Automated 3D tree-ring detection and measurement from X-ray computed tomography

Tree ring analysis is essential to reveal the environmental information encoded in the wood structure. It provides quantitative data on the anatomical structure which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, to support global vegetation models and for the dendrochronological analysis of archaeological wooden artefacts. Currently, several imaging-based methods for tree-ring detection and tree-ring feature estimation exist. However, despite advances in computer vision and edge recognition algorithms, detection of tree-rings is mostly limited to two-dimensional (2D) datasets and performed manually in some cases. This paper describes a new approach to estimate the three-dimensional (3D) structure of tree rings and their width automatically from X-ray computed tomography data. This approach relies on a modified Canny edge detection algorithm, which is capable of detecting fully connected tree-ring edges throughout the image stack. Our results show that this approach performs well on six tree species having conifer, ring-porous and diffuse-porous ring boundary structures. In our study, image denoising proved to be a critical step to achieve accurate results. A major advantage of this procedure is that it requires very little to no user interaction rendering it a reproducible procedure for tree-ring width measurements. As it also provides 3D representations of the ring edges, it also may be used in the future for the inspection of anatomical features.     

景观生态界面边界判定与动态模拟研究进展

栖息地边界对景观结构和功能具深远影响,既影响局部又作用于更大尺度区域的生态过程,同时界面的动态特征通过反馈机制影响着不同种群、群落以及生态系统。因此,在景观生态界面研究中,界面尺度依赖性和时空动态性的定量化研究已成为模型和统计学者的研究热点。鉴于此,通过介绍生态界面描述、界面监测及相关边界动态变化特征研究,阐述统计学和数学方法在不同生态系统、生态过程及尺度下界面研究中的应用,同时指出两者结合研究在生态界面定量的研究中仍面临着概念和方法上的挑战,为进一步提高景观生态界面综合研究水平提供参考。     

Effects of incorporating spatial autocorrelation into the analysis of species distribution data

Aim  Spatial autocorrelation (SAC) in data, i.e. the higher similarity of closer samples, is a common phenomenon in ecology. SAC is starting to be considered in the analysis of species distribution data, and over the last 10 years several studies have incorporated SAC into statistical models (here termed 'spatial models'). Here, I address the question of whether incorporating SAC affects estimates of model coefficients and inference from statistical models.
Methods  I review ecological studies that compare spatial and non-spatial models.
Results  In all cases coefficient estimates for environmental correlates of species distributions were affected by SAC, leading to a mis-estimation of on average c . 25%. Model fit was also improved by incorporating SAC.
Main conclusions  These biased estimates and incorrect model specifications have implications for predicting species occurrences under changing environmental conditions. Spatial models are therefore required to estimate correctly the effects of environmental drivers on species present distributions, for a statistically unbiased identification of the drivers of distribution, and hence for more accurate forecasts of future distributions.     

Body size patterns in Drosophila inhabiting a mesocosm: interactive effects of spatial variation in temperature and abundance

Body size is a major component of fitness. However, the relative contributions of different factors to optimal size, and the determinants of spatial and temporal variation in size, have not been fully established empirically. Here, we use a mesocosm of a Drosophilidae assemblage inhabiting decaying nectarines to investigate the influence of spatial variation in temperature on adult body size in Drosophila simulans Sturtevant. Two treatments were established; one in the sun where developing larvae were exposed to high temperatures and the other in the shade where temperature conditions were milder. The simple developmental effects of temperature differences (i.e. larger flies are likely to emerge from cooler environments), or the simple effects of stressful temperatures (i.e. high temperatures yield wing abnormalities and smaller flies), were overridden by interactive effects between temperature and larval density. Emergences were lower in the sun than shade, probably as a result of temperature-induced mortality. However, flies attained the same final sizes in the shade and sun. In addition, abnormally winged flies were clustered in the shaded treatments. In the shade treatments, where emergences were higher than in the sun, stressful conditions as a result of high larval density likely resulted in wing abnormalities and small size. Consequently, there was little spatial variation in size across the mesocosm, but substantial spatial variation in abundance. Under natural conditions both mortality and non-lethal effects of temperature and/or crowding are likely to play a role in the evolution of body size.     

