Area-concentrated search (ACS) is a simple movement rule implying that an animal searches for resources using a ’state-dependent correlated random walk’. Accordingly, a forager increases its searching intensity by reducing the directionality of movement (’intensive search mode’ or ISM) when it detects a resource item, but if it searches unsuccessfully for a while, it returns to a more straight-line movement to search for new resource locations elsewhere (’extensive search mode’ or ESM). We propose a modified ACS, called delayed-response ACS (dACS), which would be more efficient in resource collection than standard ACS. Instead of immediately switching from ESM to ISM when encountering a resource, as is done in standard ACS, an individual foraging in the dACS mode delays this switch by ’x’ steps so it continues moving in a straight line for a while before switching to ISM. Our results show that an individual with a suitable delay parameter ’x’ for the dACS achieves substantially higher foraging success than an individual with standard ACS (x=0). Optimal foraging success occurs when ’x’ is approximately similar to the patch radius ’r’. This is because, with dACS, an individual can penetrate deeper into a cluster and stay longer within it, ultimately increasing the number of resources collected. Modifying the half-saturation constant ’h’ also affects the success of foraging, but the effects depend on resource density and cluster size. Generally, ’h’ modulates the optimal ’x’ value only slightly. dACS can be interpreted as a survey movement within a resource cluster before switching from ESM to ISM. The dACS rule does not rely on complex spatial memory but only on memorizing whether resources were found or not. It may thus occur in a wide range of taxa, from organisms without a central nervous system to animals with complex brain systems.

Local adaptation, environmental tolerance, and dispersal mutually influence the evolution of one another and each are in turn influenced by landscape spatial structure. While each of the three have been investigated frequently in isolation in relation to spatial structure, the three have rarely been considered together. In this study, we explored how the magnitude of landscape environmental heterogeneity (compositional heterogeneity), and environmental spatial autocorrelation jointly affect the evolution of environmental niche optima, tolerance, dispersal frequency, and dispersal distance using a spatially explicit individual based model simulating organisms living, reproducing, and dispersing within grid-based fractal landscapes. Compositional heterogeneity tended to have the strongest influence over patterns while spatial autocorrelation typically played a mediating role. We found that niche adaptation and dispersal patterns were driven by a balance between pressure to avoid risk imposed by spatial heterogeneity and pressure to hedge against risk imposed by temporal environmental fluctuations. Dispersal frequency and dispersal distance were affected differently by spatial structure, underscoring the importance of considering the two independently.

Landscape structure plays a key role in mediating a variety of ecological processes affecting biodiversity patterns, however its precise effects and the mechanisms underpinning them remain unclear. While the effects of landscape structure have been extensively investigated both empirically, and theoretically from a metapopulation perspective, the effects of spatial structure at the landscape scale remain poorly explored from a metacommunity perspective. Here, we attempt to address this gap using a spatially explicit, individual-based metacommunity model to explore the effects of landscape compositional heterogeneity and per se spatial configuration on diversity at the landscape and patch level via their influence on long term community assembly processes. Our model simulates communities composed of lineages of annual, asexual organisms living, reproducing, dispersing, and competing within grid-based, fractal landscapes which vary in their magnitude of spatial environmental heterogeneity and in their degree of spatial environmental autocorrelation. Communities are additionally subject to temporal environmental fluctuation and external immigration, allowing for turnover in community composition. We found that compositional heterogeneity and spatial autocorrelation had differing effects on richness and diversity and the landscape and patch scales. We also note a slight negative effect of compositional heterogeneity on median total landscape population size. Landscape level diversity was driven by community dissimilarity at the patch level and increased with greater heterogeneity, while landscape richness was largely the result of short-term accumulation of immigrants and decreased with greater compositional heterogeneity. Both richness and diversity decreased in variance with greater compositional heterogeneity, indicating a reduction in community turnover over time. Patch-level richness and diversity patterns appeared to be driven by overall landscape richness and local mass effects, resulting in maximum patch level richness and diversity at moderate levels of compositional heterogeneity and high spatial autocorrelation.