Population Time Series Research Articles

Tracking restoration of population diversity via the portfolio effect

Abstract Declines in diversity among populations managed together have diminished aggregate stability through a decreased portfolio effect. Although the portfolio effect has been quantified in a variety of ways, management recommendations for the recovery of lost diversity rarely specify the stability benefits possible through such improvements. We introduce a metric, the Diversity Deficit (DD), that relates past losses and potential gains in aggregate stability to the changes in population diversity (i.e. covariability among population time series). We illustrate the use of this metric in retrospective analyses of the aggregate Sacramento River Fall‐run Chinook salmon stock (Oncorhynchus tshawytscha), and project potential future improvements in stability through population diversity. In the retrospective analysis, we removed individual time series from the stability calculations to determine their effects on times and locations of past losses in diversity and stability. We found an early threefold loss in stock stability resulting from the presence of a single tributary, the Sacramento River mainstem. Other shifts in stability resulted from an increase in variability of a single population, and from the synchronizing effects of low ocean survival that led to the 2008–2009 fishery closure. Only one, smaller increase in the DD (i.e. in lost stability) was due to portfolio‐wide increases in covariabilities among tributary abundances. In a prospective analysis using the DD applied to California salmon, we found that increasing biodiversity to the point of population independence and to its early high value would have reduced the probability of triggering a fishery closure. Synthesis and applications. Analyses with the Diversity Deficit (DD) metric illustrate a way to identify the times and locations of losses in population diversity, and to quantify how much restoration of population diversity could increase stability, and thus benefit resource services. In our research, the benefit was a reduction in the probability of falling below a critical management threshold leading to fishery closure, but other tangible benefits (e.g. reduction in probability of extinction) would also be possible.

Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale.

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals.

Survey precision moderates the relationship between population size and stability

Positive relationship between size of local populations and their stability is a central concept in conservation biology, utilised in its tools including metapopulation models and population viability analysis, although most empirical studies found this relationship rather weak. In the present study I evaluated the impact of the precision of population estimates on the strength of the relationship between population sizes, or patch areas used as their proxy, and the extent of their temporal variability. Based on the data provided by 13-year monitoring of three species of Maculinea (= Phengaris) butterflies, I quantified the variability of their population time-series with the coefficients of variation (CVs), using both their overall temporal variances as well as the variances corrected for sampling variance, which reflects the precision (sampling error) of seasonal population size estimates. The extent of population variability, expressed with the CVs uncorrected for sampling variance, correlated negatively with both population size and habitat patch area, but these relationships were relatively weak, especially in M. teleius and M. nausithous (R2 ~0.1). However, sampling error increased considerably with population size and patch area, due to smaller fractions of individuals being sampled. Consequently, in the case of the CVs based on the variances corrected for sampling variance, the negative correlations with population size and patch area were substantially stronger (R2 reaching 0.3–0.6). My results highlight the advantage of monitoring methods that allow quantifying sampling error, which is essential for assessing species extinction risk.

Climate change, climatic variation and extreme biological responses.

Extreme climatic events could be major drivers of biodiversity change, but it is unclear whether extreme biological changes are (i) individualistic (species- or group-specific), (ii) commonly associated with unusual climatic events and/or (iii) important determinants of long-term population trends. Using population time series for 238 widespread species (207 Lepidoptera and 31 birds) in England since 1968, we found that population ‘crashes’ (outliers in terms of species' year-to-year population changes) were 46% more frequent than population ‘explosions’. (i) Every year, at least three species experienced extreme changes in population size, and in 41 of the 44 years considered, some species experienced population crashes while others simultaneously experienced population explosions. This suggests that, even within the same broad taxonomic groups, species are exhibiting individualistic dynamics, most probably driven by their responses to different, short-term events associated with climatic variability. (ii) Six out of 44 years showed a significant excess of species experiencing extreme population changes (5 years for Lepidoptera, 1 for birds). These ‘consensus years’ were associated with climatically extreme years, consistent with a link between extreme population responses and climatic variability, although not all climatically extreme years generated excess numbers of extreme population responses. (iii) Links between extreme population changes and long-term population trends were absent in Lepidoptera and modest (but significant) in birds. We conclude that extreme biological responses are individualistic, in the sense that the extreme population changes of most species are taking place in different years, and that long-term trends of widespread species have not, to date, been dominated by these extreme changes.This article is part of the themed issue ‘Behavioural, ecological and evolutionary responses to extreme climatic events’.

