Chihuahuan Desert kangaroo rats: nonlinear effects of population dynamics, competition, and rainfall

Macroscale patterns of rodent herbivory damage and underlying mechanisms in forests of China

BACKGROUND

Understanding the macroscale patterns of rodent herbivory damage and their driving factors are essential for effective rodent management. This study examines how climatic factors and human activities influence the large-scale spatial distribution of rodent herbivory damage in forests of China.

RESULTS

I utilized a unique long-term province-level dataset of rodent damage in China to map its extent across the country. A generalized linear mixed model was employed to analyze the relationship between rodent damage, and climatic variables, and human population density (HPD). The results reveal a clear spatial pattern of rodent herbivory damage in China's forests, primarily driven by HPD and precipitation of the warmest quarter, with a secondary influence of diurnal temperature range. These three variables explained approximately 58% of the variation in the geographic pattern of rodent herbivory damage in China's forests. Specifically, rodent damage was negatively correlated with both precipitation of the warmest quarter and HPD. Higher precipitation during the warmest quarter (often as rainstorms) probably exacerbates rodent mortality through flooding their burrows and imposing thermal stress, while higher HPD probably increases predation pressure, further lowering rodent populations. Additionally, rodent damage was positively related to diurnal temperature range, likely because greater diurnal temperature fluctuations impose greater thermal stress on their predator, thereby enhancing rodent survival. Consequently, regions in northwestern China that are arid, experience high diurnal temperature fluctuations and have low human populations, are particularly vulnerable to severe rodent damage. In contrast, southern and southeastern China, with higher precipitation, milder temperature fluctuations, and denser human populations, experience significantly lower rodent damage.

CONCLUSION

The findings suggest that the combination of precipitation during the warmest quarter, diurnal temperature range, and HPD can serve as effective indicators of rodent pest severity in forests. This underscores the need for proactive surveillance and management in arid regions with high diurnal temperature fluctuations and low population densities worldwide.

Beyond single-species models: leveraging multispecies forecasts to navigate the dynamics of ecological predictability

Background

Forecasting the responses of natural populations to environmental change is a key priority in the management of ecological systems. This is challenging because the dynamics of multi-species ecological communities are influenced by many factors. Populations can exhibit complex, nonlinear responses to environmental change, often over multiple temporal lags. In addition, biotic interactions, and other sources of multi-species dependence, are major contributors to patterns of population variation. Theory suggests that near-term ecological forecasts of population abundances can be improved by modelling these dependencies, but empirical support for this idea is lacking.

Methods

We test whether models that learn from multiple species, both to estimate nonlinear environmental effects and temporal interactions, improve ecological forecasts compared to simpler single species models for a semi-arid rodent community. Using dynamic generalized additive models, we analyze time series of monthly captures for nine rodent species over 25 years.

Results

Model comparisons provide strong evidence that multi-species dependencies improve both hindcast and forecast performance, as models that captured these effects gave superior predictions than models that ignored them. We show that changes in abundance for some species can have delayed, nonlinear effects on others, and that lagged, nonlinear effects of temperature and vegetation greenness are key drivers of changes in abundance for this system.

Conclusions

Our findings highlight that multivariate models are useful not only to improve near-term ecological forecasts but also to ask targeted questions about ecological interactions and drivers of change. This study emphasizes the importance of jointly modelling species' shared responses to the environment and their delayed temporal interactions when teasing apart community dynamics.

Transferability of ecological forecasting models to novel biotic conditions in a long-term experimental study

Ecological forecasting models play an increasingly important role for managing natural resources and assessing our fundamental knowledge of processes driving ecological dynamics. As global environmental change pushes ecosystems beyond their historical conditions, the utility of these models may depend on their transferability to novel conditions. Because species interactions can alter resource use, timing of reproduction, and other aspects of a species' realized niche, changes in biotic conditions, which can arise from community reorganization events in response to environmental change, have the potential to impact model transferability. Using a long-term experiment on desert rodents, we assessed model transferability under novel biotic conditions to better understand the limitations of ecological forecasting. We show that ecological forecasts can be less accurate when the models generating them are transferred to novel biotic conditions and that the extent of model transferability can depend on the species being forecast. We also demonstrate the importance of incorporating uncertainty into forecast evaluation with transferred models generating less accurate and more uncertain forecasts. These results suggest that how a species perceives its competitive landscape can influence model transferability and that when uncertainties are properly accounted for, transferred models may still be appropriate for decision making. Assessing the extent of the transferability of forecasting models is a crucial step to increase our understanding of the limitations of ecological forecasts.

