Functional spatial scale of community composition change in response to windthrow disturbance in a deciduous temperate forest

Community dynamics in local habitats are affected by landscape characteristics such as the area and connectivity of surrounding habitats at a functional spatial scale where the community responds to landscape structure. However, the functional spatial scale at which community composition is affected by landscape structure has never been explored. We assessed the functional spatial scales of composition change in birds and in three types of arthropod communities (canopy, forest-floor and flying ones) with regard to landscape heterogeneity resulting from a large typhoon in a temperate forest of Japan. We examined the effects of tree-fall disturbance on the communities at various spatial scales, with special attention to compositional evenness. The spatial scale of the best-fitting model, which was selected from models fitted to the disturbance area at stepwise spatial scales, was interpreted as the community-specific functional spatial scale. The composition of all communities studied was all significantly dependent on gap area. The functional spatial scale was highest in birds (370 m in radius), intermediate in flying arthropods (90 m) and lowest in canopy and forest-floor arthropods (10 m). This result may reflect typical dispersal ability and the spatial range of resource use in the community. Compositional changes in each community were consistent with theory regarding traits and responses of component taxa, although the enhancement of evenness was observed only in the arthropod communities. These results imply that management and reserve selection based on functional spatial scales can be effective in the conservation of biodiversity and ecosystem services at the community level.     

绿洲-荒漠过渡带景观的植被与土壤特征要素的空间异质性分析

采用变异函数的理论和方法,通过空间异质性特征比较,研究了阜康绿洲-荒漠过渡带景观中的植被盖度和土壤环境因子的空间异质性、空间格局及其关系．在此基础上,应用Kriging局部块段插值法,以三维图形显示出各个要素的空间分布格局．结果表明,除土壤全盐量空间相关性较弱外,其他各要素具有中等程度以上的空间相关性,且空间变异主要发生在较小尺度上．其中,植被盖度的空间异质性程度较高,在3km的尺度内具有明显的空间格局．从空间分布格局看,植被盖度、土壤表层含水量和土壤pH值沿样带相邻点差异对比明显,高值区和低值区交错．草本盖度与土壤表层含水量、土壤pH间有相关关系,灌木平均盖度主要依靠深层次地下水维系．灌木和草本盖度的块金值具有明显差异,可能因为二者要求不同的生态条件,因而在同等尺度上具有不同的生态学过程．     

Cross‐scale predictive modeling of CHO cell culture growth and metabolites using Raman spectroscopy and multivariate analysis

Multi‐component, multi‐scale Raman spectroscopy modeling results from a monoclonal antibody producing CHO cell culture process including data from two development scales (3 L, 200 L) and a clinical manufacturing scale environment (2,000 L) are presented. Multivariate analysis principles are a critical component to partial least squares (PLS) modeling but can quickly turn into an overly iterative process, thus a simplified protocol is proposed for addressing necessary steps including spectral preprocessing, spectral region selection, and outlier removal to create models exclusively from cell culture process data without the inclusion of spectral data from chemically defined nutrient solutions or targeted component spiking studies. An array of single‐scale and combination‐scale modeling iterations were generated to evaluate technology capabilities and model scalability. Analysis of prediction errors across models suggests that glucose, lactate, and osmolality are well modeled. Model strength was confirmed via predictive validation and by examining performance similarity across single‐scale and combination‐scale models. Additionally, accurate predictive models were attained in most cases for viable cell density and total cell density; however, these components exhibited some scale‐dependencies that hindered model quality in cross‐scale predictions where only development data was used in calibration. Glutamate and ammonium models were also able to achieve accurate predictions in most cases. However, there are differences in the absolute concentration ranges of these components across the datasets of individual bioreactor scales. Thus, glutamate and ammonium PLS models were forced to extrapolate in cases where models were derived from small scale data only but used in cross‐scale applications predicting against manufacturing scale batches. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 31:566–577, 2015     

