Behavioral Strategies and the Spatial Pattern Formation of Nesting.

AbstractNesting in dense aggregations is common in central place foragers, such as group-living birds and insects. Both environmental heterogeneity and behavioral interactions are known to induce clustering of nests, but their relative importance remains unclear. We developed an individual-based model that simulated the spatial organization of nest building in a gregarious digger wasp, Bembix rostrata. This process-based model integrates environmental suitability, as derived from a microhabitat model, and relevant behavioral mechanisms related to local site fidelity and conspecific attraction. The drivers behind the nesting were determined by means of inverse modeling in which the emerging spatial and network patterns from simulations were compared with those observed in the field. Models with individual differences in behavior that include the simultaneous effect of a weak environmental cue and strong behavioral mechanisms yielded the best fit to the field data. The nest pattern formation of a central place foraging insect cannot be considered as the sum of environmental and behavioral mechanisms. We demonstrate the use of inverse modeling to understand complex processes that underlie nest aggregation in nature.

Multistability of model and real dryland ecosystems through spatial self-organization.

Spatial self-organization of dryland vegetation constitutes one of the most promising indicators for an ecosystem's proximity to desertification. This insight is based on studies of reaction-diffusion models that reproduce visual characteristics of vegetation patterns observed on aerial photographs. However, until now, the development of reliable early warning systems has been hampered by the lack of more in-depth comparisons between model predictions and real ecosystem patterns. In this paper, we combined topographical data, (remotely sensed) optical data, and in situ biomass measurements from two sites in Somalia to generate a multilevel description of dryland vegetation patterns. We performed an in-depth comparison between these observed vegetation pattern characteristics and predictions made by the extended-Klausmeier model for dryland vegetation patterning. Consistent with model predictions, we found that for a given topography, there is multistability of ecosystem states with different pattern wavenumbers. Furthermore, observations corroborated model predictions regarding the relationships between pattern wavenumber, total biomass, and maximum biomass. In contrast, model predictions regarding the role of slope angles were not corroborated by the empirical data, suggesting that inclusion of small-scale topographical heterogeneity is a promising avenue for future model development. Our findings suggest that patterned dryland ecosystems may be more resilient to environmental change than previously anticipated, but this enhanced resilience crucially depends on the adaptive capacity of vegetation patterns.

Self-organization of river vegetation leads to emergent buffering of river flows and water levels.

Global climate change is expected to impact hydrodynamic conditions in stream ecosystems. There is limited understanding of how stream ecosystems interact and possibly adapt to novel hydrodynamic conditions. Combining mathematical modelling with field data, we demonstrate that bio-physical feedback between plant growth and flow redistribution triggers spatial self-organization of in-channel vegetation that buffers for changed hydrological conditions. The interplay of vegetation growth and hydrodynamics results in a spatial separation of the stream into densely vegetated, low-flow zones divided by unvegetated channels of higher flow velocities. This self-organization process decouples both local flow velocities and water levels from the forcing effect of changing stream discharge. Field data from two lowland, baseflow-dominated streams support model predictions and highlight two important stream-level emergent properties: vegetation controls flow conveyance in fast-flowing channels throughout the annual growth cycle, and this buffering of discharge variations maintains water depths and wetted habitat for the stream community. Our results provide important evidence of how plant-driven self-organization allows stream ecosystems to adapt to changing hydrological conditions, maintaining suitable hydrodynamic conditions to support high biodiversity.

The shaping role of self-organization: linking vegetation patterning, plant traits and ecosystem functioning.

Self-organized spatial patterns are increasingly recognized for their contribution to ecosystem functioning, in terms of enhanced productivity, ecosystem stability, and species diversity in terrestrial as well as marine ecosystems. Most studies on the impact of spatial self-organization have focused on systems that exhibit regular patterns. However, there is an abundance of patterns in many ecosystems which are not strictly regular. Understanding of how these patterns are formed and how they affect ecosystem function is crucial for the broad acceptance of self-organization as a keystone process in ecological theory. Here, using transplantation experiments in salt marsh ecosystems dominated by Scirpus mariqueter, we demonstrate that scale-dependent feedback is driving irregular spatial pattern formation of vegetation. Field observations and experiments have revealed that this self-organization process affects a range of plant traits, including shoot-to-root ratio, rhizome orientation, rhizome node number, and rhizome length, and enhances vegetation productivity. Moreover, patchiness in self-organized salt marsh vegetation can support a better microhabitat for macrobenthos, promoting their total abundance and spatial heterogeneity of species richness. Our results extend existing concepts of self-organization and its effects on productivity and biodiversity to the spatial irregular patterns that are observed in many systems. Our work also helps to link between the so-far largely unconnected fields of self-organization theory and trait-based, functional ecology.

Landscapes of facilitation: how self-organized patchiness of aquatic macrophytes promotes diversity in streams.

