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"ecological forecasting"
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A natural history of the future : what the laws of biology tell us about the destiny of the human species
Over the past century, our species has made unprecedented technological innovations with which we have sought to control nature. From river levees to enormous one-crop fields, we continue to try to reshape nature for our purposes - so much so it seems we may be in danger of destroying it. In 'A Natural History of the Future', biologist Rob Dunn argues that nothing could be further from the truth: rather than asking whether nature will survive us, better to ask whether we will survive nature.
Book
Ecological forecasting and data assimilation in a data-rich era
Several forces are converging to transform ecological research and increase its emphasis on quantitative forecasting. These forces include (1) dramatically increased volumes of data from observational and experimental networks, (2) increases in computational power, (3) advances in ecological models and related statistical and optimization methodologies, and most importantly, (4) societal needs to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-oriented models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today's models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting and estimates of uncertainty is data assimilation (DA), which uses data to inform initial conditions and model parameters, thereby constraining a model during simulation to yield results that approximate reality as closely as possible.
This paper discusses the meaning and history of DA in ecological research and highlights its role in refining inference and generating forecasts. DA can advance ecological forecasting by (1) improving estimates of model parameters and state variables, (2) facilitating selection of alternative model structures, and (3) quantifying uncertainties arising from observations, models, and their interactions. However, DA may not improve forecasts when ecological processes are not well understood or never observed. Overall, we suggest that DA is a key technique for converting raw data into ecologically meaningful products, which is especially important in this era of dramatically increased availability of data from observational and experimental networks.
Journal Article
Lessons from the first generation of marine ecological forecast products
Recent years have seen a rapid expansion in the ability of earth system models to describe and predict the physical state of the ocean. Skilful forecasts ranging from seasonal (3 months) to decadal (5-10 years) time scales are now a reality. With the advance of these forecasts of ocean physics, the first generation of marine ecological forecasts has started to emerge. Such forecasts are potentially of great value in the management of living marine resources and for all of those who are dependent on the ocean for both nutrition and their livelihood; however, this is still a field in its infancy. We review the state of the art in this emerging field and identify the lessons that can be learnt and carried forward from these pioneering efforts. The majority of this first wave of products are forecasts of spatial distributions, possibly reflecting the inherent suitability of this response variable to the task of forecasting. Promising developments are also seen in forecasting fish-stock recruitment where, despite well-recognised challenges in understanding and predicting this response, new process knowledge and model approaches that could form a basis for forecasting are becoming available. Forecasts of phenology and coral-bleaching events are also being applied to monitoring and industry decisions. Moving marine ecological forecasting forward will require striking a balance between what is feasible and what is useful. We propose here a set of criteria to quickly identify “low-hanging fruit” that can potentially be predicted; however, ensuring the usefulness of forecast products also requires close collaboration with actively engaged end-users. Realising the full potential of marine ecological forecasting will require bridging the gaps between marine ecology and climatology on the one-hand, and between science and end-users on the other. Nevertheless, the successes seen thus far and the potential to develop further products suggest that the field of marine ecological forecasting can be expected to flourish in the coming years.
Journal Article
Handbook of Uncertainty in Eurasian Forecasting (HEF)
Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. Eurasia comprises about 36% of the world's total area and about 70% of the world population. Eurasia comprises Asia and Europe, although, geographically, it is a single continent with arbitrary geological borders. Eurasia has been a home to the world's oldest civilizations and plays an important part in the mainstream history of the world. Eurasian countries have many common characteristics and forecasting of this region can prove to be of major help in integrated resources management, leading to sustainable development, optimum decision making of international world organizations and achieving goals of world peace. This book deals with the various aspects of social and environmental importance in this region, especially climate change and hydrological modelling and flood forecasting.
eBook
Relative information contributions of model vs. data to short- and long-term forecasts of forest carbon dynamics
Biogeochemical models have been used to evaluate long-term ecosystem responses to global change on decadal and century time scales. Recently, data assimilation has been applied to improve these models for ecological forecasting. It is not clear what the relative information contributions of model (structure and parameters) vs. data are to constraints of short- and long-term forecasting. In this study, we assimilated eight sets of 10-year data (foliage, woody, and fine root biomass, litter fall, forest floor carbon [[C]], microbial C, soil C, and soil respiration) collected from Duke Forest into a Terrestrial Ecosystem model (TECO). The relative information contribution was measured by Shannon information index calculated from probability density functions (PDFs) of carbon pool sizes. The null knowledge without a model or data was defined by the uniform PDF within a prior range. The relative model contribution was information content in the PDF of modeled carbon pools minus that in the uniform PDF, while the relative data contribution was the information content in the PDF of modeled carbon pools after data was assimilated minus that before data assimilation. Our results showed that the information contribution of the model to constrain carbon dynamics increased with time whereas the data contribution declined. The eight data sets contributed more than the model to constrain C dynamics in foliage and fine root pools over the 100-year forecasts. The model, however, contributed more than the data sets to constrain the litter, fast soil organic matter (SOM), and passive SOM pools. For the two major C pools, woody biomass and slow SOM, the model contributed less information in the first few decades and then more in the following decades than the data. Knowledge of relative information contributions of model vs. data is useful for model development, uncertainty analysis, future data collection, and evaluation of ecological forecasting.