Environmental context dependency in species interactions

Ecological interactions are not uniform across time and can vary with environmental conditions. Yet, interactions among species are often measured with short-term controlled experiments whose outcomes can depend greatly on the particular environmental conditions under which they are performed. As an alternative, we use empirical dynamic modeling to estimate species interactions across a wide range of environmental conditions directly from existing long-term monitoring data. In our case study from a southern California kelp forest, we test whether interactions between multiple kelp and sea urchin species can be reliably reconstructed from time-series data and whether those interactions vary predictably in strength and direction across observed fluctuations in temperature, disturbance, and low-frequency oceanographic regimes. We show that environmental context greatly alters the strength and direction of species interactions. In particular, the state of the North Pacific Gyre Oscillation seems to drive the competitive balance between kelp species, asserting bottom-up control on kelp ecosystem dynamics. We show the importance of specifically studying variation in interaction strength, rather than mean interaction outcomes, when trying to understand the dynamics of complex ecosystems. The significant context dependency in species interactions found in this study argues for a greater utilization of long-term data and empirical dynamic modeling in studies of the dynamics of other ecosystems.

Timing outweighs magnitude of rainfall in shaping population dynamics of a small mammal species in steppe grassland

Disentangling the effects of rainfall timing and magnitude on animal and plant populations is essential to reveal the biological consequence of diverse climate change scenarios around the world. We conducted a 10-y, large-scale, manipulative experiment to examine the bottom-up effects of changes in rainfall regime on the population dynamics of Brandt’s voles in the steppe grassland of Inner Mongolia, China. We found that a moderate rainfall increase during the early growing season could produce marked increases in vole population size by increasing the biomass of preferred plant species, whereas large increases in rainfall produced no additional increase in vole population growth. Our study highlights the importance of rainfall magnitude and timing on the nonlinear population dynamics of herbivores.

Climate change–induced shifts in species phenology differ widely across trophic levels, which may lead to consumer–resource mismatches with cascading population and ecosystem consequences. Here, we examined the effects of different rainfall patterns (i.e., timing and amount) on the phenological asynchrony of population of a generalist herbivore and their food sources in semiarid steppe grassland in Inner Mongolia. We conducted a 10-y (2010 to 2019) rainfall manipulation experiment in 12 0.48-ha field enclosures and found that moderate rainfall increases during the early rather than late growing season advanced the timing of peak reproduction and drove marked increases in population size through increasing the biomass of preferred plant species. By contrast, greatly increased rainfall produced no further increases in vole population growth due to the potential negative effect of the flooding of burrows. The increases in vole population size were more coupled with increased reproduction of overwintered voles and increased body mass of young-of-year than with better survival. Our results provide experimental evidence for the fitness consequences of phenological mismatches at the population level and highlight the importance of rainfall timing on the population dynamics of small herbivores in the steppe grassland environment.

Declines in rodent abundance and diversity track regional climate variability in North American drylands

Regional long-term monitoring can enhance the detection of biodiversity declines associated with climate change, improving future projections by reducing reliance on space-for-time substitution and increasing scalability. Rodents are diverse and important consumers in drylands, regions defined by the scarcity of water that cover 45% of Earth's land surface and face increasingly drier and more variable climates. We analyzed abundance data for 22 rodent species across grassland, shrubland, ecotone, and woodland ecosystems in the southwestern USA. Two time series (1995-2006 and 2004-2013) coincided with phases of the Pacific Decadal Oscillation (PDO), which influences drought in southwestern North America. Regionally, rodent species diversity declined 20%-35%, with greater losses during the later time period. Abundance also declined regionally, but only during 2004-2013, with losses of 5% of animals captured. During the first time series (wetter climate), plant productivity outranked climate variables as the best regional predictor of rodent abundance for 70% of taxa, whereas during the second period (drier climate), climate best explained variation in abundance for 60% of taxa. Temporal dynamics in diversity and abundance differed spatially among ecosystems, with the largest declines in woodlands and shrublands of central New Mexico and Colorado. Which species were winners or losers under increasing drought and amplified interannual variability in drought depended on ecosystem type and the phase of the PDO. Fewer taxa were significant winners (18%) than losers (30%) under drought, but the identities of winners and losers differed among ecosystems for 70% of taxa. Our results suggest that the sensitivities of rodent species to climate contributed to regional declines in diversity and abundance during 1995-2013. Whether these changes portend future declines in drought-sensitive consumers in the southwestern USA will depend on the climate during the next major PDO cycle.

Climatic factors and population demography in big-eared woodrat, Neotoma macrotis

Changes in temperature and rainfall patterns can have marked impacts on small mammal populations that inhabit environments with highly fluctuating water availability. With projected increases in droughts and fewer but more intense rainfall events in the Southwestern United States, the persistence of many wildlife populations may be threatened. Our goal was to assess how temperature and rainfall during distinct dry and wet seasons influenced the dynamics of a population of big-eared woodrats (Neotoma macrotis) in a mixed oak woodland of coastal central California. We applied Pradel’s temporal symmetry models to our 21-year biannual capture–mark–recapture data set (1993–2014) to determine the effects of climatic factors on the woodrats’ apparent survival (Φ) and recruitment rate (f). Monthly Φ averaged 0.945 ± 0.001 and varied with season. Monthly f was 0.064 ± 0.001 in the wet season (f was fixed to 0 in the dry season). Monthly population growth rate (λ) varied from 0.996 ± 0.001 during the dry season to 1.001 ± 0.001 during the wet season, which indicated a stable population (0.999 ± 0.001). Total rainfall from the previous season and mean temperature during the same season positively influenced Φ and f. By contrast, Φ and f were negatively influenced by mean temperature from the previous season and total rainfall in the same season. The resulting λ fluctuated with total rainfall, particularly in the wet season. Our results suggest that the big-eared woodrat population may not be substantially affected by warm temperatures per se, potentially because of the microclimate provided by its stick houses. We also discuss its adaptability to local food resources and relatively slow life history relative to other cricetids, and propose that the big-eared woodrat population may be equipped to cope with future climate change.