Scaling processes and problems

A personal view is presented of current approaches to scaling processes and variables in the context of better understanding ecosystem function and predicting the consequences of global environmental change. Issues considered include spatial and temporal scales of interest, the scaling process, scaling strategy, scaling problems, heterogeneity, patchiness and non-linearity, aggregation methodology and feedbacks. Knowledge of processes in plants and vegetation is largely at small scales. The transfer of this knowledge up to larger spatial and longer temporal scales is an open-ended process with potential errors arising from heterogeneity and patchiness in the distribution of processes and non-linearities in the functional relationships between processes and environmental variables. Scaling now covers several orders of magnitude with respect to spatial and temporal scales with attendant risks of propagating errors. At larger scales the wide diversity of vegetation classes poses a problem, and it is suggested that this can be countered by classifying classes of vegetation (not species) into a small number of ‘functional types’ of vegetation. Scaling through summation of component processes and through derivation of appropriately averaged parameters is considered. However, the increasing role of feedbacks at larger spatial and longer temporal scales is an essential feature of the scaling process. Thus, understanding the feedbacks and including them in upscaling schemes is a major priority. A scaling strategy is outlined to minimize the propagation of errors. Because the scaling process is open-ended it is essential that good models are used and tested at each increase in scale.     

Landscape heterogeneity metrics as indicators of bird diversity: Determining the optimal spatial scales in different landscapes

Species distribution models are often used to study the biodiversity of ecosystems. The modelling process uses a number of parameters to predict others, such as the occurrence of determinate species, population size, habitat suitability or biodiversity. It is well known that the heterogeneity of landscapes can lead to changes in species’ abundance and biodiversity. However, landscape metrics depend on maps and spatial scales when it comes to undertaking a GIS analysis.We explored the goodness of fit of several models using the metrics of landscape heterogeneity and altitude as predictors of bird diversity in different landscapes and spatial scales. Two variables were used to describe biodiversity: bird richness and trophic level diversity, both of which were obtained from a breeding bird survey by means of point counts. The relationships between biodiversity and landscape metrics were compared using multiple linear regressions. All of the analyses were repeated for 14 different spatial scales and for cultivated, forest and grassland environments to determine the optimal spatial scale for each landscape typology.Our results revealed that the relationships between species’ richness and landscape heterogeneity using 1:10,000 land cover maps were strongest when working on a spatial scale up to a radius of 125–250 m around the sampled point (circa 4.9–19.6 ha). Furthermore, the correlation between measures of landscape heterogeneity and bird diversity was greater in grasslands than in cultivated or forested areas. The multi-spatial scale approach is useful for (a) assessing the accuracy of surrogates of bird diversity in different landscapes and (b) optimizing spatial model procedures for biodiversity mapping, mainly over extensive areas.     

Remote sensing of riverine landscapes

1. The fashion for examining riverine landscapes is changing as our technical instruments, from microprobes to satellites, expands to be able to examine the spatial and temporal relationships among biota, hydrology and geomorphology across scales from microhabitats to channel units to valleys to catchments. 2. The range of successful applications from remote sensing analyses of riverine landscapes has especially increased with the launch of many new instruments that record data across the electromagnetic spectrum. Engineering of the instruments has also improved such that knowledge of the radiometric properties of the digital data is more complete as a result of better instrumentation and installation of on‐board calibrations systems for many instruments. 3. With the development of faster processing on cheaper computers, it is now common for comprehensive data sets to be processed through algorithms that previously could only be applied to relatively small (<1 Mbyte) rasters of data. This technical advance is especially important for the statistical algorithms such as principal components and spectral mixture analysis that can decompose gradients in the spectral data. The combined effect is the production of regional views of riverine landscapes separated into components of water, vegetation and soil. 4. The landscape properties of riverine landscapes that have been most successfully measured with remote sensing data include community and habitat level classification and connectivity of waterbodies with optical and radar data. Laser and radar altimetric data measured from aircraft provide land elevations at resolutions as fine as decimetres. A remaining challenge is to achieve an exact match between the categories of landscape classification from the remote sensing analysis and data from field surveys or model outputs. 5. In contrast to many landscape properties, several water properties are now routinely measured as absolute values (water surface elevation, temperature, surface sediment concentration and algal concentration) with remote sensing. New analyses of both passive and active radar data in addition have led to measurements of inundation and wetness that are providing valuable insight into the dynamics of flooding and its effect on riverine landscapes. 6. Finally, an effective examination of the variability in landscape cover includes additional analyses of remote sensing products using pattern metrics that measure the scale of patchiness and distribution of the landscape properties. These types of variability measures at the regional scale contribute to an increased understanding of the way in which spatial heterogeneity of riverine landscapes varies across scales and how landscape filters (sensu Poff, 1997) influence the evolution of these diversity patterns.     

