Final Report Summary - EMMA (Evolution meets macroecology - integrating rapid adaptation into predictions of future plant species' ranges)
Human-induced climate change is predicted to drastically affect the distribution of species and their risk of extinction (IPCC 2007). The potential loss of biodiversity and ecosystem function is one of the most critical issues our society is facing (Thuiller, 2007). To address this issue, the European Council decided during the 2001 summit in Goteborg to intensify research and strengthen scientific knowledge in the field of global change.
One of the major aims of global change research is the development of reliable predictions on future geographic distributions of species, communities and functional diversity to assess the impacts on ecosystem goods and services, to develop adaptive management and conservation strategies and to demonstrate the magnitude of the problem to decision makers (DIVERSITAS-International). The development of modern statistical approaches as well as the progress in high-performance computing in the last decade allows data handling and the simulation of future climatic and biodiversity scenarios on a high technological level. However, the full potential of model-based assessments on climate change impacts is far from being tapped, since we are still lacking a comprehensive framework linking the knowledge of complementary fields of global change research such as macroecology, population ecology, community ecology, evolutionary ecology, physiology and socio-economy.
The integrand and key idea of the project EMMA is the linkage between two of these fields: evolutionary biology and macroecology. Since the processes considered here act on the opposing ends of the organisational hierarchy, they have rarely been combined. However, if we are to understand under which conditions species will be able to cope with environmental change, it is important to scale up the knowledge of the genetics underlying adaptation to the level of population demography and eventually macro-evolutionary dynamics.
As a contribution to this research agenda, we developed a modelling framework that allows investigating the co-actions of ecological and evolutionary processes at the individual-level and the resulting patterns of species distributions and evolutionary trajectories at large temporal scales. Based on this model, we were able to show a number of previously unexplored phenomena that are likely to modulate the fate of a species under environmental change:
(1) Local adaptation to heterogeneous habitat can strongly decrease a populations survival probability under temporal environmental change. When dispersal is high and habitat heterogeneous, the number of viable offspring in each generation can be drastically reduced due to migration load. At population level, this is of little consequence when the climate is stable, as long as the number of surviving juveniles can replace the parental generation. However, when the population needs to adapt to new conditions, the absolute number of beneficial mutations becomes crucial. High rates of juvenile dispersal into habitat to which they are ill-adapted reduces the effective rate at which beneficial climate mutations can be fixed in the population. Ultimately, the interaction between habitat heterogeneity and temporal environmental change leads to the observed reduction in the probability of evolutionary rescue.
(2) Local adaptation increases the probability of partial evolutionary rescue and might promote speciation. The second key result - partial evolutionary rescue - is in its mechanism closely linked to the process described above. Newly arising beneficial mutations with respect to climate can become locally highly abundant, but when the absolute fitness of dispersing individuals is lower than unity due to also carrying alleles that are not adapted to local conditions, these mutations cannot spread, leading to decreased population size and patchy distributions. Many of the model simulations showed clearly diverging evolutionary trajectories of the remaining sub-populations, indicating the potential for subsequent speciation.
(3) The degree of linkage between quantitative trait loci (QTL) coding for adaptation to climate and local environment determines whether the effect of dispersal on a population's evolvability is positive or negative. In this part of the project, we could demonstrate that under conditions of linkage between different QTL, the generally positive effect of dispersal for a species' response to environmental change might be outweighed by the increasing maladaptation that dispersal generates under local adaptation. Thus, dispersal may actually be a hindrance to evolutionary rescue, for a broad range of parameter values when linkage exists between QTL. Only for small population sizes and low selection for local environmental conditions should we expect that strong dispersal capabilities would necessarily increase population fitness and survival probability.
These above results demonstrate that many of the typically complex dynamics of evolutionary adaptation in natural populations cannot be captured without taking into account ecological processes at the individual level and the complicating effect of space. Thus, we believe that the type of allelic simulation model we developed within the framework of EMMA will be needed, if we are to ultimately make robust quantitative predictions on the likelihood of evolutionary rescue in particular populations or species. Demonstrating the strong effect of habitat heterogeneity and local adaptation on the evolutionary potential of populations will help to understand current biodiversity patterns and help to improve prediction tools on future species range dynamics.
