Multivariate Autoregressive State-space Research Articles

Different patterns of coupled predator-prey dynamics when the same species interact in different locations.

Individual predator and prey species exhibit coupled population dynamics in simple laboratory systems and simple natural communities. It is unclear how often such pairwise coupling occurs in more complex communities, in which an individual predator species might feed on several prey species and an individual prey species might be attacked by several predators. To examine this problem, we applied multivariate autoregressive state-space (MARSS) models to 5-year time-series of monthly surveys of a predatory fish, the eastern mosquitofish (Gambusia holbrooki), and its littoral zone prey species, the least killifish (Heterandria formosa), in three locations in north Florida. The MARSS models were consistent with coupled predator-prey dynamics at two of the three locations. In one of these two locations, the estimated densities of the two species displayed classic predator-prey oscillations. In the third location, there was a positive effect of killifish density on mosquitofish density but no detectable effect of mosquitofish density on killifish density. In all three locations, increased submergent vegetation cover was associated with increased prey density but not increased predator density. Eigenvalues analyses for the joint predator-prey dynamics indicated that one of the cyclic locations had more stable dynamics than the other locations. The three different patterns demonstrate that the dynamics of a pairwise predator-prey interaction emerge not only from the characteristics of the prey and the predator, but also those of the habitat and trophic web in which the predator and prey are embedded.

Using multivariate autoregressive state‐space models to examine stock structure of Greenland halibut in the North Atlantic

AbstractAccurate information on population structure is essential for effective fisheries management. Greenland halibut (Reinhardtius hippoglossoides) in the North Atlantic is managed as four separate offshore stocks. We use Multivariate Autoregressive State‐Space (MARSS) models to assess population structure by means of abundance and biomass trends in four regions (Norwegian Sea, Iceland, Southeast Greenland, and Northwest Atlantic) where three offshore stocks are recognized: (1) Baffin Bay–Davis Strait (Northwest Atlantic stock), (2) Southeast Greenland and Iceland (West Nordic stock (WNS)), and (3) the Barents and Norwegian Seas (Northeast Arctic stock). We formulated model alternatives, using bottom trawl survey data from each region for 1996–2019, to evaluate support for different population structures. Abundance and biomass observations from each region were linked to growth rate parameters in MARSS models and the impact of climate (North Atlantic Oscillation Index) and fishing (commercial catches) on stock dynamics was investigated. Top models identified the Northwest Atlantic as an independent population. Best‐fit models treated Greenland halibut in the WNS as two independent populations (east and west), with potential connections between eastern Iceland and the western Barents Sea. These results suggest a mismatch between current stock perception and management boundaries in the Northeast Atlantic.

Hydrodynamics structure plankton communities and interactions in a freshwater tidal estuary

AbstractDrivers of phytoplankton and zooplankton dynamics vary spatially and temporally in estuaries due to variation in hydrodynamic exchange and residence time, complicating efforts to understand controls on food web productivity. We conducted approximately monthly (2012–2019; n = 74) longitudinal sampling at 10 fixed stations along a freshwater tidal terminal channel in the San Francisco Estuary, California, characterized by seaward to landward gradients in water residence time, turbidity, nutrient concentrations, and plankton community composition. We used multivariate autoregressive state space (MARSS) models to quantify environmental (abiotic) and biotic controls on phytoplankton and mesozooplankton biomass. The importance of specific abiotic drivers (e.g., water temperature, turbidity, nutrients) and trophic interactions differed significantly among hydrodynamic exchange zones with different mean residence times. Abiotic drivers explained more variation in phytoplankton and zooplankton dynamics than a model including only trophic interactions, but individual phytoplankton–zooplankton interactions explained more variation than individual abiotic drivers. Interactions between zooplankton and phytoplankton were strongest in landward reaches with the longest residence times and the highest zooplankton biomass. Interactions between cryptophytes and both copepods and cladocerans were stronger than interactions between bacillariophytes (diatoms) and zooplankton taxa, despite contributing less biovolume in all but the most landward reaches. Our results demonstrate that trophic interactions and their relative strengths vary in a hydrodynamic context, contributing to food web heterogeneity within estuaries at spatial scales smaller than the freshwater to marine transition.