A sampling study of the size and power of tests for paired survival data

The paired-t, sign, and signed rank tests were compared for samples from a bivariate exponential distribution. Each is a valid α-level test. One test was not uniformly more powerful than the others for all sample sizes, α levels, correlations, and alternative hypotheses considered, but the signed rank test did well consistently. It was always preferable to the sign test and never was appreciably worse than the paired-t test. The relative performance of the tests depends on α as well as the sample size.     

Spatial vision in the praying mantis: is distance implicated in size detection?

Summary The catching behaviour of the praying mantis Sphodromantis viridis is investigated in order to see whether or not the detection of prey size depends on the detection of prey distance. A first experiment demonstrates the mantid's ability to discriminate small differences in prey distance. Next, the preferred prey size is determined for a number of distances with the preference being indicated by the strike rates. The results demonstrate that the mantid's judgements of size are based on a relative (angular) scale rather than on an absolute (millimetre) scale. This is a strong piece of evidence that a relation between size and distance does not exist. Finally, the attack behaviour is analysed in detail, but it turns out that prey size has no effect on the organization of both the lunge of the body and the strike of the raptorial forelegs. Taken together, the findings of this study suggest that mantids localize prey with precision, but they do so without any knowledge of the absolute size of prey.     

Plant size and spatial pattern in a natural population of Myosotis micrantha

Abstract. We examined spatial distributions and plant sizes along a transect through a natural population of a winter annual, Myosotis micrantha. A size hierarchy existed, as indicated by high values of Gini coefficients of inequality for plant mass and correlated measures. Plants with no immediate conspecific neighbors were larger than plants with one or more near neighbors, suggesting that competition from near neighbors depressed plant size. However, there was strong positive spatial autocorrelation in plant size: large plants were associated with large neighbors and small ones with small neighbors. Plant size was also positively correlated with the combined biomass of near neighbors. The population formed a two-phase mosaic of patches of relatively large plants alternating with patches of smaller plants. The data suggest that individual plants compete with conspecifics, but the effects of competition are symmetrical. The most likely explanations for this spatially structured size hierarchy are variation in plant density, patchy distribution of resources, or a combination of the two.     

Conditionally autoregressive models improve occupancy analyses of autocorrelated data: An example with environmental DNA

Site occupancy‐detection models (SODMs) are statistical models widely used for biodiversity surveys where imperfect detection of species occurs. For instance, SODMs are increasingly used to analyse environmental DNA (eDNA) data, taking into account the occurrence of both false‐positive and false‐negative errors. However, species occurrence data are often characterized by spatial and temporal autocorrelation, which might challenge the use of standard SODMs. Here we reviewed the literature of eDNA biodiversity surveys and found that most of studies do not take into account spatial or temporal autocorrelation. We then demonstrated how the analysis of data with spatial or temporal autocorrelation can be improved by using a conditionally autoregressive SODM, and show its application to environmental DNA data. We tested the autoregressive model on both simulated and real data sets, including chronosequences with different degrees of autocorrelation, and a spatial data set on a virtual landscape. Analyses of simulated data showed that autoregressive SODMs perform better than traditional SODMs in the estimation of key parameters such as true‐/false‐positive rates and show a better discrimination capacity (e.g., higher true skill statistics). The usefulness of autoregressive SODMs was particularly high in data sets with strong autocorrelation. When applied to real eDNA data sets (eDNA from lake sediment cores and freshwater), autoregressive SODM provided more precise estimation of true‐/false‐positive rates, resulting in more reasonable inference of occupancy states. Our results suggest that analyses of occurrence data, such as many applications of eDNA, can be largely improved by applying conditionally autoregressive specifications to SODMs.     