Savanna elephant numbers are only a quarter of their expected values

Savannas once constituted the range of many species that human encroachment has now reduced to a fraction of their former distribution. Many survive only in protected areas. Poaching reduces the savanna elephant, even where protected, likely to the detriment of savanna ecosystems. While resources go into estimating elephant populations, an ecological benchmark by which to assess counts is lacking. Knowing how many elephants there are and how many poachers kill is important, but on their own, such data lack context. We collated savanna elephant count data from 73 protected areas across the continent estimated to hold ~50% of Africa’s elephants and extracted densities from 18 broadly stable population time series. We modeled these densities using primary productivity, water availability, and an index of poaching as predictors. We then used the model to predict stable densities given current conditions and poaching for all 73 populations. Next, to generate ecological benchmarks, we predicted such densities for a scenario of zero poaching. Where historical data are available, they corroborate or exceed benchmarks. According to recent counts, collectively, the 73 savanna elephant populations are at 75% of the size predicted based on current conditions and poaching levels. However, populations are at <25% of ecological benchmarks given a scenario of zero poaching (~967,000)—a total deficit of ~730,000 elephants. Populations in 30% of the 73 protected areas were <5% of their benchmarks, and the median current density as a percentage of ecological benchmark across protected areas was just 13%. The ecological context provided by these benchmark values, in conjunction with ongoing census projects, allow efficient targeting of conservation efforts.

Cross-realm assessment of climate change impacts on species' abundance trends.

Climate change, land-use change, pollution and exploitation are among the main drivers of species' population trends; however, their relative importance is much debated. We used a unique collection of over 1,000 local population time series in 22 communities across terrestrial, freshwater and marine realms within central Europe to compare the impacts of long-term temperature change and other environmental drivers from 1980 onwards. To disentangle different drivers, we related species' population trends to species- and driver-specific attributes, such as temperature and habitat preference or pollution tolerance. We found a consistent impact of temperature change on the local abundances of terrestrial species. Populations of warm-dwelling species increased more than those of cold-dwelling species. In contrast, impacts of temperature change on aquatic species' abundances were variable. Effects of temperature preference were more consistent in terrestrial communities than effects of habitat preference, suggesting that the impacts of temperature change have become widespread for recent changes in abundance within many terrestrial communities of central Europe.

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.

Is global dimming and brightening in Japan limited to urban areas?

Abstract. Worldwide observations indicate secular trends of all-sky surface solar radiation on a decadal time scale, termed global dimming and brightening. Accordingly, the observed surface radiation in Japan generally shows a strong decline until the end of the 1980s and then a recovery until around 2000. Because a substantial number of measurement stations are located within or close to populated areas, one may speculate that the observed trends are strongly influenced by local air pollution and are thus not of large-scale significance. This hypothesis poses a serious question as to what regional extent the global dimming and brightening are significant: are the global dimming and brightening truly global phenomena, or regional, or even only local? Our study focused on 14 meteorological observatories that measured all-sky surface solar radiation, zenith transmittance, and maximum transmittance. On the basis of municipality population time series, historical land use maps, recent satellite images, and actual site visits, we concluded that eight stations have been significantly influenced by urbanization, with the remaining six stations being left pristine. Between the urban and rural areas, no marked differences were identified in the temporal trends of the aforementioned meteorological parameters. Our findings suggest that global dimming and brightening in Japan occurred on a large scale, independently of urbanization.

Using multivariate cross correlations, Granger causality and graphical models to quantify spatiotemporal synchronization and causality between pest populations.

