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Questions: How do the dominant species in a Mediterranean community (Juniperus sabina, Juniperus communis and Pinus sylvestris) colonize abandoned fields? At what rates? Does dispersal limitation shape species colonization patterns? Does J. sabina act as nurse plant for the other two species? If so, in which stages of development: seedlings and saplings or older individuals? Location: Abandoned crop fields in the Alto Tajo Natural Park, central-eastern Spain. Methods: We mapped all individuals of the three species in three 4–14-ha plots, and aged them using dendrochronology. Spatial patterns in 2000, 1980 and 1960 were reconstructed according to estimated ages in 2014. We used a battery of spatial point-pattern analyses to evaluate dispersal in junipers, dispersal in pines, and the role of J. sabina as nurse plant for the other two species. Results: Both junipers colonized earlier than pines, probably due to their more effective endozoochorous dispersal. Late-coming pines, once established, expanded faster due to their higher seed productivity. Recent recruits of J. communis and P. sylvestris showed a random relationship with J. sabina canopies, whereas spatial patterns of older individuals in relation to J. sabina canopies ranged from attraction (plot 2 and marginally plot 1), suggesting facilitation, to repulsion in plot 3. These differences in spatial patterns between plots could be related to a shift in dominant herbivores, from sheep (plots 1 and 2) to red deer (plot 3). Conclusions: Dispersal and plant–plant interactions drove colonization in Mediterranean old fields. The inclusion of a temporal perspective in the analysis of spatial patterns allowed the detection of shifting interactions between J. sabina and the other two species, depending on their life stage. This is a clear advance compared with the usual static analyses, as it provides additional clues to interpret the mechanisms and processes underlying their origin.

Understanding spatial controls on wildfires is important when designing adaptive fire management plans and optimizing fuel treatment locations on a forest landscape. Previous research about this topic focused primarily on spatial controls for fire origin locations alone. Fire spread and behavior were largely overlooked. This paper contrasts the relative importance of biotic, abiotic, and anthropogenic constraints on the spatial pattern of fire occurrence with that on burn probability (i.e., the probability that fire will spread to a particular location). Spatial point pattern analysis and landscape succession fire model (LANDIS) were used to create maps to show the contrast. We quantified spatial controls on both fire occurrence and fire spread in the Midwest Ozark Highlands region, USA. This area exhibits a typical anthropogenic surface fire regime. We found that (1) human accessibility and land ownership were primary limiting factors in shaping clustered fire origin locations; (2) vegetation and topography had a negligible influence on fire occurrence in this anthropogenic regime; (3) burn probability was higher in grassland and open woodland than in closed-canopy forest, even though fire occurrence density was less in these vegetation types; and (4) biotic and abiotic factors were secondary descriptive ingredients for determining the spatial patterns of burn probability. This study demonstrates how fire occurrence and spread interact with landscape patterns to affect the spatial distribution of wildfire risk. The application of spatial point pattern data analysis would also be valuable to researchers working on landscape forest fire models to integrate historical ignition location patterns in fire simulation.

Envelope tests are a popular tool in spatial statistics, where they are used in goodness-of-fit testing. These tests graphically compare an empirical function T(r) with its simulated counterparts from the null model. However, the type I error probability α is conventionally controlled for a fixed distance r only, whereas the functions are inspected on an interval of distances I. In this study, we propose two approaches related to Barnard's Monte Carlo test for building global envelope tests on l: ordering the empirical and simulated functions on the basis of their r-wise ranks among each other, and the construction of envelopes for a deviation test. These new tests allow the a priori choice of the global and they yield p-values. We illustrate these tests by using simulated and real point pattern data.

This paper reviews a number of methods for the exploration and modelling of spatial point patterns with particular reference to geographical epidemiology (the geographical incidence of disease). Such methods go well beyond the conventional 'nearest-neighbour' and 'quadrat' analyses which have little to offer in an epidemiological context because they fail to allow for spatial variation in population density. Correction for this is essential if the aim is to assess the evidence for 'clustering' of cases of disease. We examine methods for exploring spatial variation in disease risk, spatial and space-time clustering, and we consider methods for modelling the raised incidence of disease around suspected point sources of pollution. All methods are illustrated by reference to recent case studies including child cancer incidence, Burkitt's lymphoma, cancer of the larynx and childhood asthma. An Appendix considers a range of possible software environments within which to apply these methods. The links to modern geographical information systems are discussed.

In the analysis of spatial point patterns, an important role is played by statistical tests based on simulation envelopes, such as the envelope of simulations of Ripley's K function. Recent ecological literature has correctly pointed out a common error in the interpretation of simulation envelopes. However, this has led to a widespread belief that the tests themselves are invalid. On the contrary, envelope-based statistical tests are correct statistical procedures, under appropriate conditions. In this paper, we explain the principles of Monte Carlo tests and their correct interpretation, canvas the benefits of graphical procedures, measure the statistical performance of several popular tests, and make practical recommendations. There are several caveats including the under-recognized problem that Monte Carlo tests of goodness of fit are probably conservative if the model parameters have to be estimated from data. Finally, we discuss whether graphs of simulation envelopes can be used to infer the scale of spatial interaction.