A Visual Analytics Approach for Ecosystem Dynamics based on Empirical Dynamic Modeling

An important approach for scientific inquiry across many disciplines involves using observational time series data to understand the relationships between key variables to gain mechanistic insights into the underlying rules that govern the given system. In real systems, such as those found in ecology, the relationships between time series variables are generally not static; instead, these relationships are dynamical and change in a nonlinear or state-dependent manner. To further understand such systems, we investigate integrating methods that appropriately characterize these dynamics (i.e., methods that measure interactions as they change with time-varying system states) with visualization techniques that can help analyze the behavior of the system. Here, we focus on empirical dynamic modeling (EDM) as a state-of-the-art method that specifically identifies causal variables and measures changing state-dependent relationships between time series variables. Instead of using approaches centered on parametric equations, EDM is an equation-free approach that studies systems based on their dynamic attractors. We propose a visual analytics system to support the identification and mechanistic interpretation of system states using an EDM-constructed dynamic graph. This work, as detailed in four analysis tasks and demonstrated with a GUI, provides a novel synthesis of EDM and visualization techniques such as brush-link visualization and visual summarization to interpret dynamic graphs representing ecosystem dynamics. We applied our proposed system to ecological simulation data and real data from a marine mesocosm study as two key use cases. Our case studies show that our visual analytics tools support the identification and interpretation of the system state by the user, and enable us to discover both confirmatory and new findings in ecosystem dynamics. Overall, we demonstrated that our system can facilitate an understanding of how systems function beyond the intuitive analysis of high-dimensional information based on specific domain knowledge.

Regularized S-Map Reveals Varying Bacterial Interactions

There is a growing awareness that bacterial interactions follow a highly nonlinear pattern in reality. However, it is challenging to track the varying bacterial interactions using pairwise correlation analysis, which fails to explore their potential effects on the behavior of microbes. Here, we utilized a regularized sequential locally weighted global linear map (S-map) to capture the varying interspecific interactions from the time series data of a bacterial community under exposure to nitrite. Our results show that bacterial interactions are highly variable and that asymmetric interactions dominate the interaction pattern in a community. Furthermore, we propose a Jacobian coefficient-based statistical method to predict the harmony level of a bacterial community at each successive ecosystem state. The results show that the bacterial community exhibits a higher harmony level in nitrite-treated samples than in the control group. We show that the community harmony level is positively associated with the specific endogenous respiration rates and biofilm formation of the culture. In addition, the community tends to process lower diversity and structural stability under zero- and high-nitrite stresses. We demonstrate that the harmony level, rather than structural stability, is a useful index for unveiling the underlying mechanism of bacterial performance. Overall, the regularized S-map can help us to understand bacterial interactions in ecosystems more accurately than previous approaches.IMPORTANCE It has long been acknowledged that bacterial interactions play important roles in community structure and function. Revealing the interaction variability can allow an understanding of how bacteria respond to perturbation and why bacterial community performance changes. Such information should improve our skills in engineering bacterial communities (e.g., in a wastewater treatment plant) and achieve better removal performance and lower energy consumption.

Rodents' responses to manipulated plant litter and seed densities: implications for restoration

Rodent populations in arid grasslands do not always track seed production, possibly due to high levels of plant litter. When natural disturbances are suppressed, litter accumulates becoming physically complex, causing rodents to harvest fewer seeds per equivalent time foraging. It also alters security from predation. Restoring natural disturbances may be an important element in conserving rodent communities. The aim of this study was to assess the influence of two levels of plant litter cover and seed densities on nocturnal rodent population characteristics in a semiarid grassland. Specifically, I hypothesized that kangaroo rats, pocket mice, grasshopper mice, and total rodents would be higher in the sparse plant litter treatment than dense litter, whereas deer mice would be lower in sparse plots. I further hypothesized that kangaroo rats and deer mice would be higher in the seed augmented treatment compared to the unseeded treatment. A prescribed fire removed litter in four of eight plots prior to sowing native seeds 1 year postfire into two burned and two unburned plots. Rodents were live-trapped during spring and fall 1 year. Sparse litter treatment had higher total rodent abundance, biomass, and frequency of offspring compared to dense plots indicating use of stored seeds. Banner-tailed kangaroo rats had higher abundance, implying reduced predation risk. Pocket mice body mass was greater in dense plots. After winter, seeded plots had higher kangaroo rat body mass and grasshopper mice abundance than unseeded, reflecting the use of stored seeds. These short term results demonstrate litter's physical complexity may be equivalent to seed pulses on the responses of nocturnal rodents. Managers might positively influence grassland rodents by providing a mosaic of varying levels of plant litter.