Timing of hydrologic anomalies direct impacts on migration traits in a flood pulse fishery system

Abstract Understanding adaptive reservoir management strategies that balance ecological outcomes with other objectives necessitates properly articulated environmental objectives. Aside from flood pulse extent related metrics, residual‐based descriptors provide robust descriptions of fish assemblage structure and harvest. We proposed a model framework based on spectral analysis of hydrologic variation and the Multivariate AutoRegressive State Space (MARSS) model to statistically quantify the effect sizes of hydrologic variation impacts on total fish catch and functional group (migration types) fish harvest and applied it to 17 years of fish harvest data from the Lower Mekong River Basin (LMB). Our findings suggest that duration and timing of hydrologic anomalies matter as much as their magnitude. Anomaly droughts coupled with strong pulse can benefit species if timed correctly. Longitudinal migrators were more sensitive to anomalous floods and droughts than other migratory species. Fish catch projections using effect sizes derived from historical data revealed that a well‐timed and protracted drought followed by a powerful flood pulse would be advantageous to the fishery, but a flood delay could negate such benefits. Synthesis and applications: We designed a spectral analysis framework to quantify hydrological variation and linked it with fishery harvest records. Quantified effect sizes of hydrologic anomalous events demonstrated one designed hydrograph for fishery benefits: properly timed prolong drought followed by a strong flood pulse. Our results add to a growing body of research that suggests ecological flows can be engineered. For most dams, the rule curve describing reservoir releases and resulting downstream hydrograph are designed in an ecological vacuum in which the objectives are to maximize human services—power production, flood control or navigation. Our work demonstrates that hydrograph can be designed to manage aspects of functional biodiversity directly. Though the exact shape of our hydrograph may not apply to other engineered river systems, we suggest that the approach can be applied generally, and globally both to developed and developing river basins. Specifically, a functional biodiversity rule curve could be optimized as an additional objective function in a multi‐objective optimization framework. Our methodology provides a quantitative method for deriving an ecological flow for this larger tradeoff analysis.

A 90-year record of lake whitefish Coregonus clupeaformis abundances in Michigan waters of the upper Laurentian Great Lakes

Fishery managers throughout the upper Laurentian Great Lakes are currently faced with a two-decade decline in abundance and harvest of lake whitefish Coregonus clupeaformis stocks. We used multivariate auto-regressive state-space (MARSS) models to develop long-term (90-year; 1929–2018) time-series of lake whitefish relative abundance based on commercial catch-per-effort (CPE) data for 13 statistical districts in State of Michigan waters, including 1836 and 1842 Treaty ceded waters, of Lakes Superior, Michigan, and Huron. CPE time-series were used to estimate historical baseline conditions, which were compared to more recent conditions, specifically with reference to select regulatory, environmental, and ecological conditions in each lake and fishing intensity. Population growth rates suggested that lake whitefish stocks responded: (1) negatively to high levels of harvest and expansion of sea lamprey Petromyzon marinus populations during the early-1900s; (2) positively to commercial fishery regulation and sea lamprey control during the late-1950s and early-1960s; and (3) negatively to establishment of dreissenid mussels Dreissena spp. in Lakes Michigan and Huron by 2005 and the recent period of low productivity in all three lakes since the mid- to late-2000s. When placed in a historical context, the most recent (2011–2018) lake whitefish abundances are low, intermediate, and high in 31 %, 46 %, and 23 % of all districts examined. Although environmental and ecological conditions likely drove recent declines, correlation analysis suggested that higher levels of fishing intensity were associated with greater district-specific declines in abundance during the last two decades (1999–2016), a period characterized by lower overall productivity and limited recruitment in most lake whitefish stocks.