红松老龄林主要树种的空间异质性特征与比较的定量研究

 空间异质性研究对认识不同尺度上的生态系统结构、功能和过程具有重要的理论意义。采用地统计学理论和方法,通过空间异质性特征和比较研究了我国东北红松老龄林中主要树种,即红松(Pinus koraiensis)、云杉(Picea koraiensis)、冷杉(Abies nephrolepis)、枫桦(Betula costata)和紫椴(Tilia amurensis)的空间异质性程度、空间异质性组成、尺度和格局问题。结果表明：红松老龄林中主要树种虽然生长在同一林分中,但是它们的空间异质性与格局具有明显的差异。红松的空间异质性程度最高,分数维较低,其空间异质性主要由空间自相关部分组成,在450m的尺度内,具有明显的空间格局。枫桦的空间异质性程度最低,分数维最大,空间异质性主要由随机部分组成,在大于100m的尺度上不存在明显的空间格局。云杉和冷杉由于生长的立地相似,因此具有相似的空间异质性特征和格局。紫椴是红松最重要的伴生树种,其空间异质性程度、空间异质性组成、尺度和格局等方面与红松接近。红松老龄林中主要树种空间异质性与格局在一定尺度上的差异,反映这些树种在生态系统中具有不同的生态学功能和过程。     

长白山阔叶红松林物种多度和空间分布格局的关系

应用随机分布多度模型和聚集分布多度模型,探讨不同研究尺度下物种多度和空间分布格局的关系.结果表明,预测的物种多度不仅受物种分布面积大小的影响,还受其聚集程度的影响.物种多度和空间分布格局的关系存在着明显的尺度效应,即随着研究尺度的增加,无论是随机分布多度模型还是聚集分布多度模型,通过物种空间分布格局来预测物种多度的准确度都在下降.聚集分布多度模型预测物种多度的结果要好于随机分布多度模型,这表明该区大多数物种是聚集分布的.由于物种的空间分布格局不同,不同物种多度的预测值和真实值之间的差异也不同.因此,为了进一步提高模型预测的准确性,进一步考虑不同物种的生活史特性是必要的.     

Can we project changes in fish abundance and distribution in response to climate?

Large‐scale and long‐term changes in fish abundance and distribution in response to climate change have been simulated using both statistical and process‐based models. However, national and regional fisheries management requires also shorter term projections on smaller spatial scales, and these need to be validated against fisheries data. A 26‐year time series of fish surveys with high spatial resolution in the North‐East Atlantic provides a unique opportunity to assess the ability of models to correctly simulate the changes in fish distribution and abundance that occurred in response to climate variability and change. We use a dynamic bioclimate envelope model forced by physical–biogeochemical output from eight ocean models to simulate changes in fish abundance and distribution at scales down to a spatial resolution of 0.5°. When comparing with these simulations with annual fish survey data, we found the largest differences at the 0.5° scale. Differences between fishery model runs driven by different biogeochemical models decrease dramatically when results are aggregated to larger scales (e.g. the whole North Sea), to total catches rather than individual species or when the ensemble mean instead of individual simulations are used. Recent improvements in the fidelity of biogeochemical models translate into lower error rates in the fisheries simulations. However, predictions based on different biogeochemical models are often more similar to each other than they are to the survey data, except for some pelagic species. We conclude that model results can be used to guide fisheries management at larger spatial scales, but more caution is needed at smaller scales.     