Edge Effects on the Density of Cheirogaleus major

We investigated how greater dwarf lemur (Cheirogaleus major) densities, ambient air temperature, and dendrometrics (tree height and diameter) varied along forest edge-interior gradients in the Vohibola III Classified Forest in SE Madagascar. We also assessed if spatial variations in densities of Cheirogaleus major provide indirect evidence of increased predation pressure in the transition zone between edge and interior forest habitats, i.e., an ecological trap. We conducted diurnal temperature surveys (N = 394) and nocturnal surveys of Cheirogaleus major (N = 182) over 2 yr along 4 1250-m transects that ran perpendicular to the forest edge in Vohibola III. We did not see Cheirogaleus major from May to mid-September, and the highest sighting frequency occurred during October–November. Cheirogaleus major exhibited a negative edge response because densities ranged from low levels in edge habitats to higher levels in the forest interior. After we tested for spatial autocorrelation, edge-related variations in densities of Cheirogaleus major covaried most strongly with tree diameter. Edge responses of Cheirogaleus major may reflect spatial variations in fruit and liana abundance, though data are needed on the precise relationship between tree diameter and food production to confirm the relationship. Edge-related variations in densities of Cheirogaleus major may also provide indirect evidence of an ecological trap. Testing and controlling for spatial autocorrelation should be important components of future studies of primate conservation biology and ecology.     

The effect of aquatic and terrestrial habitat characteristics on occurrence and breeding probability in a montane amphibian: insights from a spatially explicit multistate occupancy model

Understanding species distribution and predicting range shifts are major goals of ecology and biogeography. Obtaining reliable predictions of how species distribution might change in response to habitat change requires knowledge of habitat availability, occupancy, use for breeding, and spatial autocorrelation in these parameters. Amphibians in alpine areas provide an excellent model system for disentangling habitat drivers of occupancy from that of breeding while explicitly accounting for spatial autocorrelation. We focused on the widespread common frog (Rana temporaria) inhabiting alpine lakes in the Southern Carpathians, Romania. We used single season multistate occupancy models developed to account for imperfect detection and spatial autocorrelation to estimate the occupancy and breeding probabilities and to evaluate their response to habitat characteristics. We found that frogs do not occur in all water bodies [occupancy probability: 0.697; 95% credible interval (0.614, 0.729)] and do not breed in a substantial proportion of water bodies where they occur [breeding probability conditional on occupancy: 0.707; 95% credible interval (0.670, 0.729)]. Habitat characteristics explain water body occupancy but not breeding probability; and altitude, water body surface area, water body sinuosity and permanency, presence of invertebrates, and grazing along the banks all had positive effects on occupancy. We also detected strong spatial autocorrelation in occupancy and breeding probabilities. Thus, our results indicate that habitat choice by montane amphibians is influenced by both spatial autocorrelation and habitat characteristics. Because spatial autocorrelations matter and because the presence of adults is not the same as the presence of a reproducing population, it will be difficult to predict the effects of habitat change on high altitude amphibian populations.     

Isolation by distance,based on microsatellite data,tested with spatial autocorrelation (spaida) and assignment test (spassign)

spassign and spaida are two small programs useful to detect isolate by distance of microsatellite loci. The programs are written in C and are available for Linux and Windows system at http://www.hi.is/~snaebj/programs.html . spaida calculates two estimates of spatial autocorrelation, Moran's I and Geary's c, first by assuming the infinite allele model, and second by assuming a stepwise mutational model. spassign calculates the assignment probabilities of an individuals genotype to the location where it was sampled and compares probabilities of assignment to other locations. Genetic distances among regions based on the overall differences in likelihoods are calculated.     

Effects of species and habitat positional errors on the performance and interpretation of species distribution models

Aim A key assumption in species distribution modelling is that both species and environmental data layers contain no positional errors, yet this will rarely be true. This study assesses the effect of introduced positional errors on the performance and interpretation of species distribution models. Location Baixo Alentejo region of Portugal. Methods Data on steppe bird occurrence were collected using a random stratified sampling design on a 1‐km² pixel grid. Environmental data were sourced from satellite imagery and digital maps. Error was deliberately introduced into the species data as shifts in a random direction of 0–1, 2–3, 4–5 and 0–5 pixels. Whole habitat layers were shifted by 1 pixel to cause mis‐registration, and the cumulative effect of one to three shifted layers investigated. Distribution models were built for three species using three algorithms with three replicates. Test models were compared with controls without errors. Results Positional errors in the species data led to a drop in model performance (larger errors having larger effects – typically up to 10% drop in area under the curve on average), although not enough for models to be rejected. Model interpretation was more severely affected with inconsistencies in the contributing variables. Errors in the habitat layers had similar although lesser effects. Main conclusions Models with species positional errors are hard to detect, often statistically good, ecologically plausible and useful for prediction, but interpreting them is dangerous. Mis‐registered habitat layers produce smaller effects probably because shifting entire layers does not break down the correlation structure to the same extent as random shifts in individual species observations. Spatial autocorrelation in the habitat layers may protect against species positional errors to some extent but the relationship is complex and requires further work. The key recommendation must be that positional errors should be minimised through careful field design and data processing.     