One of the major aims of global change research is the development of reliable predictions on future geographic distributions of species, communities and functional diversity to assess the impacts on ecosystem goods and services, to develop adaptive management and conservation strategies and to demonstrate the magnitude of the problem to decision makers (DIVERSITAS-International). The development of modern statistical approaches as well as the progress in high-performance computing in the last decade allows data handling and the simulation of future climatic and biodiversity scenarios on a high technological level. However, the full potential of model-based assessments on climate change impacts is far from being tapped, since we are still lacking a comprehensive framework linking the knowledge of complementary fields of global change research such as macroecology, population ecology, community ecology, evolutionary ecology, physiology and socio-economy.
The integrand and key idea of the project EMMA is the linkage between two of these fields: evolutionary biology and macroecology. Since the processes considered here act on the opposing ends of the organisational hierarchy, they have rarely been combined. However, if we are to understand under which conditions species will be able to cope with environmental change, it is important to scale up the knowledge of the genetics underlying adaptation to the level of population demography and eventually macro-evolutionary dynamics.
As a contribution to this research agenda, we developed a modelling framework that allows investigating the co-actions of ecological and evolutionary processes at the individual-level and the resulting patterns of species distributions and evolutionary trajectories at large temporal scales. Based on this model, we were able to show a number of previously unexplored phenomena that are likely to modulate the fate of a species under environmental change:
(1) Local adaptation to heterogeneous habitat can strongly decrease a populations survival probability under temporal environmental change. When dispersal is high and habitat heterogeneous, the number of viable offspring in each generation can be drastically reduced due to migration load. At population level, this is of little consequence when the climate is stable, as long as the number of surviving juveniles can replace the parental generation. However, when the population needs to adapt to new conditions, the absolute number of beneficial mutations becomes crucial. High rates of juvenile dispersal into habitat to which they are ill-adapted reduces the effective rate at which beneficial climate mutations can be fixed in the population. Ultimately, the interaction between habitat heterogeneity and temporal environmental change leads to the observed reduction in the probability of evolutionary rescue.
(2) Local adaptation increases the probability of partial evolutionary rescue and might promote speciation. The second key result - partial evolutionary rescue - is in its mechanism closely linked to the process described above. Newly arising beneficial mutations with respect to climate can become locally highly abundant, but when the absolute fitness of dispersing individuals is lower than unity due to also carrying alleles that are not adapted to local conditions, these mutations cannot spread, leading to decreased population size and patchy distributions. Many of the model simulations showed clearly diverging evolutionary trajectories of the remaining sub-populations, indicating the potential for subsequent speciation.
(3) The degree of linkage between quantitative trait loci (QTL) coding for adaptation to climate and local environment determines whether the effect of dispersal on a population's evolvability is positive or negative. In this part of the project, we could demonstrate that under conditions of linkage between different QTL, the generally positive effect of dispersal for a species' response to environmental change might be outweighed by the increasing maladaptation that dispersal generates under local adaptation. Thus, dispersal may actually be a hindrance to evolutionary rescue, for a broad range of parameter values when linkage exists between QTL. Only for small population sizes and low selection for local environmental conditions should we expect that strong dispersal capabilities would necessarily increase population fitness and survival probability.
These above results demonstrate that many of the typically complex dynamics of evolutionary adaptation in natural populations cannot be captured without taking into account ecological processes at the individual level and the complicating effect of space. Thus, we believe that the type of allelic simulation model we developed within the framework of EMMA will be needed, if we are to ultimately make robust quantitative predictions on the likelihood of evolutionary rescue in particular populations or species. Demonstrating the strong effect of habitat heterogeneity and local adaptation on the evolutionary potential of populations will help to understand current biodiversity patterns and help to improve prediction tools on future species range dynamics.