Spatial heterogeneity and plant species richness at different spatial scales under rabbit grazing

Herbivores influence spatial heterogeneity in soil resources and vegetation in ecosystems. Despite increasing recognition that spatial heterogeneity can drive species richness at different spatial scales, few studies have quantified the effect of grazing on spatial heterogeneity and species richness simultaneously. Here we document both these variables in a rabbit-grazed grassland. We measured mean values and spatial patterns of grazing intensity, rabbit droppings, plant height, plant biomass, soil water content, ammonia and nitrate in sites grazed by rabbits and in matched, ungrazed exclosures in a grassland in southern England. Plant species richness was recorded at spatial scales ranging between 0.0001 and 150 m(2). Grazing reduced plant height and plant biomass but increased levels of ammonia and nitrate in the soil. Spatial statistics revealed that rabbit-grazed sites consisted of a mixture of heavily grazed patches with low vegetation and nutrient-rich soils (lawns) surrounded by patches of high vegetation with nutrient-poor soils (tussocks). The mean patch size (range) in the grazed controls was 2.1 +/- 0.3 m for vegetation height, 3.8 +/- 1.8 m for soil water content and 2.8 +/- 0.9 m for ammonia. This is in line with the patch sizes of grazing (2.4 +/- 0.5 m) and dropping deposition (3.7 +/- 0.6 m) by rabbits. In contrast, patchiness in the ungrazed exclosures had a larger patch size and was not present for all variables. Rabbit grazing increased plant species richness at all spatial scales. Species richness was negatively correlated with plant height, but positively correlated to the coefficient of variation of plant height at all plot sizes. Species richness in large plots (<25 m(2)) was also correlated to patch size. This study indicates that the abundance of strong competitors and the nutrient availability in the soil, as well as the heterogeneity and spatial pattern of these factors may influence species richness, but the importance of these factors can differ across spatial scales.     

A principal-components approach based on heritability for combining phenotype information

For many traits, genetically relevant disease definition is unclear. For this reason, researchers applying linkage analysis often obtain information on a variety of items. With a large number of items, however, the test statistic from a multivariate analysis may require a prohibitively expensive correction for the multiple comparisons. The researcher is faced, therefore, with the issue of choosing which variables or combinations of variables to use in the linkage analysis. One approach to combining items is to first subject the data to a principal components analysis, and then perform the linkage analysis of the first few principal components. However, principal-components analyses do not take family structure into account. Here, an approach is developed in which family structure is taken into account when combining the data. The essence of the approach is to define principal components of heritability as the scores with maximum heritability in the data set, subject to being uncorrelated with each other. The principal components of heritability may be calculated as the solutions to a generalized eigensystem problem. Four simulation experiments are used to compare the power of linkage analyses based on the principal components of heritability and the usual principal components. The first of the experiments corresponds to the null hypothesis of no linkage. The second corresponds to a setting where the two kinds of principal components coincide. The third corresponds to a setting in which they are quite different and where the first of the usual principal components is not expected to have any power beyond the type I error rate. The fourth set of experiments corresponds to a setting where the usual principal components and the principal components of heritability differ, but where the first of the usual principal components is not without power. The results of the simulation experiments indicate that the principal components of heritability can be substantially different from the standard principal components and that when they are different, substantial gains in power can result by using the principal components of heritability in place of the standard principal components in linkage analyses.     

Species richness in deciduous forests: Effects of species pools and environmental variables