Host-microbiota interaction helps to explain the bottom-up effects of climate change on a small rodent species

The population cycles of small rodents have puzzled biologists for centuries. There is a growing recognition of the cascading effects of climate change on the population dynamics of rodents. However, the ultimate cause for the bottom-up effects of precipitation is poorly understood, from a microbial perspective. Here, we conducted a precipitation manipulation experiment in the field, and three feeding trials with controlled diets in the laboratory. We found precipitation supplementation facilitated the recovery of a perennial rhizomatous grass (Leymus chinensis) species, which altered the diet composition and increase the intake of fructose and fructooligosaccharides for Brandt’s vole. Lab results showed that this nutrient shift was accompanied by the modulation of gut microbiota composition and functional pathways (especially for the degradation or biosynthesis of L-histidine). Particularly, the relative abundance of Eubacterium hallii was consistently increased after feeding voles with more L. chinensis, fructose or fructooligosaccharide. These modulations ultimately increased the production of short chain fatty acids (SCFAs) and boosted the growth of vole. This study provides evidence that the precipitation pulses cascades through the plant community to affect rodent gut microbiome. Our results highlight the importance of considering host-microbiota interaction when investigating rodent population responses to climate change.

Effects of density dependence and climatic factors on population dynamics of Cricetulus barabensis: a 25-year field study

Rodents often act as keystone species in communities and play important roles in shaping structures and functions of many ecosystems. Understanding the underlying mechanisms of population fluctuation in rodents is therefore of great interest. Using the data from a 25-year field survey carried out in Inner Mongolia, China, we explored the effects of density dependence, local climatic factors, and a large-scale climatic perturbation (El Niño–Southern Oscillation) on the population dynamics of the striped hamster (Cricetulus barabensis), a rodent widely distributed in northern China. We detected a strong negative density-dependent effect on the population dynamics of C. barabensis. Rainfall had a significant positive effect on population change with a 1-year lag. The pregnancy rate of C. barabensis was negatively affected by the annual mean temperature in the current year, but positively associated with the population density in the current year and the annual Southern Oscillation Index in the previous year. Moving-window analyses suggested that, with a window length of 12 years, there was a significant interaction between rainfall and density dependence, with increasing rainfall alleviating the negative effect of density dependence. As C. barabensis often causes agricultural damage and can transmit zoonotic diseases to human beings, our results also have implications for pest and disease control.

Does intraspecific competition among Allenby's gerbils lead to an Ideal Free Distribution across foraging patches?

Employing the Ideal Free Distribution (IFD) principle as a tool, we investigated how Allenby's gerbils (Gerbillus andersoni allenbyi) utilized food patches within and moved between connected quadrants (i.e., 'habitats') in a large outdoor semi-natural enclosure. These habitats differed in initial forager densities, but provided equal numbers of standardized food patches that provided equal rewards (i.e. food) and costs (i.e. predation risk, metabolic, and missed opportunity). We quantified the gerbils' giving-up-densities (GUDs) within foraging patches and recorded their daily distribution between habitats. Individual gerbils were tagged with unique bar-coded numbers to compare their locations within and across habitats. The mean number of gerbil foragers (9.1 and 8.9 individuals, respectively) and GUDs evened out across habitats over time. Despite this, the distribution of gerbils did not remain static within foraging patches; instead, gerbils altered their use of patches across and within habitats on a nightly basis. This may be due to a combination of factors including, high levels of interference competition between foragers at patches, a lag effect before the gerbils perceived changes in competition intensity with the arrival and departure of individuals, and gerbils having imperfect knowledge of their environment. Furthermore, the pattern of microhabitat (open vs bush patches) use by gerbils differed over time, indicating that despite the distribution of gerbils and their GUDs evening out between habitats, they still preferred foraging from safer bush patches over riskier open patches. This study provides insights into how under low predation risk, strong levels of intraspecific competition can shape the distribution of foragers across and within habitats.

Range-wide genetic structure of a cooperative mouse in a semi-arid zone: Evidence for panmixia

Landscape topography and the mobility of individuals will have fundamental impacts on a species' population structure, for example by enhancing or reducing gene flow and therefore influencing the effective size and genetic diversity of the population. However, social organization will also influence population genetic structure. For example, species that live and breed in cooperative groups may experience high levels of inbreeding and strong genetic drift. The western pebble-mound mouse (Pseudomys chapmani), which occupies a highly heterogeneous, semi-arid landscape in Australia, is an enigmatic social mammal that has the intriguing behaviour of working cooperatively in groups to build permanent pebble mounds above a subterranean burrow system. Here, we used both nuclear (microsatellite) and mitochondrial (mtDNA) markers to analyse the range-wide population structure of western pebble-mound mice sourced from multiple social groups. We observed high levels of genetic diversity at the broad scale, very weak genetic differentiation at a finer scale and low levels of inbreeding. Our genetic analyses suggest that the western pebble-mound mouse population is both panmictic and highly viable. We conclude that high genetic connectivity across the complex landscape is a consequence of the species' ability to permeate their environment, which may be enhanced by "boom-bust" population dynamics driven by the semi-arid climate. More broadly, our results highlight the importance of sampling strategies to infer social structure and demonstrate that sociality is an important component of population genetic structure.