Journal Article
The potential future change of the suitability patterns of six leishmaniasis vectors in Iran
Background & objectives: Visceral and cutaneous leishmaniasis are endemic in Iran. The aim of this study was to model the changing suitability patterns of five confirmed and one suspected leishmaniasis vector Phlebotomus species resident in the country.
Methods: The potential present and future suitability patterns of the sandfly species in Iran were modelled using climate envelope forecasting method for the reference period 1970-2000 and the future period 2041-2060. Results: The reference period climate of Iran seemed to be the most suitable for Phlebotomus perfiliewi and Phlebotomus tobbi and less suitable for Phlebotomus simili, while Phlebotomus neglectus, Phlebotomus papatasi and Phlebotomus sergenti showed intermediate values among the studied sandfly species. The modelled changes in the suitability values show a similar pattern in the case of the six species, even the exact magnitude of the modelled values varied. The model results indicate that climate change could decrease the sandfly habitability in the present-day arid regions in Central Iran. The Iranian sandfly populations will move to higher elevation regions, and the suitability values of the sandfly species are predicted to increase in the foothills of the mountainous regions in the northern and the western part of the country.
Interpretation & conclusion: The increase of the maximally suitable areas in Iran was found which was predicted to be accompanied by the parallel shrinkage of the sandfly-inhabited areas in the arid regions of the country. Topographical conditions could strongly influence the suitability patterns of the vectors in Iran.
Journal Article
A roadmap towards predicting species interaction networks (across space and time)
Networks of species interactions underpin numerous ecosystem processes, but comprehensively sampling these interactions is difficult. Interactions intrinsically vary across space and time, and given the number of species that compose ecological communities, it can be tough to distinguish between a true negative (where two species never interact) from a false negative (where two species have not been observed interacting even though they actually do). Assessing the likelihood of interactions between species is an imperative for several fields of ecology. This means that to predict interactions between species—and to describe the structure, variation, and change of the ecological networks they form—we need to rely on modelling tools. Here, we provide a proof-of-concept, where we show how a simple neural network model makes accurate predictions about species interactions given limited data. We then assess the challenges and opportunities associated with improving interaction predictions, and provide a conceptual roadmap forward towards predictive models of ecological networks that is explicitly spatial and temporal. We conclude with a brief primer on the relevant methods and tools needed to start building these models, which we hope will guide this research programme forward.
This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.
Journal Article
Prediction in ecology: a first-principles framework
Quantitative predictions are ubiquitous in ecology, yet there is limited discussion on the nature of prediction in this field. Herein I derive a general quantitative framework for analyzing and partitioning the sources of uncertainty that control predictability. The implications of this framework are assessed conceptually and linked to classic questions in ecology, such as the relative importance of endogenous (density-dependent) vs. exogenous factors, stability vs. drift, and the spatial scaling of processes. The framework is used to make a number of novel predictions and reframe approaches to experimental design, model selection, and hypothesis testing. Next, the quantitative application of the framework to partitioning uncertainties is illustrated using a short-term forecast of net ecosystem exchange. Finally, I advocate for a new comparative approach to studying predictability across different ecological systems and processes and lay out a number of hypotheses about what limits predictability and how these limits should scale in space and time.
Journal Article
Dynamic generalised additive models (DGAMs) for forecasting discrete ecological time series
Generalised additive models (GAMs) are increasingly popular tools for estimating smooth nonlinear relationships between predictors and response variables. GAMs are particularly relevant in ecology for representing hierarchical functions for discrete responses that encompass complex features including zero inflation, truncation and uneven sampling. However, GAMs are less useful for producing forecasts as their smooth functions provide unstable predictions outside the range of training data. We introduce dynamic generalised additive models (DGAMs), where the GAM linear predictor is jointly estimated with unobserved dynamic components to model time series that evolve as a function of nonlinear predictor associations and latent temporal processes. These models are especially useful for analysing multiple series, as they can estimate hierarchical smooth functions while learning complex temporal associations via dimension‐reduced latent factor processes. We implement our models in the mvgam R package, which estimates unobserved parameters for smoothing splines and latent temporal processes in a probabilistic framework. Using simulations, we illustrate how our models outperform competing formulations in realistic ecological forecasting tasks while identifying important smooth predictor functions. We use a real‐world case study to highlight some of mvgam's key features, which include functions for calculating correlations among series' latent trends, performing model selection using rolling window forecasts and posterior predictive checks, online data augmentation via a recursive particle filter and visualising probabilistic uncertainties for smooth functions and predictions. Dynamic GAMs (DGAMs) offer a solution to the challenge of forecasting discrete time series while estimating ecologically relevant nonlinear predictor associations. Our Bayesian latent factor approach will be particularly useful for exploring competing dynamic ecological models that encompass hierarchical smoothing structures while providing forecasts with robust uncertainties, tasks that are becoming increasingly important in applied ecology.
Journal Article
Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability
As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1–14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend that increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.
Journal Article