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A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
Aim Biotic interactions within guilds or across trophic levels have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of species interaction distribution models (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities.
The spatial distribution of a species is determined by dynamic processes such as reproduction, mortality and dispersal. Conventional static species distribution models (SDMs) do not incorporate these processes explicitly. This limits their applicability, particularly for non-equilibrium situations such as invasions or climate change. In this paper we show how dynamic SDMs can be formulated and fitted to data within a Bayesian framework. Our focus is on discrete state-space Markov process models which provide a flexible framework to account for stochasticity in key demographic processes, including dispersal, growth and competition. We show how to construct likelihood functions for such models (both discrete and continuous time versions) and how these can be combined with suitable observation models to conduct Bayesian parameter inference using computational techniques such as Markov chain Monte Carlo. We illustrate the current state-of-the-art with three contrasting examples using both simulated and empirical data. The use of simulated data allows the robustness of the methods to be tested with respect to deficiencies in both data and model. These examples show how mechanistic understanding of the processes that determine distribution and abundance can be combined with different sources of information at a range of spatial and temporal scales. Application of such techniques will enable more reliable inference and projections, e.g. under future climate change scenarios than is possible with purely correlative approaches. Conversely, confronting such process-oriented niche models with abundance and distribution data will test current understanding and may ultimately feedback to improve underlying ecological theory.
Aim The study and prediction of speciesenvironment relationships is currently mainly based on species distribution models. These purely correlative models neglect spatial population dynamics and assume that species distributions are in equilibrium with their environment. This causes biased estimates of species niches and handicaps forecasts of range dynamics under environmental change. Here we aim to develop an approach that statistically estimates process-based models of range dynamics from data on species distributions and permits a more comprehensive quantification of forecast uncertainties.
Innovation We present an approach for the statistical estimation of process-based dynamic range models (DRMs) that integrate Hutchinson's niche concept with spatial population dynamics. In a hierarchical Bayesian framework the environmental response of demographic rates, local population dynamics and dispersal are estimated conditional upon each other while accounting for various sources of uncertainty. The method thus: (1) jointly infers species niches and spatiotemporal population dynamics from occurrence and abundance data, and (2) provides fully probabilistic forecasts of future range dynamics under environmental change. In a simulation study, we investigate the performance of DRMs for a variety of scenarios that differ in both ecological dynamics and the data used for model estimation.
Main conclusions Our results demonstrate the importance of considering dynamic aspects in the collection and analysis of biodiversity data. In combination with informative data, the presented framework has the potential to markedly improve the quantification of ecological niches, the process-based understanding of range dynamics and the forecasting of species responses to environmental change. It thereby strengthens links between biogeography, population biology and theoretical and applied ecology.
It has been proposed that growth and reproduction of animals is frequently limited by multiple nutrients simultaneously. To improve our understanding of the consequences of multiple nutrient limitations (i.e. co-limitation) for the performance of animals, we conducted standardized population growth experiments using an important aquatic consumer, the rotifer Brachionus calyciflorus. We compared nutrient profiles (sterols, fatty acids and amino acids) of rotifers and their diets to reveal consumerdiet imbalances and thus potentially limiting nutrients. In concomitant growth experiments, we directly supplemented potentially limiting substances (sterols, fatty acids, amino acids) to a nutrient-deficient diet, the cyanobacterium Synechococcus elongatus, and recorded population growth rates. The results from the supplementation experiments corroborated the nutrient limitations predicted by assessing consumerdiet imbalances, but provided more detailed information on co-limiting nutrients. While the fatty acid deficiency of the cyanobacterium appeared to be of minor importance, the addition of both cholesterol and certain amino acids (leucine and isoleucine) improved population growth rates of rotifers, indicating a simultaneous limitation by sterols and amino acids. Our results add to growing evidence that consumers frequently face multiple nutrient limitations and suggest that the concept of co-limitation has to be considered in studies assessing nutrient-limited growth responses of consumers.
The distribution of poikilotherms is determined by the thermal structure of the marine environment that they are exposed to. Recent research has indicated that changes in migration phenology of beluga whales in the Arctic are triggered by changes in the thermal structure of the marine environment in their summering area. If sea temperatures reflect the spatial distribution of food resources, then changes in the thermal regime will affect how homogeneous or clumped food is distributed. We explore, by individual-based modelling, the hypothesis that changes in migration phenology are not necessarily or exclusively triggered by changes in food abundance, but also by changes in the spatial aggregation of food. We found that the level of food aggregation can significantly affect the relationship between the timing of the start of migration to the winter grounds and the total prey capture of individuals. Our approach strongly indicates that changes in the spatial distribution of food resources should be considered for understanding and quantitatively predicting changes in the phenology of animal migration.
