From topography to hydrology—The modifiable area unit problem impacts freshwater species distribution models

Species distribution models (SDMs) are statistical tools to identify potentially suitable habitats for species. For SDMs in river ecosystems, species occurrences and predictor data are often aggregated across subcatchments that serve as modeling units. The level of aggregation (i.e., model resolution) influences the statistical relationships between species occurrences and environmental predictors—a phenomenon known as the modifiable area unit problem (MAUP), making model outputs directly contingent on the model resolution. Here, we test how model performance, predictor importance, and the spatial congruence of species predictions depend on the model resolution (i.e., average subcatchment size) of SDMs. We modeled the potential habitat suitability of 50 native fish species in the upper Danube catchment at 10 different model resolutions. Model resolutions were derived using a 90‐m digital‐elevation model by using the GRASS‐GIS module r.watershed. Here, we decreased the average subcatchment size gradually from 632 to 2 km². We then ran ensemble SDMs based on five algorithms using topographical, climatic, hydrological, and land‐use predictors for each species and resolution. Model evaluation scores were consistently high, as sensitivity and True Skill Statistic values ranged from 86.1–93.2 and 0.61–0.73, respectively. The most contributing predictor changed from topography at coarse, to hydrology at fine resolutions. Climate predictors played an intermediate role for all resolutions, while land use was of little importance. Regarding the predicted habitat suitability, we identified a spatial filtering from coarse to intermediate resolutions. The predicted habitat suitability within a coarse resolution was not ported to all smaller, nested subcatchments, but only to a fraction that held the suitable environmental conditions. Across finer resolutions, the mapped predictions were spatially congruent without such filter effect. We show that freshwater SDM predictions can have consistently high evaluation scores while mapped predictions differ significantly and are highly contingent on the underlying subcatchment size. We encourage building freshwater SDMs across multiple catchment sizes, to assess model variability and uncertainties in model outcomes emerging from the MAUP.     

Generalized additive mixed models for disentangling long‐term trends,local anomalies,and seasonality in fruit tree phenology

Quantifying temporal patterns of ephemeral plant structures such as leaves, flowers, and fruits gives insight into both plant and animal ecology. Different scales of temporal changes in fruits, for example within‐ versus across‐year variability, are driven by different processes, but are not always easy to disentangle. We apply generalized additive mixed models (GAMMs) to study a long‐term fruit presence–absence data set of individual trees collected from a high‐altitude Afromontane tropical rain forest site within Bwindi Impenetrable National Park (BINP), Uganda. Our primary aim was to highlight and evaluate GAMM methodology, and quantify both intra‐ and interannual changes in fruit production. First, we conduct several simulation experiments to study the practical utility of model selection and smooth term estimation relevant for disentangling intra‐ and interannual variability. These simulations indicate that estimation of nonlinearity and seasonality is generally accurately identified using asymptotic theory. Applied to the empirical data set, we found that the forest‐level fruiting variability arises from both regular seasonality and significant interannual variability, with the years 2009–2010 in particular showing a significant increase in the presence of fruits‐driven by increased productivity of most species, and a regular annual peak associated occurring at the end of one of the two dry seasons. Our analyses illustrate a statistical framework for disentangling short‐term increases/decreases in fruiting effort while pinpointing specific times in which fruiting is atypical, providing a first step for assessing the impacts of regular and irregular (e.g., climate change) abiotic covariates on fruiting phenology. Some consequences of the rich diversity of fruiting patterns observed here for the population biology of frugivores in BINP are also discussed.     

Modeling plant species distributions under future climates: how fine scale do climate projections need to be?