Foraging strategies of individual silky pocket mice over a boom-bust cycle in a stochastic dryland ecosystem

Small mammals use multiple foraging strategies to compensate for fluctuating resource quality in stochastic environments. These strategies may lead to increased dietary overlap when competition for resources is strong. To quantify temporal contributions of high (C₃) versus low quality (C₄) resources in diets of silky pocket mice (Perognathus flavus), we used stable carbon isotope (δ¹³C) analysis of 1391 plasma samples collected over 2 years. Of these, 695 samples were from 170 individuals sampled ≥ 3 times across seasons or years, allowing us to assess changes in dietary breadth at the population and individual levels across a boom-bust population cycle. In 2014, the P. flavus population increased to 412 captures compared to 8 captures in prior and subsequent years, while populations of co-occurring small mammals remained stable. As intraspecific competition increased, the population-wide dietary niche of P. flavus did not change, but individual specialization increased significantly. During this period, ~ 27% (41/151) of individuals sampled specialized on C₃ resources, which were abundant during the spring and previous fall seasons. Most of the remaining individuals were C₃-C₄ generalists (64%) (96/151), and only 9% (14/151) specialized on C₄ resources. In 2015, P. flavus population density and resource availability declined, individual dietary breadth expanded (84% generalists), no C₃ specialists were found, and specialization on C₄ resources increased (16%). Our results demonstrate a high degree of inter-individual plasticity in P. flavus foraging strategies, which has implications for how this species will respond to environmental change that is predicted to decrease C₃ resources in the future.

Density feedbacks mediate effects of environmental change on population dynamics of a semidesert rodent

Population dynamics are the result of an interplay between extrinsic and intrinsic environmental drivers. Predicting the effects of environmental change on wildlife populations therefore requires a thorough understanding of the mechanisms through which different environmental drivers interact to generate changes in population size and structure. In this study, we disentangled the roles of temperature, food availability and population density in shaping short- and long-term population dynamics of the African striped mouse, a small rodent inhabiting a semidesert with high intra- and interannual variation in environmental conditions. We parameterized a female-only stage-structured matrix population model with vital rates depending on temperature, food availability and population density, using monthly mark-recapture data from 1609 mice trapped over 9 years (2005-2014). We then applied perturbation analyses to determine relative strengths and demographic pathways of these drivers in affecting population dynamics. Furthermore, we used stochastic population projections to gain insights into how three different climate change scenarios might affect size, structure and persistence of this population. We identified food availability, acting through reproduction, as the main driver of changes in both short- and long-term population dynamics. This mechanism was mediated by strong density feedbacks, which stabilized the population after high peaks and allowed it to recover from detrimental crashes. Density dependence thus buffered the population against environmental change, and even adverse climate change scenarios were predicted to have little effect on population persistence (extinction risk over 100 years <5%) despite leading to overall lower abundances. Explicitly linking environment-demography relationships to population dynamics allowed us to accurately capture past population dynamics. It further enabled establishing the roles and relative importances of extrinsic and intrinsic environmental drivers, and we conclude that doing this is essential when investigating impacts of climate change on wildlife populations.

Two decades of climate driving the dynamics of functional and taxonomic diversity of a tropical small mammal community in western Mexico

Understanding the effects of global climate disruption on biodiversity is important to future conservation efforts. While taxonomic diversity is widely studied, functional diversity of plants, and recently animals, is receiving increasing attention. Most studies of mammals are short-term, focus on temperate habitats, and rely on traits described in the literature rather than generating traits from observations. Unlike previous studies, this long-term field study assessed the factors driving the functional and taxonomic diversity of small-mammal assemblages in dry tropical forests using both traits recorded from literature and a demographic database. We assessed the drivers (abundance and biomass, temperature and rainfall) of taxonomic richness and functional diversity for two rain-driven seasons in two adjacent but distinct forests-upland and lowland (arroyo or riparian) forests. Our analysis found that rainfall, both seasonal and atypical, was the primary factor driving functional and taxonomic diversity of small-mammal assemblages. Functional responses differed between the two types of forests, however, with effects being stronger in the harsher conditions of the upland forests than in the less severe conditions prevailing in the arroyo (riparian) forest. The latter also supports a richer, more diverse, and more stable small-mammal assemblage. These findings highlight the importance of climate to tropical biological diversity, as extreme climate events (hurricanes, droughts and floods) and disruption of rainfall patterns were shown to decrease biodiversity. They also support the need to preserve these habitats, as their high taxonomic diversity and functional redundancy makes them resilient against global climate disruption and local extreme events. Tropical dry forests constitute a potential reservoir for biodiversity and the ecosystem services they provide. Unfortunately, these forests are among the most endangered terrestrial ecosystems because of deforestation and the likely impacts of global climate disruption.