Objectives Investigate the spatial concentrations and spatial stability of criminal event data at the micro-spatial unit of analysis in Vancouver, British Columbia. Methods Geo-referenced crime data, 2003–2013, representing four property crime types (commercial burglary, mischief, theft from vehicle, theft of vehicle) are analyzed considering crime concentrations at the street segment and street intersection level as well as through the use of a nonparametric spatial point pattern test that identifies the stability in spatial point patterns in pairwise and longitudinal contexts. Results Property crime in Vancouver is highly concentrated in a small percentage of street segments and intersections, as few as 5 % of street segments and intersections in 2013 depending on the crime type. The spatial point pattern test shows that spatial stability is almost always present when considering all street segments and intersections. However, when only considering the street segments and intersections that have crime, spatial stability is only present in recent years for pairwise comparisons and moderately stable in the longitudinal tests. Conclusions Despite the crime drop that has occurred in Vancouver, there is still spatial stability present over time at levels suitable for theoretical development. However, caution must be taken when developing initiatives for situational crime prevention.

ContextsThe expansion of fast-growing Moso bamboo (Phyllostachys edulis) into forests is likely further favored by climate change, creating more transitional regions within forests. Such forest-bamboo transitional zones provide windows to look at ecological processes driving bamboo’s interaction with competing species across space.ObjectivesWe tested the hypothesis that spatial patterns at different scales of the ecotone can inform bamboo’s invasive spread and its competitive engulfing strategy, with the allocation of biomass and resources within a bamboo colony being a key life-history strategy to facilitate its spatial spread.MethodsWe used remote sensing imagery and field survey data to analyze the dynamics of bamboo-tree transitional boundaries in Tianmu Mountain Nature Reserve (TMNR) in southeastern China. We evaluated bamboo’s spatial spread and its allocation of resources along the transitional gradient.ResultsBoth remote sensing and field data showed bamboo recovery and advancement into tree territories after the extensive logging of bamboo but with a slower spread compared to historical records. The spatial distributions of bamboo and tree stems were not random at their transitional interfaces and were affected by competition. Successful bamboo expansion into trees required close coordination between stems and rhizomes within a colony, as they served different functions in clonal integration.ConclusionsOur study initiates a scale-dependent analysis of shifts in bamboo-tree boundaries, which provides insights on how to accurately predict future bamboo distributions under climate change accounting for interspecific competition and bamboo’s clonal integration of resources.

We propose a computationally efficient and statistically principled method for kernel smoothing of point pattern data on a linear network. The point locations, and the network itself, are convolved with a two-dimensional kernel and then combined into an intensity function on the network. This can be computed rapidly using the fast Fourier transform, even on large networks and for large bandwidths, and is robust against errors in network geometry. The estimator is consistent, and its statistical efficiency is only slightly suboptimal. We discuss bias, variance, asymptotics, bandwidth selection, variance estimation, relative risk estimation and adaptive smoothing. The methods are used to analyse spatially varying frequency of traffic accidents in Western Australia and the relative risk of different types of traffic accidents in Medellín, Colombia.

Sea pens are colonial octocorals inhabiting mostly muddy and sandy soft sediments, globally. Despite their cosmopolitan distribution, evidence of high functional value, and susceptibility to external stressors, sea pens remain poorly investigated relative to other coral species. Here, a quantitative analysis of the fine-scale spatial patterns of deep-sea sea pens (Octocorallia: Pennatuloidea) within a submarine canyon conservation area in English waters is conducted using Remotely Operated Vehicle (ROV) seabed images, three-dimensional photogrammetry models, and Spatial Point Pattern Analysis (SPPA). The density distribution of sea pens at the canyon flank scale (100s of metres) is also reported for context. At the canyon flank scale, sea pen density decreases with increasing depth, with colonies virtually absent from waters deeper than ca. 1100 m. At the fine scale, sea pens exhibit a tendency to cluster relative to sea pens and tube-dwelling anemones (Cerianthidae sp.), suggesting mutual habitat associations. Conversely, overdispersion best characterises the nature of sea pen distribution relative to stalked sponges ( Hyalonema sp.), suggesting occurrence of competition or, possibly, allelopathy. This study demonstrates that high-resolution spatial analysis techniques can be employed to disentangle biotic interactions among a set of taxa, revealing key processes and structures underlying the ecology of benthic fauna.

Spatially explicit consideration of species distribution can significantly add to our understanding of species coexistence. In this paper, we evaluated the relative importance of habitat heterogeneity and other clustering processes (e.g., dispersal limitation, collectively called the non‐habitat clustering process) in explaining the spatial distribution patterns of 341 tree species in three stem‐mapped 25–50 ha plots of tropical, subtropical, and temperate forests. Their relative importance was estimated by a method that can take one mechanism into account when estimating the effects of the other mechanism and vice versa. Our results demonstrated that habitat heterogeneity was less important in explaining the observed species patterns than other clustering processes in plots with flat topography but was more important in one of the three plots that had a complex topography. Meanwhile, both types of clustering mechanisms (habitat or non‐habitat) were pervasive among species at the 50‐ha scale across the studied plots. Our analyses also revealed considerable variation among species in the relative importance of the two types of mechanism within each plot and showed that this species‐level variation can be partially explained by differences in dispersal mode and growth form of species in a highly heterogeneous environment. Our findings provide new perspectives on the formation of species clustering. One important finding is that a significant species–habitat association does not necessarily mean that the habitat heterogeneity has a decisive influence on species distribution. The second insight is that the large species‐level variation in the relative importance of the two types of clustering mechanisms should not be ignored. Non‐habitat clustering processes can play an important role on species distribution.