Karin Johst

Improving the forecast for biodiversity under climate change

BACKGROUND As global climate change accelerates, one of the most urgent tasks for the coming decades is to develop accurate predictions about biological responses to guide the effective protection of biodiversity. Predictive models in biology provide a means for scientists to project changes to species and ecosystems in response to disturbances such as climate change. Most current predictive models, however, exclude important biological mechanisms such as demography, dispersal, evolution, and species interactions. These biological mechanisms have been shown to be important in mediating past and present responses to climate change. Thus, current modeling efforts do not provide sufficiently accurate predictions. Despite the many complexities involved, biologists are rapidly developing tools that include the key biological processes needed to improve predictive accuracy. The biggest obstacle to applying these more realistic models is that the data needed to inform them are almost always missing. We suggest ways to fill this growing gap between model sophistication and information to predict and prevent the most damaging aspects of climate change for life on Earth. ADVANCES On the basis of empirical and theoretical evidence, we identify six biological mechanisms that commonly shape responses to climate change yet are too often missing from current predictive models: physiology; demography, life history, and phenology; species interactions; evolutionary potential and population differentiation; dispersal, colonization, and range dynamics; and responses to environmental variation. We prioritize the types of information needed to inform each of these mechanisms and suggest proxies for data that are missing or difficult to collect. We show that even for well-studied species, we often lack critical information that would be necessary to apply more realistic, mechanistic models. Consequently, data limitations likely override the potential gains in accuracy of more realistic models. Given the enormous challenge of collecting this detailed information on millions of species around the world, we highlight practical methods that promote the greatest gains in predictive accuracy. Trait-based approaches leverage sparse data to make more general inferences about unstudied species. Targeting species with high climate sensitivity and disproportionate ecological impact can yield important insights about future ecosystem change. Adaptive modeling schemes provide a means to target the most important data while simultaneously improving predictive accuracy. OUTLOOK Strategic collections of essential biological information will allow us to build generalizable insights that inform our broader ability to anticipate species’ responses to climate change and other human-caused disturbances. By increasing accuracy and making uncertainties explicit, scientists can deliver improved projections for biodiversity under climate change together with characterizations of uncertainty to support more informed decisions by policymakers and land managers. Toward this end, a globally coordinated effort to fill data gaps in advance of the growing climate-fueled biodiversity crisis offers substantial advantages in efficiency, coverage, and accuracy. Biologists can take advantage of the lessons learned from the Intergovernmental Panel on Climate Change’s development, coordination, and integration of climate change projections. Climate and weather projections were greatly improved by incorporating important mechanisms and testing predictions against global weather station data. Biology can do the same. We need to adopt this meteorological approach to predicting biological responses to climate change to enhance our ability to mitigate future changes to global biodiversity and the services it provides to humans. Emerging models are beginning to incorporate six key biological mechanisms that can improve predictions of biological responses to climate change. Models that include biological mechanisms have been used to project (clockwise from top) the evolution of disease-harboring mosquitoes, future environments and land use, physiological responses of invasive species such as cane toads, demographic responses of penguins to future climates, climate-dependent dispersal behavior in butterflies, and mismatched interactions between butterflies and their host plants. Despite these modeling advances, we seldom have the detailed data needed to build these models, necessitating new efforts to collect the relevant data to parameterize more biologically realistic predictive models. New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species’ responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity.

FORAGING IN A PATCHY AND DYNAMIC LANDSCAPE: HUMAN LAND USE AND THE WHITE STORK

In the agricultural landscapes of Europe, the White Stork (Ciconia ciconia) prefers to forage on meadows with short vegetation. Thus, food supply for the nestlings and, consequently, breeding success of this central-place forager depend on the temporal and spatial mowing activities of farmers around the nest to generate a patchy and dynamic food availability. Using a spatially explicit model, we study the impact of different land use patterns on food supply and breeding success of a central-place forager. The conclusions of our model are twofold. First, for the White Stork, our model suggests that sequential (asynchronous) mowing increases breeding success compared to the synchronous mowing activities presently applied by farmers. Second and more generally, we conclude that, with increasing heterogeneity and dynamics of the landscape, the patch selection strategy be- comes increasingly important for predicting food supply. Thus, landscape-oriented behavior is an important, but often neglected, component of conservation biology and management, especially in agricultural landscapes.