Aim To understand the role and significance of the reindeer, Rangifer tarandus (Linnaeus, 1758), as a specific indicator in terms of late Quaternary biogeography and to determine the effects of global climate change on its range and local extinction dynamics at the end of the Ice Age.
Location Late Pleistocene/early Holocene range of reindeer over all of central and western Europe, including southern Scandinavia and northern Iberia, but excluding Russia, Belarus and the Ukraine.
Methods Radiocarbon-dated subfossil records of R. tarandus from both archaeological and natural deposits younger than 25,000 years were assembled in a database. The distribution area was divided into six representative regions. The C-14 dates were calibrated and plotted chronologically in maps in order to compare presence and absence and regional extinction patterns from one region to another.
Main conclusions The late Quaternary record for reindeer in Europe during the last 25 kyr shows a climate-driven dispersal and retreat in response to climate change, with regional variations. The collapse of the mammoth steppe biome did not lead to the local extinction in Europe, as in the case of other megafaunal species. Rangifer tarandus co-existed for about 3000 years during the Late Glacial and early Holocene with typical temperate species such as red deer and roe deer in non-analogue faunal communities. The regional extinction at the end of the Pleistocene coincides with the transition from light open birch/pine forests to pine/deciduous forests.
Global change, especially land-use intensification, affects human well-being by impacting the delivery of multiple ecosystem services (multifunctionality). However, whether biodiversity loss is a major component of global change effects on multifunctionality in real-world ecosystems, as in experimental ones, remains unclear. Therefore, we assessed biodiversity, functional composition and 14 ecosystem services on 150 agricultural grasslands differing in land-use intensity. We also introduce five multifunctionality measures in which ecosystem services were weighted according to realistic land-use objectives. We found that indirect land-use effects, i.e. those mediated by biodiversity loss and by changes to functional composition, were as strong as direct effects on average. Their strength varied with land-use objectives and regional context. Biodiversity loss explained indirect effects in a region of intermediate productivity and was most damaging when land-use objectives favoured supporting and cultural services. In contrast, functional composition shifts, towards fast-growing plant species, strongly increased provisioning services in more inherently unproductive grasslands.
Epigenetic modifications, of which DNA methylation is the best studied one, can convey environmental information through generations via parental germ lines. Past studies have focused on the maternal transmission of epigenetic information to the offspring of isogenic mice and rats in response to external changes, whereas heterogeneous wild mammals as well as paternal epigenetic effects have been widely neglected. In most wild mammal species, males are the dispersing sex and have to cope with differing habitats and thermal changes. As temperature is a major environmental factor we investigated if genetically heterogeneous Wild guinea pig (Cavia aperea) males can adapt epigenetically to an increase in temperature and if that response will be transmitted to the next generation(s). Five adult male guinea pigs (F0) were exposed to an increased ambient temperature for 2 months, i.e. the duration of spermatogenesis. We studied the liver (as the main thermoregulatory organ) of F0 fathers and F1 sons, and testes of F1 sons for paternal transmission of epigenetic modifications across generation(s). Reduced representation bisulphite sequencing revealed shared differentially methylated regions in annotated areas between F0 livers before and after heat treatment, and their sons’ livers and testes, which indicated a general response with ecological relevance. Thus, paternal exposure to a temporally limited increased ambient temperature led to an ‘immediate’ and ‘heritable’ epigenetic response that may even be transmitted to the F2 generation. In the context of globally rising temperatures epigenetic mechanisms may become increasingly relevant for the survival of species.
Warming and eutrophication are two of the most important global change stressors for natural ecosystems, but their interaction is poorly understood. We used a dynamic model of complex, size-structured food webs to assess interactive effects on diversity and network structure. We found antagonistic impacts: Warming increases diversity in eutrophic systems and decreases it in oligotrophic systems. These effects interact with the community size structure: Communities of similarly sized species such as parasitoid-host systems are stabilized by warming and destabilized by eutrophication, whereas the diversity of size-structured predator-prey networks decreases strongly with warming, but decreases only weakly with eutrophication. Nonrandom extinction risks for generalists and specialists lead to higher connectance in networks without size structure and lower connectance in size-structured communities. Overall, our results unravel interactive impacts of warming and eutrophication and suggest that size structure may serve as an important proxy for predicting the community sensitivity to these global change stressors.