Abstract. The study was conducted in deciduous forests of two Swedish regions, Öland and Uppland. It had two objectives: to (1) test the species pool hypothesis by examining if differences in small‐scale species richness are related to differences in large‐scale species richness and the size of the regional species pool, and (2) to examine the relationship between species richness and productivity and its scale‐dependence. The first data set comprised 36 sites of moderate to high productivity. In each site, we recorded the presence of vascular plant species in nested plots ranging from 0.001 to 1000 m² and measured several environmental variables. Soil pH and Ellenberg site indicator scores for nitrogen were used as estimators of productivity. The second data set included 24 transects (each with 20 1‐m² plots) on Öland in sites with low to high productivity. Species number, soil pH and relative light intensity were determined in each plot. The forest sites on Öland were more species‐rich than the Uppland sites on all spatial scales, although environmental conditions were similar. Small‐scale and large‐scale species richness were positively correlated. The results present evidence in favour of the species pool hypothesis. In the nested‐plots data set, species number was negatively correlated with pH and nitrogen indicator scores, whereas a unimodal relationship between species number and pH was found for the transect data set. These results, as well as previously published data, support the hump‐shaped relationship between species richness and productivity in Swedish deciduous forests. Two explanations for the higher species richness of the sites with moderate productivity are given: first, these sites have a higher environmental heterogeneity and second, they have a larger ‘habitat‐specific’ species pool.     

Measures of giant panda habitat selection across multiple spatial scales for species conservation

Examining ecological processes across spatial scales is crucial as animals select and use resources at different scales. We carried out field surveys in September 2005, March–September 2006, and April 2007, and used ecological niche factor analysis to determine habitat preferences for the giant panda (Ailuropoda melanoleuca) across 4 spatial scales: daily movement, core range, home range, and seasonal elevational migration. We found that giant pandas prefer conifer forest and mixed forest at higher than average elevation (2,157 m) of study area in the 4 scale models. However, we also observed significant scale differences in habitat selection. The strength of habitat preference increased with scale for the 2 disturbed forests (sparse forest and fragmented forest), and decreased with scale for 0–30° gentle slope and south- and north-facing aspect. Furthermore, habitat suitability patterns were scale-dependent. These findings highlight the need to determine species–environment associations across multiple scales for habitat management and species conservation. © 2012 The Wildlife Society.     

Scale-dependent effects of windthrow disturbance on forest arthropod communities

The effect of disturbance on local communities may operate within the context of the spatial landscape. We examined the scale-dependent effects of windthrow disturbance caused by a large typhoon on three arthropod communities in a temperate forest of Japan. Canopy arthropods were collected by beating foliage, forest-floor arthropods were collected by sweeping the vegetation, and flying arthropods were collected in Malaise traps. To assess the “functional spatial scale” at which arthropods responded to tree-fall disturbance, the gap rate was quantified at different spatial scales by sequentially enlarging the radius of a circular landscape sector in 10-m increments from 10 to 500 m. We then analyzed the responses of order richness and abundance to the gap rate for each arthropod community. The spatial scale of the significant best-fitting model, which was selected from the models fitted to the gap rate at stepwise spatial scales, was regarded as the arthropod-specific functional spatial scale. Arthropod order richness was not dependent on the gap rate. In contrast, arthropod order abundance depended significantly on the gap rate in many orders, but varied in the response direction and functional spatial scale. These order-specific, scale-dependent responses to tree-fall gaps could complicate interactions among organisms, leading to complex community organization. An understanding of the spatial processes that link the use of space by organisms with the spatial scale at which ecological processes are experienced is required to elucidate the responses of populations, communities, and biotic interactions to disturbances in a spatial landscape context.     

基于零模型的宁夏荒漠草原优势种群点格局分析

植物种群空间分布格局是多种生态过程综合作用的结果。明确植物优势种群个体的空间分布格局与形成机制有助于认识种群生态适应对策与群落多样性维持机制。以宁夏荒漠草原优势种群蒙古冰草、短花针茅、牛枝子和牛心朴子为研究对象,采用完全空间随机零模型分析其种群空间分布格局特征,并通过异质泊松零模型与泊松聚块零模型探讨生境异质性、扩散限制等因子在其空间分布格局形成过程中的作用。结果显示:(1)完全空间随机零模型下,4个物种在4 m尺度范围内表现为聚集分布,随尺度增大,逐渐过渡到随机分布和均匀分布。(2)在排除生境异质性的异质泊松零模型下,蒙古冰草种群在整个研究尺度上表现为随机分布;牛枝子、短花针茅和牛心朴子种群仅分别在0—0.2、0.1—0.4 m与0—0.2 m尺度范围内发生偏离,表现为均匀分布与聚集分布,其他尺度均为随机分布。(3)在排除扩散限制的泊松聚块零模型下,所研究种群均表现为随机分布。综上,荒漠草原优势种群在小尺度范围内主要表现为聚集分布;生境异质性与扩散限制均是驱动其空间分布格局形成的重要因子,相对而言,小尺度空间范围内扩散限制的作用更为显著。     