BackgroundThis work combines multivariate time series analysis and graph theory to detect synchronization and causality among certain ecological variables and to represent significant correlations via network projections. Four different statistical tools (cross-correlations, partial cross-correlations, Granger causality and partial Granger causality) utilized to quantify correlation strength and causality among biological entities. These indices correspond to different ways to estimate the relationships between different variables and to construct ecological networks using the variables as nodes and the indices as edges. Specifically, correlations and Granger causality indices introduce rules that define the associations (links) between the ecological variables (nodes). This approach is used for the first time to analyze time series of moth populations as well as temperature and relative humidity in order to detect spatiotemporal synchronization over an agricultural study area and to illustrate significant correlations and causality interactions via graphical models.ResultsThe networks resulting from the different approaches are trimmed and show how the network configurations are affected by each construction technique. The Granger statistical rules provide a simple test to determine whether one series (population) is caused by another series (i.e. environmental variable or other population) even when they are not correlated. In most cases, the statistical analysis and the related graphical models, revealed intra-specific links, a fact that may be linked to similarities in pest population life cycles and synchronizations. Graph theoretic landscape projections reveal that significant associations in the populations are not subject to landscape characteristics. Populations may be linked over great distances through physical features such as rivers and not only at adjacent locations in which significant interactions are more likely to appear. In some cases, incidental connections, with no ecological explanation, were also observed; however, this was expected because some of the statistical methods used to define non trivial associations show connections that cannot be interpreted phenomenologically.ConclusionsIncorporating multivariate causal interactions in a probabilistic sense comes closer to reality than doing per se binary theoretic constructs because the former conceptually incorporate the dynamics of all kinds of ecological variables within the network. The advantage of Granger rules over correlations is that Granger rules have dynamic features and provide an easy way to examine the dynamic causal relations of multiple time-series variables. The constructed networks may provide an intuitive, advantageous representation of multiple populations’ associations that can be realized within an agro-ecosystem. These relationships may be due to life cycle synchronizations, exposure to a shared climate or even more complicated ecological interactions such as moving behavior, dispersal patterns and host allocation. Moreover, they are useful for drawing inferences regarding pest population dynamics and their spatial management. Extending these models by including more variables should allow the exploration of intra and interspecies relationships in larger ecological systems, and the identification of specific population traits that might constrain their structures in larger areas.Electronic supplementary materialThe online version of this article (doi:10.1186/s12898-016-0087-7) contains supplementary material, which is available to authorized users.

Density‐dependent population regulation detected in short time series of saproxylic beetles

AbstractUnderstanding the regulation of natural populations has been a long‐standing problem in ecology. Here we analyze the population dynamics of 17 species of saproxylic beetles in Shizuoka Prefecture, Japan collected over 11–12 years using autoregressive integrated moving average (ARIMA) models. We first examined the dynamics for indications of the order of the ARIMA models and evaluated the time series to determine that it was not simply a random, white noise sequence. All species dynamics were not mere random noise, and ARIMA models up to lag 3 were considered. The best model was selected from the possible ones using several criteria: model convergence, weak residual autocorrelation, the small sample AIC must be among the smallest that were not significantly different, and the lag indicated by the cutoff values in the detrended partial autocorrelation function. We found significant and nearly significant direct density‐dependence for 14 of the 17 species, varying from −0.709 and stronger. The characteristic return rates were strong and only one species had a weak return rate (&gt;0.9), implying that these species were strongly regulated by density‐dependent factors. We found that populations with higher order ARIMA models (lag 2 and 3) had weaker return rates than populations with ARIMA models with only one lag, suggesting that species with more complex dynamics were more weakly regulated. These results contrast with previous suggestions that 20+ years are needed to detect density dependence from population time series and that most populations are weakly regulated.

Tuning the Voices of a Choir: Detecting Ecological Gradients in Time-Series Populations.

This paper introduces a new approach–the Principal Component Gradient Analysis (PCGA)–to detect ecological gradients in time-series populations, i.e. several time-series originating from different individuals of a population. Detection of ecological gradients is of particular importance when dealing with time-series from heterogeneous populations which express differing trends. PCGA makes use of polar coordinates of loadings from the first two axes obtained by principal component analysis (PCA) to define groups of similar trends. Based on the mean inter-series correlation (rbar) the gain of increasing a common underlying signal by PCGA groups is quantified using Monte Carlo Simulations. In terms of validation PCGA is compared to three other existing approaches. Focusing on dendrochronological examples, PCGA is shown to correctly determine population gradients and in particular cases to be advantageous over other considered methods. Furthermore, PCGA groups in each example allowed for enhancing the strength of a common underlying signal and comparably well as hierarchical cluster analysis. Our results indicate that PCGA potentially allows for a better understanding of mechanisms causing time-series population gradients as well as objectively enhancing the performance of climate transfer functions in dendroclimatology. While our examples highlight the relevance of PCGA to the field of dendrochronology, we believe that also other disciplines working with data of comparable structure may benefit from PCGA.

Competition promotes the persistence of populations in ecosystems.

Competition is one of the most common form in ecological systems, which plays important roles in population dynamics. However, the influences of competition on persistence of populations remain unclear when space effect is included. In this paper, we investigated a predator-prey model with competition and spatial diffusion. Based on pattern formations and time series of populations, we found that competitions induce the persistence of populations, which denies competitive exclusion principle. Moreover, we testify the robustness of these effects. Our results also suggest that space may lead to the emergence of new phenomenon in ecosystems.