Assessing the spatial and temporal organization of Red Kangaroo, Western Grey Kangaroo and Eastern Grey Kangaroo populations in eastern Australia using multivariate autoregressive state‐space models

SummaryTo identify patterns in the temporal dynamics and spatial organization of kangaroo subpopulations that are commercially harvested, we fitted multivariate autoregressive state‐space (MARSS) models to time series of kangaroo abundance from the main harvest zones of eastern Australia (1990–2019 for New South Wales and 1984–2019 for Queensland). We found that the responses of most populations to process variation, that is variation due to environmental or demographic stochasticity were correlated and that populations responded synchronously to environmental changes. Furthermore, we examined the influence of the covariates lagged rainfall, Southern Oscillation Index (SOI) and harvest rate on kangaroo abundance. We found that lagged rainfall had a positive influence, SOI and harvest rate had negative influences but the influence of covariates was not consistent across species or between subpopulations. In terms of population spatial structure, the analysis identified two subpopulations of Red Kangaroo (Osphranter rufus) in New South Wales and four subpopulations of grey kangaroos (a combination of Eastern Grey Kangaroo (Macropus giganteus) and Western Grey Kangaroo (Macropus fuliginosus)), which appeared to be associated with a rainfall gradient from east‐west. In Queensland, separate subpopulations were identified in each of the three main harvest management zones for both Red Kangaroo and Eastern Grey Kangaroo. The implications of these results for the management of commercially harvested kangaroos are discussed.

Drought effects on invertebrate metapopulation dynamics and quasi-extinction risk in an intermittent river network.

Ecological communities can remain stable in the face of disturbance if their constituent species have different resistance and resilience strategies. In turn, local stability scales up regionally if heterogeneous landscapes maintain spatial asynchrony across discrete populations-but not if large-scale stressors synchronize environmental conditions and biological responses. Here, we hypothesized that droughts could drastically decrease the stability of invertebrate metapopulations both by filtering out poorly adapted species locally, and by synchronizing their dynamics across a river network. We tested this hypothesis via multivariate autoregressive state-space (MARSS) models on spatially replicated, long-term data describing aquatic invertebrate communities and hydrological conditions in a set of temperate, lowland streams subject to seasonal and supraseasonal drying events. This quantitative approach allowed us to assess the influence of local (flow magnitude) and network-scale (hydrological connectivity) drivers on invertebrate long-term trajectories, and to simulate near-future responses to a range of drought scenarios. We found that fluctuations in species abundances were heterogeneous across communities and driven by a combination of hydrological and stochastic drivers. Among metapopulations, increasing extent of dry reaches reduced the abundance of functional groups with low resistance or resilience capacities (i.e. low ability to persist in situ or recolonize from elsewhere, respectively). Our simulations revealed that metapopulation quasi-extinction risk for taxa vulnerable to drought increased exponentially as flowing habitats contracted within the river network, whereas the risk for taxa with resistance and resilience traits remained stable. Our results suggest that drought can be a synchronizing agent in riverscapes, potentially leading to regional quasi-extinction of species with lower resistance and resilience abilities. Better recognition of drought-driven synchronization may increase realism in species extinction forecasts as hydroclimatic extremes continue to intensify worldwide.

Potential fluctuation of δ

Many studies have used stable isotope ratios in marine organisms to infer ecology, but few studies have considered potential temporal changes of baseline isotopic values. Such changes can reflect natural and anthropogenic changes in regional nutrient cycling and food-web ecology. This study evaluated changes in carbon and nitrogen stable isotope ratios (δ13C and δ15N respectively) of three small pelagic forage fish species (sardine (Sardinops melanostictus), anchovy (Engraulis japonicus), chub mackerel (Scomber japonicus)) in the Sea of Japan and East China Sea over 22 years (1998–2019). We calculated annual means of both stable isotope ratios with a generalised linear model (GLM) and time-series analysis (multivariate autoregressive state-space, MARSS) to account for environmental and anthropogenic effects. The highest and lowest least-square means of δ13C on the basis of the GLM were –17.2‰ (in 2005) and –18.5‰ (in 2013) respectively, and values declined drastically from 2005 to 2006. δ15N values decreased gradually on the basis of MARSS time-series, or were stable on the basis of the GLM analysis. Trends of δ13C and δ15N of forage fish species can be affected by the changes in baseline values, and changes in baseline values should, therefore, be taken into consideration when stable isotope ratios are used to infer food-web structures.