Small-scale spatial soil-plant relationship in semi-arid gypsum environments

Studies on soil patterning on a small scale in arid and semi-arid regions have rarely been conducted. Many papers implicitly assume that plant distribution is controlled by some soil variables acting at small scales. We have directly tackled the relationships between soil and some biotic variables including plant distribution at small scales in an Iberian semi-arid gypsum environment. This has been carried out by means of Canonical Correspondence Analysis as a hypothesis-testing tool. CCA models show that the spatial data matrix is able to explain a relevant fraction of the soil data set (P < 0.001). The most important variable, as firstly selected in the CCA stepwise selection procedure, suggests the existence of a vegetation-elevation gradient in relation to soil physical properties; the rest of selected variables indicates the existence of other spatial trends which may be related to certain microgeomorphological features. On the other hand, only the cover of annuals and the cover of litter are selected in the case of the biotic data set as constraining matrix, but not the cover of any perennial plant. Partial CCA models indicated that the remaining information explained by the spatial data set after adjusting the biotic set as covariables is also significant (p < 0.001). This variability is not related to the existence of vegetation bands as shown by the two selected variables in the case of the partial CCA models. The primary source of spatial soil variation is related to the existence of three community bands and these differences are able to explain even the change of plant life forms in vegetated band. The soil parameters controlling the changes are mainly related to texture and surface features. However, we detected other sources of spatial soil variation out of this primary model. This hierarchical spatial pattern seems to be related to some geomorphological traits of the landscape, such as soil crust strength, presence of gypsum crystals or bare zones, and not to the presence of mature gypsophytes (at least the five most frequent) which might ameliorate the soil environment. Furthermore, the biotic data set is not able to explain any new fraction of soil variability out of that already explained by the spatial data set. This revised version was published online in June 2006 with corrections to the Cover Date.     

Habitat selection of breeding riparian birds in an urban environment: untangling the relative importance of biophysical elements and spatial scale

Aim Urbanization is a leading threat to global biodiversity, yet little is known about how the spatial arrangement and composition of biophysical elements – buildings and vegetation – within a metropolitan area influence habitat selection. Here, we ask: what is the relative importance of the structure and composition of these elements on bird species across multiple spatial scales? Location The temperate metropolitan area of Cincinnati, Ohio, USA. Methods We surveyed breeding birds on 71 plots along an urban gradient. We modelled relative density for 48 bird species in relation to local woody vegetation composition and structure and to tree cover, grass cover and building density within 50–1000 m of each plot. We used an information‐theoretic approach to compare models and variables. Results At the proximate scale, native tree and understory stem frequency were the most important vegetation variables explaining bird distributions. Species’ responses to landscape biophysical features and spatial scales varied. Most native species responded positively to vegetation measures and negatively to building density. Models combining both local vegetation and landscape information represented best or competitive models for the majority of species, while models containing only local vegetation characteristics were rarely competitive. Smaller spatial scales (≤ 500 m) were most important for 36 species, and eight species had best models at larger scales (> 500 m); however, several species had competitive models across multiple scales. Main conclusions Habitat selection by birds within the urban matrix is the result of a combination of factors operating at both proximate and broader spatial scales. Efforts to manage and design urban areas to benefit native birds require both fine‐scale (e.g., individual landowners and landscape design) and larger landscape actions (e.g., regional comprehensive planning).     