An ecological-economic modelling procedure to design compensation payments for the efficient spatio-temporal allocation of species protection measures

Abstract Using an interdisciplinary approach, we present an ecological-economic modelling procedure to design compensation payments for species protection. We develop our procedure on the example of White Stork protection in a spatio-temporally structured landscape generated by human land use. The proposed procedure is able to solve complex allocation problems such as the spatio-temporal allocation of a budget among more than two areas with spatially differing species-specific cost and benefit functions of any shapes. Furthermore, the procedure delivers the efficient compensation payments not only qualitatively but quantitatively, and is hence relevant to the implementation of species protection policies.

Does red noise increase or decrease extinction risk? Single extreme events versus series of unfavorable conditions.

Recent theoretical studies have shown contrasting effects of temporal correlation of environmental fluctuations (red noise) on the risk of population extinction. It is still debated whether and under which conditions red noise increases or decreases extinction risk compared with uncorrelated (white) noise. Here, we explain the opposing effects by introducing two features of red noise time series. On the one hand, positive autocorrelation increases the probability of series of poor environmental conditions, implying increasing extinction risk. On the other hand, for a given time period, the probability of at least one extremely bad year (“catastrophe”) is reduced compared with white noise, implying decreasing extinction risk. Which of these two features determines extinction risk depends on the strength of environmental fluctuations and the sensitivity of population dynamics to these fluctuations. If extreme (catastrophic) events can occur (strong noise) or sensitivity is high (overcompensatory density dependence), then temporal correlation decreases extinction risk; otherwise, it increases it. Thus, our results provide a simple explanation for the contrasting previous findings and are a crucial step toward a general understanding of the effect of noise color on extinction risk.

Evolution of complex dynamics in spatially structured populations

Dynamics of populations depend on demographic parameters which may change during evolution. In simple ecological models given by one–dimensional difference equations, the evolution of demographic parameters generally leads to equilibrium population dynamics. Here we show that this is not true in spatially structured ecological models. Using a multi–patch metapopulation model, we study the evolutionary dynamics of phenotypes that differ both in their response to local crowding, i.e. in their competitive behaviour within a habitat, and in their rate of dispersal between habitats. Our simulation results show that evolution can favour phenotypes that have the intrinsic potential for very complex dynamics provided that the environment is spatially structured and temporally variable. These phenotypes owe their evolutionary persistence to their large dispersal rates. They typically coexist with phenotypes that have low dispersal rates and that exhibit equilibrium dynamics when alone. This coexistence is brought about through the phenomenon of evolutionary branching, during which an initially uniform population splits into the two phenotypic classes.

The effect of dispersal on local population dynamics

Abstract Habitats in spatially structured populations are coupled by dispersal. Thus, local and global dynamics may be influenced by the dispersal strategy used. We study the consequences of various unconditional and conditional dispersal strategies to the local dynamics. We found that not only the magnitude but also the mode of dispersal may affect local dynamics significantly. Density dependent dispersal induces an additional amount of variability of the local dynamics because it interacts with the spatial and temporal variability of the environment via the local density. Therefore, the same dispersal strategy may induce quite different patterns of variability of local dynamics depending on the landscape. The structure and parameters of our model were guided by the population dynamics of black-headed gull (Larus ridibundus). Therefore, comparing patterns of our simulations with field patterns, we are able to make suggestions about the dispersal strategy used by this gull species.

Evolution of dispersal: the importance of the temporal order of reproduction and dispersal

Dispersal is a key process in the ecology and evolution of spatially–structured populations. Dispersal may occur before or after reproduction, a feature ignored in models analysing the eveolution of dispersal. Using a simulation model, we examine how the temporal order of reproduction and dispersal within the life cycle affects the competition of genotypes with different dispersal strategies. We found that the evolutionary outcome in time–discrete models depends significantly on the temporal order of dispersal and reproduction, provided that: (i) density–dependent dispersal strategies are involved into competition, and (ii) the environment is temporally variable. Our results suggest that selection can act in different directions, depending on the relative timing of dispersal, reproduction and environmental fluctuations in the life cycle.