Recent studies suggest that species distribution models (SDMs) based on fine‐scale climate data may provide markedly different estimates of climate‐change impacts than coarse‐scale models. However, these studies disagree in their conclusions of how scale influences projected species distributions. In rugged terrain, coarse‐scale climate grids may not capture topographically controlled climate variation at the scale that constitutes microhabitat or refugia for some species. Although finer scale data are therefore considered to better reflect climatic conditions experienced by species, there have been few formal analyses of how modeled distributions differ with scale. We modeled distributions for 52 plant species endemic to the California Floristic Province of different life forms and range sizes under recent and future climate across a 2000‐fold range of spatial scales (0.008–16 km²). We produced unique current and future climate datasets by separately downscaling 4 km climate models to three finer resolutions based on 800, 270, and 90 m digital elevation models and deriving bioclimatic predictors from them. As climate‐data resolution became coarser, SDMs predicted larger habitat area with diminishing spatial congruence between fine‐ and coarse‐scale predictions. These trends were most pronounced at the coarsest resolutions and depended on climate scenario and species' range size. On average, SDMs projected onto 4 km climate data predicted 42% more stable habitat (the amount of spatial overlap between predicted current and future climatically suitable habitat) compared with 800 m data. We found only modest agreement between areas predicted to be stable by 90 m models generalized to 4 km grids compared with areas classified as stable based on 4 km models, suggesting that some climate refugia captured at finer scales may be missed using coarser scale data. These differences in projected locations of habitat change may have more serious implications than net habitat area when predictive maps form the basis of conservation decision making.     

Projecting species’ vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?

Species distribution models (SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross‐validation skill indicates extrapolative skill. We compared projected vulnerability for 29 wetland‐dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross‐validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross‐validation results were correlated with extrapolation results, the use of cross‐validation performance metrics to guide modeling choices where knowledge is limited was supported.     

Predicting top predator habitats in the Southwest Indian Ocean

Top predators need to develop optimal strategies of resources and habitats utilization in order to optimize their foraging success. At the individual scale, a predator has to maximize his intake of food while minimizing his cost of foraging to optimize his energetic gain. At the ecosystem scale, we hypothesized that foraging strategies of predators also respond to their general energetic constraints. Predators with energetically costly lifestyles may be constrained to select high quality habitats whereas more phlegmatic predators may occupy both low and high quality habitats. The objectives of this study were 1) to investigate predator responses to heterogeneity in habitat quality with reference to their energetic strategies and 2) to evaluate their responses to contemporaneous versus averaged habitat quality. We collected cetacean and seabird data from an aerial survey in the Southwest Indian Ocean, a region characterized by heterogeneous oceanographic conditions. We classified cetaceans and seabirds into energetic guilds and described their habitats using remotely sensed covariates at contemporaneous and time‐averaged resolutions and static covariates. We used generalized additive models to predict their habitats at the regional scale. Strategies of habitat utilization appeared in accordance with predators energetic constraints. Cetaceans responded to the heterogeneity in habitat quality, with higher densities predicted in more productive areas. However, the costly Delphininae appeared to be more dependent on habitat quality (showing a 1‐to‐13 ratio between the lowest and highest density sectors) than the more phlegmatic sperm and beaked whales (showing only a 1‐to‐3 ratio). For seabirds, predictions primarily reflected colony locations, although the colony effect was stronger for costly seabirds. Moreover, our results suggest that predators may respond better to persistent oceanographic features. To provide a third dimension to habitat quality, cetacean strategies of utilization of the vertical habitat could be related to the distribution of micronekton in the water column.     

Identifying the relevant spatial and temporal scales in plant species occurrence models: The case of arable weeds in landscape mosaic of crops

Species distribution models (SDMs) represent potential valuable tools to explore factors underlying species occurrence over a large range of spatial scales. However, a recurrent problem with this approach is identifying the appropriate spatial and temporal scales for modeling. This problem is reinforced in plant populations for which it is often difficult to evaluate the limits of habitat patches. In this study, we aimed at developing SDMs for 13 arable weeds in highly dynamic small agricultural region. Although weed dynamic is widely thought to result from local processes, we explored the spatial and temporal scales that would best explain species occurrence over the area. Models were developed using weed occurrence data in 58 fields over four consecutive years (2008–2011) and spatial organization of management practices over the landscape for eight consecutive years (2004–2011). We used a model selection approach based on the minimum AIC criteria to select the best SDMs. Results showed that SDMs can successfully be applied to model weed occurrence over a small region. The appropriate temporal scale to consider in weed SDMs should encompass several years to reflect the effect of management history while the relevant spatial scale should extend beyond the crop field itself and include the field border and neighboring fields. This study illustrates that adopting a multiple scale approach is successful to model plant occurrence over a highly dynamic landscape.     