Spatial regression techniques for inter‐population data: studying the relationships between morphological and environmental variation

Understanding the importance of environmental dimensions behind the morphological variation among populations has long been a central goal of evolutionary biology. The main objective of this study was to review the spatial regression techniques employed to test the association between morphological and environmental variables. In addition, we show empirically how spatial regression techniques can be used to test the association of cranial form variation among worldwide human populations with a set of ecological variables, taking into account the spatial autocorrelation in data. We suggest that spatial autocorrelation must be studied to explore the spatial structure underlying morphological variation and incorporated in regression models to provide more accurate statistical estimates of the relationships between morphological and ecological variables. Finally, we discuss the statistical properties of these techniques and the underlying reasons for using the spatial approach in population studies.     

Modelling occupancy of an imperilled stream fish at multiple scales while accounting for imperfect detection: implications for conservation

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Testing Bergmann's rule and the resource seasonality hypothesis in Malagasy primates using GIS-based climate data

We tested four major hypotheses on the ecological aspects of body mass variation in extant Malagasy strepsirrhines: thermoregulation, resource seasonality/scarcity, resource quality, and primary productivity. These biogeographic hypotheses focus on the ecological aspects of body mass variation, largely ignoring the role of phylogeny for explaining body mass variation within lineages. We tested the independent effects of climate and resource-related variables on variation in body mass among Malagasy primates using recently developed comparative methods that account for phylogenetic history and spatial autocorrelation. We extracted data on lemur body mass and climate variables for a total of 43 species from 39 sites. Climatic data were obtained from the WorldClim database, which is based on climate data from weather stations compiled around the world. Using generalized linear models that incorporate parameters to account for phylogenetic and spatial autocorrelation, we found that diet and climate variables were weak predictors of lemur body mass. Moreover, there was a strong phylogenetic effect relative to the effects of space on lemur body mass in all models. Thus, we failed to find support for any of the four hypotheses on patterns of geography and body mass in extant strepsirrhines. Our results indicate that body mass has been conserved since early in the evolutionary history of each genus, while species diversified into different environmental niches. Our findings are in contrast to some previous studies that have suggested resource and climate related effects on body mass, though these studies have examined this question at different taxonomic and/or geographic scales.     

Effects of multiple founder populations on spatial genetic structure of reintroduced American martens

Reintroductions and translocations are increasingly used to repatriate or increase probabilities of persistence for animal and plant species. Genetic and demographic characteristics of founding individuals and suitability of habitat at release sites are commonly believed to affect the success of these conservation programs. Genetic divergence among multiple source populations of American martens (Martes americana) and well documented introduction histories permitted analyses of post‐introduction dispersion from release sites and development of genetic clusters in the Upper Peninsula (UP) of Michigan <50 years following release. Location and size of spatial genetic clusters and measures of individual‐based autocorrelation were inferred using 11 microsatellite loci. We identified three genetic clusters in geographic proximity to original release locations. Estimated distances of effective gene flow based on spatial autocorrelation varied greatly among genetic clusters (30–90 km). Spatial contiguity of genetic clusters has been largely maintained with evidence for admixture primarily in localized regions, suggesting recent contact or locally retarded rates of gene flow. Data provide guidance for future studies of the effects of permeabilities of different land‐cover and land‐use features to dispersal and of other biotic and environmental factors that may contribute to the colonization process and development of spatial genetic associations.     

EcoGenetics: An R package for the management and exploratory analysis of spatial data in landscape genetics

The integration of ecology and genetics has become established in recent decades, in hand with the development of new technologies, whose implementation is allowing an improvement of the tools used for data analysis. In a landscape genetics context, integrative management of population information from different sources can make spatial studies involving phenotypic, genotypic and environmental data simpler, more accessible and faster. Tools for exploratory analysis of autocorrelation can help to uncover the spatial genetic structure of populations and generate appropriate hypotheses in searching for possible causes and consequences of their spatial processes. This study presents EcoGenetics, an R package with tools for multisource management and exploratory analysis in landscape genetics.