Spatial heterogeneity plays a crucial role in the coexistence of species. Despite recognition of the importance of self-organization in creating environmental heterogeneity in otherwise uniform landscapes, the effects of such self-organized pattern formation in promoting coexistence through facilitation are still unknown. In this study, we investigated the effects of pattern formation on species interactions and community spatial structure in ecosystems with limited underlying environmental heterogeneity, using self-organized patchiness of the aquatic macrophyte Callitriche platycarpa in streams as a model system. Our theoretical model predicted that pattern formation in aquatic vegetation - due to feedback interactions between plant growth, water flow and sedimentation processes - could promote species coexistence, by creating heterogeneous flow conditions inside and around the plant patches. The spatial plant patterns predicted by our model agreed with field observations at the reach scale in naturally vegetated rivers, where we found a significant spatial aggregation of two macrophyte species around C. platycarpa. Field transplantation experiments showed that C. platycarpa had a positive effect on the growth of both beneficiary species, and the intensity of this facilitative effect was correlated with the heterogeneous hydrodynamic conditions created within and around C. platycarpa patches. Our results emphasize the importance of self-organized patchiness in promoting species coexistence by creating a landscape of facilitation, where new niches and facilitative effects arise in different locations. Understanding the interplay between competition and facilitation is therefore essential for successful management of biodiversity in many ecosystems.

Phase separation explains a new class of self-organized spatial patterns in ecological systems.

The origin of regular spatial patterns in ecological systems has long fascinated researchers. Turing's activator-inhibitor principle is considered the central paradigm to explain such patterns. According to this principle, local activation combined with long-range inhibition of growth and survival is an essential prerequisite for pattern formation. Here, we show that the physical principle of phase separation, solely based on density-dependent movement by organisms, represents an alternative class of self-organized pattern formation in ecology. Using experiments with self-organizing mussel beds, we derive an empirical relation between the speed of animal movement and local animal density. By incorporating this relation in a partial differential equation, we demonstrate that this model corresponds mathematically to the well-known Cahn-Hilliard equation for phase separation in physics. Finally, we show that the predicted patterns match those found both in field observations and in our experiments. Our results reveal a principle for ecological self-organization, where phase separation rather than activation and inhibition processes drives spatial pattern formation.

Geo-evolutionary feedbacks: integrating rapid evolution and landscape change.

We develop a conceptual framework for geo-evolutionary feedbacks which describes the mutual interplay between landscape change and the evolution of traits of organisms residing on the landscape, with an emphasis on contemporary timeframes. Geo-evolutionary feedbacks can be realized via the direct evolution of geomorphic engineering traits or can be mediated by the evolution of trait variation that affects the population size and distribution of the specific geomorphic engineering organisms involved. Organisms that modify their local environments provide the basis for patch-scale geo-evolutionary feedbacks, whereas spatial self-organization provides a mechanism for geo-evolutionary feedbacks at the landscape scale. Understanding these likely prevalent geo-evolutionary feedbacks, that occur at timescales similar to anthropogenic climate change, will be essential to better predict landscape adaptive capacity and change.

Long-distance interactions regulate the structure and resilience of coastal ecosystems.

Mounting evidence indicates that spatial interactions are important in structuring coastal ecosystems. Until recently, however, most of this work has been focused on seemingly exceptional systems that are characterized by regular, self-organized patterns. In this review, we document that interactions that operate at long distances, beyond the direct neighborhood of individual organisms, are more common and have much more far-reaching implications for coastal ecosystems than was previously realized. We review studies from a variety of ecosystem types-including cobble beaches, mussel beds, coral reefs, seagrass meadows, and mangrove forests-that reveal a startling interplay of positive and negative interactions between habitats across distances of up to a kilometer. In addition to classical feeding relations, alterations of physical conditions constitute an important part of these long-distance interactions. This entanglement of habitats has crucial implications for how humans manage coastal ecosystems, and evaluations of anthropogenic impact should explicitly address long-distance and system-wide effects before we deem these human activities to be causing little harm.

Spatially constrained adaptive rewiring in cortical networks creates spatially modular small world architectures.

A modular small-world topology in functional and anatomical networks of the cortex is eminently suitable as an information processing architecture. This structure was shown in model studies to arise adaptively; it emerges through rewiring of network connections according to patterns of synchrony in ongoing oscillatory neural activity. However, in order to improve the applicability of such models to the cortex, spatial characteristics of cortical connectivity need to be respected, which were previously neglected. For this purpose we consider networks endowed with a metric by embedding them into a physical space. We provide an adaptive rewiring model with a spatial distance function and a corresponding spatially local rewiring bias. The spatially constrained adaptive rewiring principle is able to steer the evolving network topology to small world status, even more consistently so than without spatial constraints. Locally biased adaptive rewiring results in a spatial layout of the connectivity structure, in which topologically segregated modules correspond to spatially segregated regions, and these regions are linked by long-range connections. The principle of locally biased adaptive rewiring, thus, may explain both the topological connectivity structure and spatial distribution of connections between neuronal units in a large-scale cortical architecture.