Anomaly Detection and Mode Identification in Multimode Processes Using the Field Kalman Filter

A process plant can have multiple modes of operation due to varying demand, availability of resources, or the fundamental design of a process. Each of these modes is considered as normal operation. Anomalies in the process are characterized as deviations away from normal operation. Such anomalies can be indicative of developing faults which, if left unresolved, can lead to failures and unplanned downtime. The field Kalman filter (FKF) is a model-based approach, which is adopted in this article for monitoring a multimode process. Previously, the FKF has been applied in process monitoring to differentiate normal operation from known faulty modes of operation. This article extends the FKF so that it may detect occurrences of anomalies and differentiate them from the various normal modes of operation. A method is proposed for off-line training an FKF monitoring model and online monitoring. The off-line part comprises training an FKF model based on multivariate autoregressive state-space (MARSS) models fitted to historical process data. A monitoring indicator is also introduced. Online monitoring, based on the FKF for anomaly detection and mode identification, is demonstrated using a simulated multimode process. The performance of the proposed method is also demonstrated using data obtained from a pilot-scale multiphase flow facility. The results show that the method can be applied successfully for anomaly detection and mode identification.

Incorporating spatial synchrony in the status assessment of a threatened species with multivariate analysis

Spatial synchrony—correlated abundance fluctuations among distinct populations—is associated with increased extinction risk but is not a component of widely-used extinction risk assessments (e.g., IUCN Red List, U.S. Fish and Wildlife Service's Species Status Assessment). Alongside traditional viability metrics (i.e., the number of populations, their spatial extent, the status of each population), consideration of spatial synchrony in these assessments may provide additional insight into extinction risk as well as the relative importance of intrinsic and extrinsic factors on population dynamics. We demonstrate a method for estimating abundance trends in populations of the endangered freshwater fish, the amber darter (Percina antesella), while simultaneously assessing empirical support for existence of spatial synchrony among its two populations in the Conasauga and Etowah rivers in Georgia, U.S.A. Our analysis was performed using multivariate autoregressive state-space (MARSS) models with annual sampling data from 1996 to 2018 at 16 sites distributed between the two rivers. Our results indicate that amber darter populations have declined substantially, with 9% annual losses in both the Conasauga and Etowah rivers, suggesting rangewide imperilment. Furthermore, model selection indicated little support for models with fully independent dynamics between rivers, which may compound overall extinction risk. This analysis demonstrates the utility of tools such as MARSS models for assessing spatial synchrony and long-term population trajectories of imperiled species, resulting in improved vulnerability assessments that do not assume independence among separate populations.

Zone specific trends in coral cover, genera and growth-forms in the World-Heritage listed Ningaloo Reef

On coral reefs, changes in the cover and relative abundance of hard coral taxa often follow disturbance. Although the ecological responses of common coral taxa have been well documented, little is known about the ecological responses of uncommon coral taxa or of coral morphological groups across multiple adjacent reef zones. We used Multivariate Auto-Regressive State-Space modelling to assess the rate and direction of change of hard coral cover across a variety of coral genera, growth forms, and susceptibility to bleaching and physical damage covering multiple reef zones at northern Ningaloo Reef in Western Australia. Trends were assessed between 2007 and 2016, during which multiple episodic disturbances occurred including cyclones and a heatwave. We provide evidence of zone specific trends, not only in total hard coral cover, but also in taxonomic and morphological groups of corals at Ningaloo Reef. Declines in total coral cover on the reef flat corresponded with declines in fast growing corals, particularly Acropora. In contrast, total coral cover on the reef slope and inshore (lagoon) did not undergo significant change, despite divergent trajectories of individual genera. Importantly, we also show that changes in the composition of coral assemblages can be detected using a morphological based approach when changes are not evident using a taxonomic approach. Therefore, we recommend that future assessments of coral reef trends incorporate not just standard metrics such as total coral cover, but also metrics that provide for detailed descriptions of trends in common and uncommon taxa and morphological groups across multiple reef zones.