Dynamic occupancy models for analyzing species' range dynamics across large geographic scales

Large‐scale biodiversity data are needed to predict species' responses to global change and to address basic questions in macroecology. While such data are increasingly becoming available, their analysis is challenging because of the typically large heterogeneity in spatial sampling intensity and the need to account for observation processes. Two further challenges are accounting for spatial effects that are not explained by covariates, and drawing inference on dynamics at these large spatial scales. We developed dynamic occupancy models to analyze large‐scale atlas data. In addition to occupancy, these models estimate local colonization and persistence probabilities. We accounted for spatial autocorrelation using conditional autoregressive models and autologistic models. We fitted the models to detection/nondetection data collected on a quarter‐degree grid across southern Africa during two atlas projects, using the hadeda ibis (Bostrychia hagedash) as an example. The model accurately reproduced the range expansion between the first (SABAP1: 1987–1992) and second (SABAP2: 2007–2012) Southern African Bird Atlas Project into the drier parts of interior South Africa. Grid cells occupied during SABAP1 generally remained occupied, but colonization of unoccupied grid cells was strongly dependent on the number of occupied grid cells in the neighborhood. The detection probability strongly varied across space due to variation in effort, observer identity, seasonality, and unexplained spatial effects. We present a flexible hierarchical approach for analyzing grid‐based atlas data using dynamical occupancy models. Our model is similar to a species' distribution model obtained using generalized additive models but has a number of advantages. Our model accounts for the heterogeneous sampling process, spatial correlation, and perhaps most importantly, allows us to examine dynamic aspects of species ranges.     

Deriving fine-scale models of human mobility from aggregated origin-destination flow data

The spatial dynamics of epidemics are fundamentally affected by patterns of human mobility. Mobile phone call detail records (CDRs) are a rich source of mobility data, and allow semi-mechanistic models of movement to be parameterised even for resource-poor settings. While the gravity model typically reproduces human movement reasonably well at the administrative level spatial scale, past studies suggest that parameter estimates vary with the level of spatial discretisation at which models are fitted. Given that privacy concerns usually preclude public release of very fine-scale movement data, such variation would be problematic for individual-based simulations of epidemic spread parametrised at a fine spatial scale. We therefore present new methods to fit fine-scale mathematical mobility models (here we implement variants of the gravity and radiation models) to spatially aggregated movement data and investigate how model parameter estimates vary with spatial resolution. We use gridded population data at 1km resolution to derive population counts at different spatial scales (down to ∼ 5km grids) and implement mobility models at each scale. Parameters are estimated from administrative-level flow data between overnight locations in Kenya and Namibia derived from CDRs: where the model spatial resolution exceeds that of the mobility data, we compare the flow data between a particular origin and destination with the sum of all model flows between cells that lie within those particular origin and destination administrative units. Clear evidence of over-dispersion supports the use of negative binomial instead of Poisson likelihood for count data with high values. Radiation models use fewer parameters than the gravity model and better predict trips between overnight locations for both considered countries. Results show that estimates for some parameters change between countries and with spatial resolution and highlight how imperfect flow data and spatial population distribution can influence model fit.     

The importance of local and landscape-scale processes to the occupancy of wetlands by pond-breeding amphibians

Variation in the distribution and abundance of species across landscapes has traditionally been attributed to processes operating at fine spatial scales (i.e., environmental conditions at the scale of the sampling unit), but processes that operate across larger spatial scales such as seasonal migration or dispersal are also important. To determine the relative importance of these processes, we evaluated hypothesized relationships between the probability of occupancy in wetlands by two amphibians [wood frogs (Lithobates sylvaticus) and boreal chorus frogs (Pseudacris maculata)] and attributes of the landscape measured at three spatial scales in Rocky Mountain National Park, Colorado. We used cost-based buffers and least-cost distances to derive estimates of landscape attributes that may affect occupancy patterns from the broader spatial scales. The most highly ranked models provide strong support for a positive relationship between occupancy by breeding wood frogs and the amount of streamside habitat adjacent to a wetland. The model selection results for boreal chorus frogs are highly uncertain, though several of the most highly ranked models indicate a positive association between occupancy and the number of neighboring, occupied wetlands. We found little evidence that occupancy of either species was correlated with local-scale attributes measured at the scale of individual wetlands, suggesting that processes operating at broader scales may be more important in influencing occupancy patterns in amphibian populations.