Using Species Distribution Models For Fungi

Species distribution models (SDMs) are an emerging tool in the study of fungi, and their use is expanding across species and research topics. To summarise progress to date and to highlight important considerations for future users, we review 283 studies that apply SDMs to fungi. We found that macrofungi, lichens, and pathogenic microfungi are most often studied. While many studies only aim to model species response to environmental covariates, the use of SDMs for explicitly predicting fungal occurrence in space and time is growing. Many studies collect fungal occurrence data, but the use of pre-collected records from reference collections and citizen science programs is increasing. Challenges of applying SDMs to fungi include detection and sampling biases, and uncertainties in identification and taxonomy. Further, finding environmental covariates at appropriate spatial and temporal scales is important, as fungi can respond to fine-scale environmental patterns. Fine-scale covariate data can be difficult to gather across space, but we show remote-sensing measurements are viable for fungi SDMs. For those fungi interacting with host species, host information is also important, and can be used as covariates in SDMs. We also highlight that competition among fungi, and dispersal, can affect observed distributions, with the latter particularly prominent for invasive fungi. We show how one can account for these processes in models, when suitable data are available. Finally, we note that environmental DNA records create new opportunities and challenges for future modelling efforts, and discuss the difficulties in predicting invasions and climate change impacts. The application of SDMs to fungi has already provided interesting lessons on how to adapt modelling tools for specific questions, and fungi will continue to be relevant test subjects for further technical development of SDMs.     

Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

Modeling organism distributions from survey data involves numerous statistical challenges, including accounting for zero‐inflation, overdispersion, and selection and incorporation of environmental covariates. In environments with high spatial and temporal variability, addressing these challenges often requires numerous assumptions regarding organism distributions and their relationships to biophysical features. These assumptions may limit the resolution or accuracy of predictions resulting from survey‐based distribution models. We propose an iterative modeling approach that incorporates a negative binomial hurdle, followed by modeling of the relationship of organism distribution and abundance to environmental covariates using generalized additive models (GAM) and generalized additive models for location, scale, and shape (GAMLSS). Our approach accounts for key features of survey data by separating binary (presence‐absence) from count (abundance) data, separately modeling the mean and dispersion of count data, and incorporating selection of appropriate covariates and response functions from a suite of potential covariates while avoiding overfitting. We apply our modeling approach to surveys of sea duck abundance and distribution in Nantucket Sound (Massachusetts, USA), which has been proposed as a location for offshore wind energy development. Our model results highlight the importance of spatiotemporal variation in this system, as well as identifying key habitat features including distance to shore, sediment grain size, and seafloor topographic variation. Our work provides a powerful, flexible, and highly repeatable modeling framework with minimal assumptions that can be broadly applied to the modeling of survey data with high spatiotemporal variability. Applying GAMLSS models to the count portion of survey data allows us to incorporate potential overdispersion, which can dramatically affect model results in highly dynamic systems. Our approach is particularly relevant to systems in which little a priori knowledge is available regarding relationships between organism distributions and biophysical features, since it incorporates simultaneous selection of covariates and their functional relationships with organism responses.     