Long-term changes in abundances of Sonoran Desert lizards reveal complex responses to climatic variation

Understanding how climatic variation affects animal populations and communities is essential for addressing threats posed by climate change, especially in systems where impacts are projected to be high. We evaluated abundance dynamics of five common species of diurnal lizards over 25 years in a Sonoran Desert transition zone where precipitation decreased and temperature increased across time, and assessed hypotheses for the influence of climatic flux on spatiotemporal variation in abundances. We repeatedly surveyed lizards in spring and summer of each year at up to 32 sites, and used hierarchical mixture models to estimate detection probabilities, abundances, and population growth rates. Among terrestrial species, abundances of a short-lived, winter-spring breeder increased markedly by an estimated 237%-285% across time, while two larger spring-summer breeders with higher thermal preferences declined by up to 64%. Abundances of two arboreal species that occupy shaded and thus sheltered microhabitats fluctuated but did not decline systematically. Abundances of all species increased with precipitation at short lag times (1-1.5 years) likely due to enhanced food availability, but often declined after periods of high precipitation at longer lag times (2-4 years) likely due to predation and other biotic pressures. Although rising maximum daily temperatures (T_max ) are expected to drive global declines of lizards, associations with T_max were variable and weak for most species. Instead, abundances of all species declined with rising daily minimum temperatures, suggesting degradation of cool refugia imposed widespread metabolic or other costs. Our results suggest climate warming and drying are having major impacts on lizard communities by driving declines in species with traits that augment exposure to abiotic extremes and by modifying species interactions. The complexity of patterns we report indicates that evaluating and responding to the influence of climate change on biodiversity must consider a broad array of ecological processes.

Desert mammal populations are limited by introduced predators rather than future climate change

Climate change is predicted to place up to one in six species at risk of extinction in coming decades, but extinction probability is likely to be influenced further by biotic interactions such as predation. We use structural equation modelling to integrate results from remote camera trapping and long-term (17-22 years) regional-scale (8000 km²) datasets on vegetation and small vertebrates (greater than 38 880 captures) to explore how biotic processes and two key abiotic drivers influence the structure of a diverse assemblage of desert biota in central Australia. We use our models to predict how changes in rainfall and wildfire are likely to influence the cover and productivity of the dominant vegetation and the impacts of predators on their primary rodent prey over a 100-year timeframe. Our results show that, while vegetation cover may decline due to climate change, the strongest negative effect on prey populations in this desert system is top-down suppression from introduced predators.

Implications of animal water balance for terrestrial food webs

Recent research has documented shifts in per capita trophic interactions and food webs in response to changes in environmental moisture, from the top-down (consumers to plants), rather than solely bottom-up (plants to consumers). These responses may be predictable from effects of physiological, behavioral, and ecological traits on animal water balance, although predictions could be modified by energy or nutrient requirements, the risk of predation, population-level responses, and bottom-up effects. Relatively little work has explicitly explored food web effects of changes in animal water balance, despite the likelihood of widespread relevance, including during periodic droughts in mesic locations, where taxa may lack adaptations for water conservation. More research is needed, particularly in light of climate change and hydrological alteration.

Changes in abundance and reproductive activity of small arid-zone murid rodents on an active cattle station in central Australia

Context Boom and bust population cycles are characteristic of many arid-zone rodents, but it is unknown to what extent these dynamics might be influenced by the presence of invasive rodents, such as the house mouse (Mus musculus) in Australia. Aim To determine whether the presence of M. musculus can have negative consequences on the population abundance and reproduction of two old Australian endemic rodents (the spinifex hopping mouse, Notomys alexis, and sandy inland mouse, Pseudomys hermannsburgensis). Methods The study took place on the sand dunes of a cattle station in central Australia. Population abundance was estimated as the number of individuals caught in small mammal traps, and female reproductive condition by external examination and, in a few cases, euthanasia and inspection of the reproductive tract. Key results Two synchronous periods of high abundance of N. alexis and M. musculus occurred several months after significant rainfall events, whereas the abundance of P. hermannsburgensis was consistently low. No reproduction took place in N. alexis or M. musculus when populations had reached high abundance. During low-rainfall periods, M. musculus was not detected on the sand dunes, and the two endemic species were sparsely distributed, with reproduction occasionally being evident. Conclusions During dry periods, M. musculus contracted back to refuges around the homestead and, after significant rainfall, it expanded onto the sand dunes and became abundant at the same time as did N. alexis. In contrast, and unlike in areas where M. musculus was generally rare, P. hermannsburgensis always remained at a low abundance. These patterns suggest that in areas of the natural environment close to human-modified sites, populations of at least one species of an old endemic rodent are supressed by the presence of M. musculus. Reproduction did not occur in the old endemics at times of high M. musculus abundance, but did take place in spring/early summer, even in some dry years. Implications The spread of M. musculus into the Australian arid zone may have had negative impacts on the population dynamics of P. hermannsburgensis. These findings suggest that the presence of human settlements has resulted in refuges for house mice, which periodically spread out into the natural environment during ‘boom’ times and adversely affect the natural population cycle of ecologically similar species such as P. hermannsburgensis.