Drivers shaping episodic and gradual changes in phytoplankton community succession: Taxonomic versus functional groups

Describing temporal changes in phytoplankton communities is complicated owing to (i) multivariate environmental drivers, (ii) inter-specific relationships, and (iii) various species. With long-term research data from the lower Nakdong River from 1993 to 2016, we examined the temporal changes at two scales—episodic (from weekly to monthly) and long-term (yearly)—and screened the potential environmental drivers. Phytoplankton community component patterns were modeled with the drivers as covariates, using multivariate autoregressive state-space (MARSS) models, to assess their response to environmental drivers and biotic interactions. We assumed that compared to taxonomic classification, functional classification would obtain a better identification of community response to temporal variability. Over 24 years, the succession patterns of the dominant taxonomic and functional groups decreased in diversity, with the greatest decreases in biomass of Bacillariophyceae and group D (mainly the diatom Stephanodiscus hantzschii), and coincided with the introduction of group H1 (dinitrogen-fixing nostocaleans). The potential drivers for these changes were precipitation, water level, and total nitrogen (TN) for taxonomic groups and TN, total phosphorus, and euphotic zone depth for functional groups. The results of the MARSS model and temporal trends for each driver indicated that the increases in the water level and light availability were mostly related with the taxonomic and functional groups, respectively. The model for functional groups proposed a total of 24 significant inter-group relationships, where five relationships supported the succession patterns of dominant groups in the Nakdong River. Combined with the effects of increased light availability, a positive relationship between groups H1 and M (mainly Cyanobacteria and Microcystis aeruginosa) appears to induce cyanobacterial bloom development over a long period. These results can be fundamental information for river system management concerning the resulting cascading effects of changes in environmental drivers and inter-group relationships on the phytoplankton community composition.

Making the most of incomplete long-term datasets: the MARSS solution

ABSTRACT Long-term field-based monitoring is essential to develop a deep understanding of how ecosystems function and to identify species at risk of decline. However, conducting long-term field-based research poses some unique challenges due to the harsh environmental conditions or extreme weather events that may be encountered. Such conditions are especially likely to occur in arid environments. Fieldwork issues can arise from vehicle breakdowns, wildfires and heavy rainfall events, all of which can delay or even cancel data collection. In addition, long-term monitoring typically requires multiple observers, which may add observation bias to estimates of measured parameters. Thus there is an increasing need to develop new statistical techniques that take advantage of the power of long time-series datasets that also are incomplete. Here we discuss multivariate autoregressive state-space (MARSS) modelling; a relatively new statistical technique for modelling long-term time-series data. MARSS models allow u...

Combined use of trawl fishery and research vessel survey data in a multivariate autoregressive state-space (MARSS) model to improve the accuracy of abundance index estimates

Fishery-dependent catch per unit effort (CPUE) data have been used as an abundance index (AI) in fish stock assessments. However, fishery-dependent CPUE data are influenced not only by changes in fish abundance but also by other factors, such as the choice or restrictions of fishing grounds to operate. Accordingly, bias may arise in AIs due to a lack of data from unfished or rarely fished areas. To improve the accuracy of AI estimates, spatially arranged CPUE datasets from both trawl fisheries and research vessel surveys in the East China Sea were concurrently analyzed in the present study using a multivariate autoregressive state-space (MARSS) model. Survey datasets complemented information on stock status in the fishing areas where fishery-dependent datasets were limited. As a result, the combined use of datasets from both sources effectively improved the accuracy of estimates of AIs and the spatial distribution of the population density of each fish species.

Handling missing data in multivariate time series using a vector autoregressive model-imputation (VAR-IM) algorithm

Imputing missing data from a multivariate time series dataset remains a challenging problem. There is an abundance of research on using various techniques to impute missing, biased, or corrupted values to a dataset. While a great amount of work has been done in this field, most imputing methodologies are centered about a specific application, typically involving static data analysis and simple time series modelling. However, these approaches fall short of desired goals when the data originates from a multivariate time series. The objective of this paper is to introduce a new algorithm for handling missing data from multivariate time series datasets. This new approach is based on a vector autoregressive (VAR) model by combining an expectation and minimization (EM) algorithm with the prediction error minimization (PEM) method. The new algorithm is called a vector autoregressive imputation method (VAR-IM). A description of the algorithm is presented and a case study was accomplished using the VAR-IM. The case study was applied to a real-world data set involving electrocardiogram (ECG) data. The VAR-IM method was compared with both traditional methods list wise deletion and linear regression substitution; and modern methods Multivariate Auto-Regressive State-Space (MARSS) and expectation maximization algorithm (EM). Generally, the VAR-IM method achieved significant improvement of the imputation tasks as compared with the other two methods. Although an improvement, a summary of the limitations and restrictions when using VAR-IM is presented.