Fine‐grain,large‐domain climate models based on climate station and comprehensive topographic information improve microrefugia detection

Large‐domain species distribution models (SDMs) fail to identify microrefugia, as they are based on climate estimates that are either too coarse or that ignore relevant topographic climate‐forcing factors. Climate station data are considered inadequate to produce such estimates, a viewpoint we challenge here. Using climate stations and topographic data, we developed three sets of large‐domain (450 000 km²), fine‐grain (50 m) temperature grids accounting for different levels of topographic complexity. Using these fine‐grain grids and the Worldclim data, we fitted SDMs for 78 alpine species over Sweden, and assessed over‐ versus underestimations of local extinction and area of microrefugia by comparing modelled distributions at species' rear edges. Accounting for well‐known topographic climate‐forcing factors improved our ability to model fine‐scale climate, despite using only climate station data. This approach captured the effect of cool air pooling, distance to sea, and relative humidity on local‐scale temperature, but the effect of solar radiation could not be accurately accounted for. Predicted extinction rate decreased with increasing spatial resolution of the climate models and with increasing number of topographic climate‐forcing factors accounted for. About half of the microrefugia detected in the most topographically complete models were not detected in the coarser SDMs and in the models calibrated from climate variables extracted from elevation only. Although major limitations remain, climate station data can potentially be used to produce fine‐grain topoclimate grids, opening up the opportunity to model local‐scale ecological processes over large domains. Accounting for the topographic complexity encountered within landscapes permits the detection of microrefugia that would otherwise remain undetected. Topographic heterogeneity is likely to have a massive impact on species persistence, and should be included in studies on the effects of climate change.     

Seascape drivers of Macrocystis pyrifera population genetic structure in the northeast Pacific

At small spatial and temporal scales, genetic differentiation is largely controlled by constraints on gene flow, while genetic diversity across a species' distribution is shaped on longer temporal and spatial scales. We assess the hypothesis that oceanographic transport and other seascape features explain different scales of genetic structure of giant kelp, Macrocystis pyrifera. We followed a hierarchical approach to perform a microsatellite‐based analysis of genetic differentiation in Macrocystis across its distribution in the northeast Pacific. We used seascape genetic approaches to identify large‐scale biogeographic population clusters and investigate whether they could be explained by oceanographic transport and other environmental drivers. We then modelled population genetic differentiation within clusters as a function of oceanographic transport and other environmental factors. Five geographic clusters were identified: Alaska/Canada, central California, continental Santa Barbara, California Channel Islands and mainland southern California/Baja California peninsula. The strongest break occurred between central and southern California, with mainland Santa Barbara sites forming a transition zone between the two. Breaks between clusters corresponded approximately to previously identified biogeographic breaks, but were not solely explained by oceanographic transport. An isolation‐by‐environment (IBE) pattern was observed where the northern and southern Channel Islands clustered together, but not with closer mainland sites, despite the greater distance between them. The strongest environmental association with this IBE pattern was observed with light extinction coefficient, which extends suitable habitat to deeper areas. Within clusters, we found support for previous results showing that oceanographic connectivity plays an important role in the population genetic structure of Macrocystis in the Northern hemisphere.     

Dispersal limitation and spatial scale affect model based projections of Pinus uncinata response to climate change in the Pyrenees

Species Distribution Models (SDMs) were employed to assess the potential impact of climate change on the distribution of Pinus uncinata in the Pyrenees, where it is the dominant tree species in subalpine forest and alpine tree lines. Predicting forest response to climate change is a challenging task in mountain regions but also a conservation priority. We examined the potential impact of spatial scale on SDM projections by conducting all analyses at four spatial resolutions. We further examined the potential effect of dispersal constraints by applying a threshold distance of maximal advancement derived from a spatially explicit, individual‐based simulation model of tree line dynamics. Under current conditions, SDMs including climatic factors related to stress or growth limitation performed best. These models were then employed to project P. uncinata distribution under two emission scenarios, using data generated from several regional climate models. At the end of this century, P. uncinata is expected to migrate northward and upward, occupying habitat currently inhabited by alpine plant species. However, consideration of dispersal limitation and/or changing the spatial resolution of the analysis modified the assessment of climate change impact on mountain ecosystems, especially in the case of estimates of colonization and extinction at the regional scale. Our study highlights the need to improve the characterization of biological processes within SDMs, as well as to consider simultaneously different scales when assessing potential habitat loss under future climate conditions.     

