or time. Species differ in their optimal environmental conditions, which results in turnover in community composition. We vary biodiversity by modelling communities with different sized regional species pools and ask how the amount of biomass per unit area depends on the number of species present, and the spatial or temporal scale at which it is measured. We find that more biodiversity is required to maintain functioning at larger temporal and spatial scales. The number of species required increases quickly when environmental autocorrelation is low, and slowly when autocorrelation is high. Both spatial and temporal environmental heterogeneity lead to scale dependence in BEF, but autocorrelation has larger impacts when environmental change is temporal. These findings show how the biodiversity required to maintain functioning is expected to increase over space and time.

Demography_Lab, an educational application to evaluate population growth: Unstructured and matrix models

Training in Population Ecology asks for scalable applications capable of embarking students on a trip from basic concepts to the projection of populations under the various effects of density dependence and stochasticity. Demography_Lab is an educational tool for teaching Population Ecology aspiring to cover such a wide range of objectives. The application uses stochastic models to evaluate the future of populations. Demography_Lab may accommodate a wide range of life cycles and can construct models for populations with and without an age or stage structure. Difference equations are used for unstructured populations and matrix models for structured populations. Both types of models operate in discrete time. Models can be very simple, constructed with very limited demographic information or parameter-rich, with a complex density-dependence structure and detailed effects of the different sources of stochasticity. Demography_Lab allows for deterministic projections, asymptotic analysis, the extraction of confidence intervals for demographic parameters, and stochastic projections. Stochastic population growth is evaluated using up to three sources of stochasticity: environmental and demographic stochasticity and sampling error in obtaining the projection matrix. The user has full control on the effect of stochasticity on vital rates. The effect of the three sources of stochasticity may be evaluated independently for each vital rate. The user has also full control on density dependence. It may be included as a ceiling population size controlling the number of individuals in the population or it may be evaluated independently for each vital rate. Sensitivity analysis can be done for the asymptotic population growth rate or for the probability of extinction. Elasticity of the probability of extinction may be evaluated in response to changes in vital rates, and in response to changes in the intensity of density dependence and environmental stochasticity.

Towards an Evolutionary Theory of Stress Responses

All organisms have a stress response system to cope with environmental threats, yet its precise form varies hugely within and across individuals, populations, and species. While the physiological mechanisms are increasingly understood, how stress responses have evolved remains elusive. Here, we show that important insights can be gained from models that incorporate physiological mechanisms within an evolutionary optimality analysis (the 'evo-mecho' approach). Our approach reveals environmental predictability and physiological constraints as key factors shaping stress response evolution, generating testable predictions about variation across species and contexts. We call for an integrated research programme combining theory, experimental evolution, and comparative analysis to advance scientific understanding of how this core physiological system has evolved.

Physiological fractals: visual and statistical evidence across timescales and experimental states

A marker of engaging in compassion meditation and related processes is an increase in heart-rate variability (HRV), typically interpreted as a marker of parasympathetic nervous system response. While insightful, open questions remain. For example, which timescale is best to examine the effects of meditation and related practices on HRV? Furthermore, how might advanced time-series analyses--such as stationarity--be able to examine dynamic changes in the mean and variance of the HRV signal across time? Here we apply such methods to previously published data, which measured HRV pre- and post- a two-week compassionate mind training (CMT) intervention. Inspection of these data reveals that a visualization of HRV correlations across resting and compassion meditation states, pre- and post-two-week training, is retained across numerous recording timescales. Here, the fractal-like nature of our data indicates that the accuracy of representing HRV data can exist across timescales, albeit with greater or lesser granularity. Interestingly, inspection of the HRV signal at Time 2 compassion meditation versus Time 1 revealed a more highly correlated (i.e. potentially more stable) signal. We followed up these results with tests of stationarity, which revealed Time 2 had a less stochastic (variable) signal than Time 1, and a measure of distance in the time series, which showed that Time 2 had less of an average difference between rest and meditation than at Time 1. Our results provide novel assessment of visual and statistical markers of HRV change across distinct experimental states.

Transgenerational Plasticity in Human-Altered Environments

Our ability to predict how species will respond to human-induced rapid environmental change (HIREC) may depend upon our understanding of transgenerational plasticity (TGP), which occurs when environments experienced by previous generations influence phenotypes of subsequent generations. TGP evolved to help organisms cope with environmental stressors when parental environments are highly predictive of offspring environments. HIREC can alter conditions that favored TGP in historical environments by reducing parents' ability to detect environmental conditions, disrupting previous correlations between parental and offspring environments, and interfering with the transmission of parental cues to offspring. Because of the propensity to produce errors in these processes, TGP will likely generate negative fitness outcomes in response to HIREC, though beneficial fitness outcomes may occur in some cases.

Spatial evaluation of prevalence, pattern and predictors of cervical cancer screening in India

OBJECTIVE

To investigate the social determinants of cervical cancer screening and report the locations vulnerable to poor utilization of cervical cancer screening services.

STUDY DESIGN

An ecological study with the data derived from fourth round of the National Family Health Survey conducted in India in the period 2015-2016.

METHODS

The study focused on the percentage of women who have never undergone cervical cancer screening across 639 districts in India. Moran's I statistic was used to investigate the overall clustering of location. The Getis-Ord Gi* statistic was used for the detection of significant local clusters. Spatial error, spatial lag, spatial Durbin and spatial Durbin error models were compared, and the model with best fit was reported. ArcGIS, GeoDa and R software were used for the analysis.

RESULTS

The existence of spatial autocorrelation (Moran's I = 0.61) necessitates the consideration of spatial component while studying the screening data. A significant clustering of districts with poor screening has been observed in the North-Central and North-Eastern regions of India. The geographic arrangement of the percentage of women who have undergone cervical cancer screening was associated with the percentage of women with poor wealth index (P < 0.001), not using a modern method of contraception (P < 0.001), residing in rural areas (P = 0.033) and never heard of sexually transmitted infection (P = 0.014). The range of percentage of women getting cervix screened for cancer was 0.5-68.4%, presenting the heterogeneity among the population elements.

CONCLUSION

A higher risk of poor cervical cancer screening is observed in the districts where most of the women have poor wealth index, reside in urban area, have never heard of sexually transmitted infection and do not use a modern method of contraception.

Modelling ecosystem adaptation and dangerous rates of global warming

We are in a period of relatively rapid climate change. This poses challenges for individual species and threatens the ecosystem services that humanity relies upon. Temperature is a key stressor. In a warming climate, individual organisms may be able to shift their thermal optima through phenotypic plasticity. However, such plasticity is unlikely to be sufficient over the coming centuries. Resilience to warming will also depend on how fast the distribution of traits that define a species can adapt through other methods, in particular through redistribution of the abundance of variants within the population and through genetic evolution. In this paper, we use a simple theoretical ‘trait diffusion’ model to explore how the resilience of a given species to climate change depends on the initial trait diversity (biodiversity), the trait diffusion rate (mutation rate), and the lifetime of the organism. We estimate theoretical dangerous rates of continuous global warming that would exceed the ability of a species to adapt through trait diffusion, and therefore lead to a collapse in the overall productivity of the species. As the rate of adaptation through intraspecies competition and genetic evolution decreases with species lifetime, we find critical rates of change that also depend fundamentally on lifetime. Dangerous rates of warming vary from 1°C per lifetime (at low trait diffusion rate) to 8°C per lifetime (at high trait diffusion rate). We conclude that rapid climate change is liable to favour short-lived organisms (e.g. microbes) rather than longer-lived organisms (e.g. trees).