Salish Sea Chinook salmon exhibit weaker coherence in early marine survival trends than coastal populations

AbstractIdentifying factors that influence anadromous Pacific salmon (Oncorhynchus spp.) population dynamics is complicated by their diverse life histories and large geographic range. Over the last several decades, Chinook salmon (O. tshawytscha) populations from coastal areas and the Salish Sea have exhibited substantial variability in abundance. In some cases, populations within the Salish Sea have experienced persistent declines that have not rebounded. We analyzed a time series of early marine survival from 36 hatchery Chinook salmon populations spanning ocean entry years 1980–2008 to quantify spatial and temporal coherence in survival. Overall, we observed higher inter‐population variability in survival for Salish Sea populations than non‐Salish Sea populations. Annual survival patterns of Salish Sea populations covaried over smaller spatial scales and exhibited less synchrony among proximate populations relative to non‐Salish Sea populations. These results were supported by multivariate autoregressive state space (MARSS) models which predominantly identified region‐scale differences in survival trends between northern coastal, southern coastal, Strait of Georgia, and Puget Sound population groupings. Furthermore, Dynamic Factor Analysis (DFA) of regional survival trends showed that survival of southern coastal populations was associated with the North Pacific Gyre Oscillation, a large‐scale ocean circulation pattern, whereas survival of Salish Sea populations was not. In summary, this study demonstrates that survival patterns in Chinook salmon are likely determined by a complex hierarchy of processes operating across a broad range in spatial and temporal scales, presenting challenges to the management of mixed‐stock fisheries.

Using a multivariate auto-regressive state-space (MARSS) model to evaluate fishery resources abundance in the East China Sea, based on spatial distributional information

The abundance index (AI) is a representative indicator used to assess the state of fishery resources. Conventional AI is generally calculated by summing the catch per unit of effort (CPUE) weighted by the size of each fishing area. However, CPUE data has many missing values owing to annual changes in operational fishing areas, and this can lead to a considerable bias in the estimated AI. To obtain an unbiased AI, a multivariate auto-regressive state-space (MARSS) model was used to estimate and interpolate missing values in a spatially arranged, long-term bottom-trawl CPUE dataset for yellow seabream Dentex hypselosomus and largehead hairtail Trichiurus japonicus in the East China Sea. As expected, increasing the number of analyzed fishing grids improved interpolation accuracy, but remarkably increased the time required for the analysis. Reducing the maximum number of expectation–maximization (EM) iterations in the maximum likelihood procedure was an effective way to practically reduce analysis time, while keeping the accuracy of the estimation. Thus, this EM-reduction MARSS model was applied to the entire CPUE datasets of yellow seabream and largehead hairtail to address the annual shifts in their AIs and their seasonal migration.

Population assessment using multivariate time-series analysis: A case study of rockfishes in Puget Sound.

Estimating a population's growth rate and year‐to‐year variance is a key component of population viability analysis (PVA). However, standard PVA methods require time series of counts obtained using consistent survey methods over many years. In addition, it can be difficult to separate observation and process variance, which is critical for PVA. Time‐series analysis performed with multivariate autoregressive state‐space (MARSS) models is a flexible statistical framework that allows one to address many of these limitations. MARSS models allow one to combine surveys with different gears and across different sites for estimation of PVA parameters, and to implement replication, which reduces the variance‐separation problem and maximizes informational input for mean trend estimation. Even data that are fragmented with unknown error levels can be accommodated. We present a practical case study that illustrates MARSS analysis steps: data choice, model set‐up, model selection, and parameter estimation. Our case study is an analysis of the long‐term trends of rockfish in Puget Sound, Washington, based on citizen science scuba surveys, a fishery‐independent trawl survey, and recreational fishery surveys affected by bag‐limit reductions. The best‐supported models indicated that the recreational and trawl surveys tracked different, temporally independent assemblages that declined at similar rates (an average of −3.8% to −3.9% per year). The scuba survey tracked a separate increasing and temporally independent assemblage (an average of 4.1% per year). Three rockfishes (bocaccio, canary, and yelloweye) are listed in Puget Sound under the US Endangered Species Act (ESA). These species are associated with deep water, which the recreational and trawl surveys sample better than the scuba survey. All three ESA‐listed rockfishes declined as a proportion of recreational catch between the 1970s and 2010s, suggesting that they experienced similar or more severe reductions in abundance than the 3.8–3.9% per year declines that were estimated for rockfish populations sampled by the recreational and trawl surveys.