Dynamic building stock modelling: Application to 11 European countries to support the energy efficiency and retrofit ambitions of the EU

A dynamic building stock model is applied to simulate the development of dwelling stocks in 11 European countries, over half of all European dwellings, between 1900 and 2050. The model uses time series of population and number of persons per dwelling, as well as demolition and renovation probability functions that have been derived for each country. The model performs well at simulating the long-term changes in dwelling stock composition and expected annual renovation activities. Despite differences in data collection and reporting, the modelled future trends for construction, demolition and renovation activities lead to similar patterns emerging in all countries. The model estimates future renovation activity due to the stock’s need for maintenance as a result of ageing. The simulations show only minor future increases in the renovation rates across all 11 countries to between 0.6–1.6%, falling short of the 2.5–3.0% renovation rates that are assumed in many decarbonisation scenarios. Despite this, 78% of all dwellings could benefit from energy efficiency measures by 2050, either as they are constructed (31%) or undergo deep renovation (47%). However, as no more than one deep renovation cycle is likely on this timeframe, it is crucial to install the most energy efficient measures available at these opportunities.

A Bootstrap Likelihood Approach to Bayesian Computation

SummaryThere is an increasing amount of literature focused on Bayesian computational methods to address problems with intractable likelihood. One approach is a set of algorithms known as Approximate Bayesian Computational (ABC) methods. One of the problems with these algorithms is that their performance depends on the appropriate choice of summary statistics, distance measure and tolerance level. To circumvent this problem, an alternative method based on the empirical likelihood has been introduced. This method can be easily implemented when a set of constraints, related to the moments of the distribution, is specified. However, the choice of the constraints is sometimes challenging. To overcome this difficulty, we propose an alternative method based on a bootstrap likelihood approach. The method is easy to implement and in some cases is actually faster than the other approaches considered. We illustrate the performance of our algorithm with examples from population genetics, time series and stochastic differential equations. We also test the method on a real dataset.

A Biomathematical Model of Pneumococcal Lung Infection and Antibiotic Treatment in Mice

Pneumonia is considered to be one of the leading causes of death worldwide. The outcome depends on both, proper antibiotic treatment and the effectivity of the immune response of the host. However, due to the complexity of the immunologic cascade initiated during infection, the latter cannot be predicted easily. We construct a biomathematical model of the murine immune response during infection with pneumococcus aiming at predicting the outcome of antibiotic treatment. The model consists of a number of non-linear ordinary differential equations describing dynamics of pneumococcal population, the inflammatory cytokine IL-6, neutrophils and macrophages fighting the infection and destruction of alveolar tissue due to pneumococcus. Equations were derived by translating known biological mechanisms and assuming certain response kinetics. Antibiotic therapy is modelled by a transient depletion of bacteria. Unknown model parameters were determined by fitting the predictions of the model to data sets derived from mice experiments of pneumococcal lung infection with and without antibiotic treatment. Time series of pneumococcal population, debris, neutrophils, activated epithelial cells, macrophages, monocytes and IL-6 serum concentrations were available for this purpose. The antibiotics Ampicillin and Moxifloxacin were considered. Parameter fittings resulted in a good agreement of model and data for all experimental scenarios. Identifiability of parameters is also estimated. The model can be used to predict the performance of alternative schedules of antibiotic treatment. We conclude that we established a biomathematical model of pneumococcal lung infection in mice allowing predictions regarding the outcome of different schedules of antibiotic treatment. We aim at translating the model to the human situation in the near future.

Regional-Scale Declines in Productivity of Pink and Chum Salmon Stocks in Western North America.

Sockeye salmon (Oncorhynchus nerka) stocks throughout the southern part of their North American range have experienced declines in productivity over the past two decades. In this study, we tested the hypothesis that pink (O. gorbuscha) and chum (O. keta) salmon stocks have also experienced recent declines in productivity by investigating temporal and spatial trends in productivity of 99 wild North American pink and chum salmon stocks. We used a combination of population dynamics and time series models to quantify individual stock trends as well as common temporal trends in pink and chum salmon productivity across local, regional, and continental spatial scales. Our results indicated widespread declines in productivity of wild chum salmon stocks throughout Washington (WA) and British Columbia (BC) with 81% of stocks showing recent declines in productivity, although the exact form of the trends varied among regions. For pink salmon, the majority of stocks in WA and BC (65%) did not have strong temporal trends in productivity; however, all stocks that did have trends in productivity showed declining productivity since at least brood year 1996. We found weaker evidence of widespread declines in productivity for Alaska pink and chum salmon, with some regions and stocks showing declines in productivity (e.g., Kodiak chum salmon stocks) and others showing increases (e.g., Alaska Peninsula pink salmon stocks). We also found strong positive covariation between stock productivity series at the regional spatial scale for both pink and chum salmon, along with evidence that this regional-scale positive covariation has become stronger since the early 1990s in WA and BC. In general, our results suggest that common processes operating at the regional or multi-regional spatial scales drive productivity of pink and chum salmon stocks in western North America and that the effects of these process on productivity may change over time.