From species distributions to meta‐communities

The extent that biotic interactions and dispersal influence species ranges and diversity patterns across scales remains an open question. Answering this question requires framing an analysis on the frontier between species distribution modelling (SDM), which ignores biotic interactions and dispersal limitation, and community ecology, which provides specific predictions on community and meta‐community structure and resulting diversity patterns such as species richness and functional diversity. Using both empirical and simulated datasets, we tested whether predicted occurrences from fine‐resolution SDMs provide good estimates of community structure and diversity patterns at resolutions ranging from a resolution typical of studies within reserves (250 m) to that typical of a regional biodiversity study (5 km). For both datasets, we show that the imprint of biotic interactions and dispersal limitation quickly vanishes when spatial resolution is reduced, which demonstrates the value of SDMs for tracking the imprint of community assembly processes across scales.     

Temporal scale, phytoplankton ecology and palaeolimnology

1. Scales of temporal analysis in limnology generally cover dial through to interannual changes, with occasional longer studies with up to 50 years continuous sampling data. Lakes, however, have been changing over much longer time periods than this, as is apparent from palaeolimnological studies. Temporal scales are, however, largely relative, with an individual's perspective controlling what is deemed short or long term. 2. Phytoplankton populations are variable over a variety of timescales, and the sediment record can readily record these changes from interannual through to 10³-year timescales. Because of anthropogenic influences, such as acidification and eutrophication, phytoplankton communities probably have been altered dramatically in many lakes, often before routine sampling began. Records of changing phytoplankton populations at timescales relevant to limnologists can be derived from, for example, varved sediments and used to address specific problems, such as the degree of long-term interannual variability and timescales of sexual reproduction. 3. Palaeolimnologists tend to interpret changes in sediment assemblages in terms of ecological and physiological processes which are relevant at scales that may not be resolvable in lake sediments. There is a clear need for sediment records to be interpreted in terms of the processes which operate at timescales that match the resolution of that sediment sequence. 4. Increasingly fine sampling resolutions are being attempted by palaeolimnologists, often without consideration of the reasons for such an approach or to the repeatability of the results. The increased variability associated with high-resolution sampling can make it difficult to separate noise from the ecological signal. There is a clear need for replication. 5. The necessary temporal resolution is defined by the aim of any given palaeolimnological study. If the main emphasis of a study is, for example, establishing background phosphorus concentrations, a coarser sampling resolution is probably acceptable than that required for many ‘ecological’studies.     

Nice weather for bettongs: using weather events,not climate means,in species distribution models

Current applications of species distribution models (SDM) are typically static, in that they are based on correlations between where a species has been observed (ignoring the date of the observation) and environmental features, such as long‐term climate means, that are assumed to be constant for each site. Because of this SDMs do not account for temporal variation in the distribution of suitable habitat across the range of a species. Here, we demonstrate the temporal variability in the potential geographic distributions of an endangered marsupial, the northern bettong Bettongia tropica as a case study. Models of the species distribution using temporally matched observations of the species with weather data (including extreme weather events) at the time of species observations, were better able to define habitat suitability, identify range edges and uncover competitive interactions than models based on static long‐term climate means. Droughts and variable temperature are implicated in low densities and local extinctions of northern bettong populations close to range edges. Further, we show how variable weather can influence the results of competition with the common rufous bettong Aepyprymnus rufescens. Because traditional SDMs do not account for temporal variability of suitable habitat, static SDMs may underestimate the impacts of climate change particularly as the incidence of extreme weather events is likely to rise.     

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long‐term stable habitats. The variability of complex, short‐term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs’ usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.     