The long-term population dynamics of common wasps in their native and invaded range.

Populations of introduced species are often thought to perform differently, or experience different population dynamics, in their introduced range compared to their native habitat. Differences between habitats in climate, competition or natural enemies may result in populations with varying density dependence and population dynamics. We examined the long-term population dynamics of the invasive common wasp, Vespula vulgaris, in its native range in England and its invaded range in New Zealand. We used 39years of wasp density data from four sites in England, and 23years of data from six sites in New Zealand. Wasp population time series was examined using partial rate correlation functions. Gompertz population models and multivariate autoregressive state-space (MARSS) models were fitted, incorporating climatic variation. Gompertz models successfully explained 59-66% of the variation in wasp abundance between years. Density dependence in wasp populations appeared to act similarly in both the native and invaded range, with wasp abundance in the previous year as the most important variable in predicting intrinsic rate of increase (r). No evidence of cyclic population dynamics was observed. Both the Gompertz and MARSS models highlighted the role of weather conditions in each country as significant predictors of annual wasp abundance. The temporal evolution of wasp populations at all sites was best modelled jointly using a single latent dynamic factor for local trends, with the inclusion of a latent spring weather covariate. That same parsimonious multivariate model structure was optimal in both the native and invaded range. Density dependence is overwhelmingly important in predicting wasp densities and 'wasp years' in both the native and invaded range. Spring weather conditions in both countries have a major influence, probably through their impact on wasp colony initiation and early development. The population dynamics in the native range and invaded range show no evidence of cyclic boom-and-bust dynamics. Invasive species may not exhibit different population dynamics despite considerable variation in abundances throughout their distribution.

Climate change alters the reproductive phenology and investment of a lacustrine fish, the three-spine stickleback.

High-latitude lakes are particularly sensitive to the effects of global climate change, demonstrating earlier ice breakup, longer ice-free seasons, and increased water temperatures. Such physical changes have implications for diverse life-history traits in taxa across entire lake food webs. Here, we use a five-decade time series from an Alaskan lake to explore effects of climate change on growth and reproduction of a widely distributed lacustrine fish, the three-spine stickleback (Gasterosteus aculeatus). We used multivariate autoregressive state-space (MARSS) models to describe trends in the mean length for multiple size classes and to explore the influence of physical (date of ice breakup, surface water temperature) and biological (density of con- and heterospecifics) factors. As predicted, mean size of age 1 and older fish at the end of the growing season increased across years with earlier ice breakup and warmer temperatures. In contrast, mean size of age 0 fish decreased over time. Overall, lower fish density and warmer water temperatures were associated with larger size for all cohorts. Earlier ice breakup was associated with larger size for age 1 and older fish but, paradoxically, with smaller size of age 0 fish. To explore this latter result, we used mixing models on age 0 size distributions, which revealed an additional cohort in years with early ice breakup, lowering the mean size of age 0 fish. Moreover, early ice breakup was associated with earlier breeding, evidenced by earlier capture of age 0 fish. Our results suggest that early ice breakup altered both timing and frequency of breeding; three-spine stickleback spawned earlier and more often in response to earlier ice breakup date. While previous studies have shown the influence of changing conditions in northern lakes on breeding timing and growth, this is the first to document increased breeding frequency, highlighting another pathway by which climate change can alter the ecology of northern lakes.