Model-Based Estimation of Collision Risks of Predatory Birds with Wind Turbines

The expansion of renewable energies, such as wind power, is a promising way of mitigating climate change. Because of the risk of collision with rotor blades, wind turbines have negative effects on local bird populations, particularly on raptors such as the Red Kite (Milvus milvus). Appropriate assessment tools for these effects have been lacking. To close this gap, we have developed an agent-based, spatially explicit model that simulates the foraging behavior of the Red Kite around its aerie in a landscape consisting of different land-use types. We determined the collision risk of the Red Kite with the turbine as a function of the distance between the wind turbine and the aerie and other parameters. The impact function comprises the synergistic effects of species-specific foraging behavior and landscape structure. The collision risk declines exponentially with increasing distance. The strength of this decline depends on the raptors foraging behavior, its ability to avoid wind turbines, and the mean wind speed in the region. The collision risks, which are estimated by the simulation model, are in the range of values observed in the field. The derived impact function shows that the collision risk can be described as an aggregated function of distance between the wind turbine and the raptors aerie. This allows an easy and rapid assessment of the ecological impacts of (existing or planned) wind turbines in relation to their spatial location. Furthermore, it implies that minimum buffer zones for different landscapes can be determined in a defensible way. This modeling approach can be extended to other bird species with central-place foraging behavior. It provides a helpful tool for landscape planning aimed at minimizing the impacts of wind power on biodiversity.

Extinction risk in periodically fluctuating environments

Abstract Periodically fluctuating environments occur in various ways in nature but have not, however, been studied in detail yet in the context of the color of environmental noise and extinction risk of populations. We use a stochastic model to simulate population dynamics with compensatory density regulation under four different patterns of periodically fluctuating environments. We found that extinction risk changes dramatically from what was known if the underlying environmental stochasticity driving population dynamics is periodically correlated rather than randomly correlated. Fluctuating environments with a very short period are found to decrease extinction risk over “white noise” fluctuations because a species is never in a bad environment for too long. Conversely, long periods increase extinction risk because species accumulate too much time in a bad environment. Moreover, we found the mean, variance, frequency distribution and especially the extensively studied noise color not to be sufficient for predicting extinction risk in periodically fluctuating environments. Rather, additional attributes of environmental noise have to be considered. The occurrence of monotonic trends within time series of environmental data (e.g. after ‘disturbance’ events), in combination with density regulation, may also affect extinction risk. Our study exemplifies that the investigation of periodically fluctuating environments leads to new insights into the interaction between environmental variation, population dynamics and the resulting extinction risk.

A protocol for better design, application, and communication of population viability analyses

Population viability analyses (PVAs) contribute to conservation theory, policy, and management. Most PVAs focus on single species within a given landscape and address a specific problem. This specificity often is reflected in the organization of published PVA descriptions. Many lack structure, making them difficult to understand, assess, repeat, or use for drawing generalizations across PVA studies. In an assessment comparing published PVAs and existing guidelines, we found that model selection was rarely justified; important parameters remained neglected or their implementation was described vaguely; limited details were given on parameter ranges, sensitivity analysis, and scenarios; and results were often reported too inconsistently to enable repeatability and comparability. Although many guidelines exist on how to design and implement reliable PVAs and standards exist for documenting and communicating ecological models in general, there is a lack of organized guidelines for designing, applying, and communicating PVAs that account for their diversity of structures and contents. To fill this gap, we integrated published guidelines and recommendations for PVA design and application, protocols for documenting ecological models in general and individual-based models in particular, and our collective experience in developing, applying, and reviewing PVAs. We devised a comprehensive protocol for the design, application, and communication of PVAs (DAC-PVA), which has 3 primary elements. The first defines what a useful PVA is; the second element provides a workflow for the design and application of a useful PVA and highlights important aspects that need to be considered during these processes; and the third element focuses on communication of PVAs to ensure clarity, comprehensiveness, repeatability, and comparability. Thereby, DAC-PVA should strengthen the credibility and relevance of PVAs for policy and management, and improve the capacity to generalize PVA findings across studies.