Tracking and forecasting ecosystem interactions in real time

Evidence shows that species interactions are not constant but change as the ecosystem shifts to new states. Although controlled experiments and model investigations demonstrate how nonlinear interactions can arise in principle, empirical tools to track and predict them in nature are lacking. Here we present a practical method, using available time-series data, to measure and forecast changing interactions in real systems, and identify the underlying mechanisms. The method is illustrated with model data from a marine mesocosm experiment and limnologic field data from Sparkling Lake, WI, USA. From simple to complex, these examples demonstrate the feasibility of quantifying, predicting and understanding state-dependent, nonlinear interactions as they occur in situ and in real time--a requirement for managing resources in a nonlinear, non-equilibrium world.

Detecting and modelling delayed density-dependence in abundance time series of a small mammal (Didelphis aurita)

We study the population size time series of a Neotropical small mammal with the intent of detecting and modelling population regulation processes generated by density-dependent factors and their possible delayed effects. The application of analysis tools based on principles of statistical generality are nowadays a common practice for describing these phenomena, but, in general, they are more capable of generating clear diagnosis rather than granting valuable modelling. For this reason, in our approach, we detect the principal temporal structures on the bases of different correlation measures, and from these results we build an ad-hoc minimalist autoregressive model that incorporates the main drivers of the dynamics. Surprisingly our model is capable of reproducing very well the time patterns of the empirical series and, for the first time, clearly outlines the importance of the time of attaining sexual maturity as a central temporal scale for the dynamics of this species. In fact, an important advantage of this analysis scheme is that all the model parameters are directly biologically interpretable and potentially measurable, allowing a consistency check between model outputs and independent measurements.

Rapid warming and drought negatively impact population size and reproductive dynamics of an avian predator in the arid southwest

Avian communities of arid ecosystems may be particularly vulnerable to global climate change due to the magnitude of projected change for desert regions and the inherent challenges for species residing in resource limited ecosystems. How arid-zone birds will be affected by rapid increases in air temperature and increased drought frequency and severity is poorly understood because avian responses to climate change have primarily been studied in the relatively mesic northern temperate regions. We studied the effects of increasing air temperature and aridity on a Burrowing Owl (Athene cunicularia) population in the southwestern United States from 1998 to 2013. Over 16 years, the breeding population declined 98.1%, from 52 pairs to 1 pair, and nest success and fledgling output also declined significantly. These trends were strongly associated with the combined effects of decreased precipitation and increased air temperature. Arrival on the breeding grounds, pair formation, nest initiation, and hatch dates all showed significant delays ranging from 9.4 to 25.1 days over 9 years, which have negative effects on reproduction. Adult and juvenile body mass decreased significantly over time, with a loss of 7.9% mass in adult males and 10.9% mass in adult females over 16 years, and a loss of 20.0% mass in nestlings over 8 years. Taken together, these population and reproductive trends have serious implications for local population persistence. The southwestern United States has been identified as a climate change hotspot, with projections of warmer temperatures, less winter precipitation, and an increase in frequency and severity of extreme events including drought and heat waves. An increasingly warm and dry climate may contribute to this species' decline and may already be a driving force of their apparent decline in the desert southwest.

Extreme climatic events drive mammal irruptions: regression analysis of 100-year trends in desert rainfall and temperature

Extreme climatic events, such as flooding rains, extended decadal droughts and heat waves have been identified increasingly as important regulators of natural populations. Climate models predict that global warming will drive changes in rainfall and increase the frequency and severity of extreme events. Consequently, to anticipate how organisms will respond we need to document how changes in extremes of temperature and rainfall compare to trends in the mean values of these variables and over what spatial scales the patterns are consistent. Using the longest historical weather records available for central Australia – 100 years – and quantile regression methods, we investigate if extreme climate events have changed at similar rates to median events, if annual rainfall has increased in variability, and if the frequency of large rainfall events has increased over this period. Specifically, we compared local (individual weather stations) and regional (Simpson Desert) spatial scales, and quantified trends in median (50th quantile) and extreme weather values (5th, 10th, 90th, and 95th quantiles). We found that median and extreme annual minimum and maximum temperatures have increased at both spatial scales over the past century. Rainfall changes have been inconsistent across the Simpson Desert; individual weather stations showed increases in annual rainfall, increased frequency of large rainfall events or more prolonged droughts, depending on the location. In contrast to our prediction, we found no evidence that intra-annual rainfall had become more variable over time. Using long-term live-trapping records (22 years) of desert small mammals as a case study, we demonstrate that irruptive events are driven by extreme rainfalls (>95th quantile) and that increases in the magnitude and frequency of extreme rainfall events are likely to drive changes in the populations of these species through direct and indirect changes in predation pressure and wildfires.