Finding regulation among seemingly unregulated populations: a practical framework for analyzing multivariate population time series for their interactions

The structure of ecological communities is often thought to be strongly influenced by population interactions. The interactions are often labeled as bottom-up and top-down control. Previous approaches to identify these processes often assume each population in the community is itself regulated. Therefore, each time series follows a stationary process. However, complex community structure and a lack of regulation in an individual population can result in inappropriate inferences based on traditional statistical approaches. Here, we introduce a statistical framework to analyze potentially non-stationary time series that are collectively regulated. We demonstrate the method with catch-per-unit-effort time series data of selected populations in the Gulf of Mexico. In the Gulf, we found that most of the time series data, which span 26 years, were non-stationary, thus individually unregulated. Species interaction patterns were location-dependent, but where brown shrimp interacted significantly with other species, we identified significant bottom-up forcing. On the other hand, we find almost no evidence of top-down forcing throughout the study areas.

A novel hybrid model based on artificial neural networks for solar radiation prediction

As a kind of clean, substantial and renewable energy, solar energy can reduce environmental pollution with an extensive application potential. Precise prediction of global solar radiation has great significance for the design of solar energy systems and management of solar power plants.In this paper, a new hybrid model combining the SOM-OPELM with time series strategies is presented for predicting the global solar radiation on the horizon. In this model, the SOM divides the original data into distinct clusters and the OPELM establishes the prediction model. Subsequently, three population time series strategies, (i.e. Recursive strategy, DirRec strategy and MISMO strategy) are adopted to accomplish the multi-step prediction. A comparison between the proposed SOM-OPELM model and other conventional methods is carried out to demonstrate its efficiency and estimation performance. The simulation results show that the proposed SOM-OPELM model with DirRec strategy or MISMO strategy outperforms the following models: Recursive-BP, DirRec-BP, MISMO-BP, Recursive-SOM-OPELM and ARIMA.

Assessing the spatial variability of density dependence in waterfowl populations

Population models commonly assume that the demographic parameters are spatially invariant, but there is considerable evidence that population growth rate (r) and the strength of density dependence (β) can vary over a species' range. To address this issue we developed a spatially explicit Gompertz population model based on the spatially varying coefficients approach to assess the spatial variation in population drivers. The model was fit to spatially stratified time series population estimates of the mallardAnas platyrhynchosin western North America. We included precipitation during the previous year and spring maximum temperature in the current year as environmental factors in the density dependent population model. Because density dependent models can give biased estimates for time series of abundance data, we fit a naïve model without informative priors and a model where we constrained the mean and variance ofrto biologically realistic values that were derived via a comparative demography approach. In the naïve model,rand β were not separately identifiable and their values were overestimated, leading to unrealistic population growth. The naïve model also implied spatial variation in populationrand the return time to equilibrium [1⁄(– β)] across the survey area. In contrast, in the informative model,rand the return time to equilibrium did not vary markedly among populations and were generally equal across populations. The effects of the climatic factors were similar across models. Population growth rates in the Prairie‐pothole region were positively correlated with precipitation, while in Alaska rates were positively correlated with spring temperature. Although it has been argued in the past that adding ecological realism could help avoid the pitfalls associated with density dependent models, our results demonstrate that imposing constraints on the population parameters is still the best course of action.

A modelling framework for pest population dynamics and management: An application to the grape berry moth

Physiologically based demographic models are important tools for the development of sustainable pest management as they can realistically describe the spatio-temporal dynamics of population abundance as function of environmental forcing variables, e.g. temperature and resource availability. The physiological based model presented here is based on a stochastic demographic model for a stage-structured population that has application to a wide range of species across different taxa. The species life-history strategies are described in terms of a set of biodemographic rate functions dependent from the biological characteristics of the species and their environmental driver variables. Model application required parameter estimation of the biodemographic rate functions at two levels: assessing physiological responses at the per capita level and/or using population time series data for rate functions estimation. To explore the usefulness of the modelling framework in pest management, we consider the case study of the grape berry moth Lobesia botrana, a major pest in European vineyard. Most of the model parameters were estimated from data in the literature. An unpublished dataset of population dynamics collected in a vineyard in the Veneto region (Italy) over three years was used to estimate the mortality function. Model validation was performed with a set of independent data.Model simulations provided realistic trajectories of population dynamics obtained with a limited dataset of initial conditions. The suitability of the model as a tool for decision support for grape berry moth management is discussed.