Inter- and intra-specific patterns of density dependence and population size variability in Salmoniformes

Population dynamics are typically affected by a combination of density-independent and density-dependent factors, the latter of which have been conceptually and theoretically linked with how variable population sizes are over time-which in turn has been tied to how prone populations are to extinction. To address evidence for the occurrence of density dependence and its relationship with population size variability (pv), we quantified each of these for 126 populations of 8 species of Salmoniformes. Using random-effects models, we partitioned variation in the strength of density dependence and the magnitude of pv between and within species and estimated the correlation of density dependence and population size variability at both the between- and within-species levels. We found that variation in the strength of density dependence was predominately within species (I(2) = 0.12 [corrected] variation in population size variability was distributed both between and within species (I(2) = 0.40). Contrary to theoretical and conceptual expectations, the strength of density dependence and the magnitude of population size variability were positively correlated at the between species level (r = 0.90), although this estimate had 95 % credibility intervals (Bayesian analogues to confidence intervals) that overlapped zero. The within-species correlation between density dependence and population size variability was not distinguishable from zero. Given that density dependence for Salmoniformes was highly variable within species, we next determined the joint effects of intrinsic (density-dependent) and extrinsic (density-independent) factors on the population dynamics of a threatened salmonid, the Lahontan cutthroat trout (Oncorhynchus clarkii henshawi). We found that density-dependent and -independent factors additively contributed to population dynamics. This finding suggests that the observed within-species variability in density dependence might be attributable to local differences in the strength of density-independent factors.

Climate controls on grass culm production over a quarter century in a tallgrass prairie

The flowering of grasses is a process critical to plant population dynamics and genetics, herbivore performance, and human health. To better understand the climate factors governing grass flowering, we analyzed the patterns of culm production over 25 years for three perennial tallgrass prairie species at Konza Prairie in Kansas, USA. The three species (Andropogon gerardii, Sorghastrum nutans, and Schizachyrium scoparium) all utilize the C4 photosynthetic pathway and were measured annually at the same locations for the past 25 years in an annually burned watershed. Culm production of all three species increased with higher growing-season soil moisture and precipitation but differed in their responses to water availability at different times during the growing season. Relative to Andropogon, Sorghastrum responded more to precipitation early in the growing season, and Schizachyrium responded more to precipitation late in the growing season. Flowering by each species also revealed a threshold relationship with late-season soil moisture at approximately 1 m depth, which likely is a proxy for season-long water balance. Although flowering can be influenced by conditions antecedent to the current growing season, neither soil moisture nor precipitation during the previous year influenced flowering over the 25-year period. Flowering culm production averaged 9% and 7% of total graminoid aboveground net primary production (ANPP) in the uplands and lowlands, respectively. Interannual variation in ANPP correlated only with Sorghastrum flowering, suggesting a predominant role of the species in ANPP responses to climate.

Temporal variation in the carrying capacity of a perennial grass population

Density dependence and, therefore, K (carrying capacity, equilibrium population size) are central to understanding and predicting changes in population size (N). Although resource levels certainly fluctuate, K has almost always been treated as constant in both theoretical and empirical studies. We quantified temporal variation in K by fitting extensions of standard population dynamic models to 16 annual censuses of a population of the perennial bunchgrass Bouteloua rigidiseta. Variable-K models provided substantially better fits to the data than did models that varied the potential rate of population increase. The distribution of estimated values of K was skewed, with a long right tail (i.e., a few "jackpot" years). The population did not track K closely. Relatively slow responses to changes in K combined with large, rapid changes in K sometimes caused N to be far from K. In 13%-20% of annual intervals, K was so much larger than N that the population's dynamics were best described by geometric growth and the population was, in effect, unregulated. Explicitly incorporating temporal variation in K substantially improved the realism of models with little increase in model complexity and provided novel information about this population's dynamics. Similar methods would be applicable to many other data sets.

A link between the North Atlantic Oscillation and measles dynamics during the vaccination period in England and Wales

Ecologists have become aware of the role played by interannual climatic variability on the temporal dynamics of infectious diseases. In this report, I present evidence from data on measles cases in England and Wales showing that during the post-vaccination period, the interannual variability of winter weather (represented by the North Atlantic Oscillation, NAO) influences the annual dynamics of the disease. Using annual measles data from seven cities and simple logistic models, this study reveals how, after vaccination, NAO increases its effects on measles fluctuations. In addition, this study shows that vaccination may be represented as a simple vertical and lateral perturbation effect (Royama's classification), by reducing the maximum per capita growth rate and the equilibrium number of infected individuals. The results suggest that vaccination will not lead to outbreaks of measles from regular cyclic to irregular chaotic dynamics. In contrast, because of the reduction in per capita growth rates, the disease dynamics appear to be more stable than during the pre-vaccination period. The analysis of annual data on infectious diseases may be useful for detecting long-term effects of climate and complements the classical analyses and modeling based on monthly or seasonal time-step data.