Improving species distribution models for climate change studies: variable selection and scale

Statistical species distribution models (SDMs) are widely used to predict the potential changes in species distributions under climate change scenarios. We suggest that we need to revisit the conceptual framework and ecological assumptions on which the relationship between species distributions and environment is based. We present a simple conceptual framework to examine the selection of environmental predictors and data resolution scales. These vary widely in recent papers, with light inconsistently included in the models. Focusing on light as a necessary component of plant SDMs, we briefly review its dependence on aspect and slope and existing knowledge of its influence on plant distribution. Differences in light regimes between north‐ and south‐facing aspects in temperate latitudes can produce differences in temperature equivalent to moves 200 km polewards. Local topography may create refugia that are not recognized in many climate change SDMs using coarse‐scale data. We argue that current assumptions about the selection of predictors and data resolution need further testing. Application of these ideas can clarify many issues of scale, extent and choice of predictors, and potentially improve the use of SDMs for climate change modelling of biodiversity.     

Climate and demography of the planktivorous Cassin's auklet Ptychoramphus aleuticus off northern California: implications for population change

1. We performed demographic analyses on Cassin's auklet Ptychoramphus aleuticus, a zooplanktivorous seabird inhabiting the variable California Current System, to understand how temporal environmental variability influences population dynamics. 2. We used capture-recapture data from 1986 to 2002 to rank models of interannual variation in survival, breeding propensity, breeding success, and recruitment. 3. All demographic parameters exhibited temporal variability. Interannual variation in survival was best modelled as a nonlinear function of the winter Southern Oscillation Index (SOI). Breeding propensity was best modelled as a threshold function of local sea surface temperature. Breeding success and recruitment were best modelled with year-dependent annual variation. 4. Changes in the SOI force El Ni?o/La Ni?a events, which in turn alter prey availability to seabirds in this system. Demographic responses varied during El Ni?os/La Ni?as. Survival diminished substantially during the 1997-98 El Ni?o event, while breeding propensity was affected during both the 1992 and 1998 El Ni?os. Breeding success was reduced during the 1992, 1993, and 1998 El Ni?os, but was unusually high in 2002. Recruitment was higher at the beginning and end of this time-series. 5. While demographic responses varied interannually, parameter values covaried in a positive fashion, a situation conducive to rapid population change. During the 11 years study period, the Farallon auklet breeding population declined at 6.05 +/- 0.80% (SE) per year, a cumulative decline of 49.7%. This study demonstrates how climate variability has influenced key demographic processes for this diminished marine bird population.     

One size does not fit all: flexible models are required to understand animal movement across scales

1. Large data sets containing precise movement data from free-roaming animals are now becoming commonplace. One means of analysing individual movement data is through discrete, random walk-based models. 2. Random walk models are easily modified to incorporate common features of animal movement, and the ways that these modifications affect the scaling of net displacement are well studied. Recently, ecologists have begun to explore more complex statistical models with multiple latent states, each of which are characterized by a distribution of step lengths and have their own unimodal distribution of turning angles centred on one type of turn (e.g. reversals). 3. Here, we introduce the compound wrapped Cauchy distribution, which allows for multimodal distributions of turning angles within a single state. When used as a single state model, the parameters provide a straightforward summary of the relative contributions of different turn types. The compound wrapped Cauchy distribution can also be used to build multiple state models. 4. We hypothesize that a multiple state model with unimodal distributions of turning angles will best describe movement at finer resolutions, while a multiple state model using our multimodal distribution will better describe movement at intermediate temporal resolutions. At coarser temporal resolutions, a single state model using our multimodal distribution should be sufficient. We parameterize and compare the performance of these models at four different temporal resolutions (1, 4, 12 and 24 h) using data from eight individuals of Loxodonta cyclotis and find support for our hypotheses. 5. We assess the efficacy of the different models in extrapolating to coarser temporal resolution by comparing properties of data simulated from the different models to the properties of the observed data. At coarser resolutions, simulated data sets recreate many aspects of the observed data; however, only one of the models accurately predicts step length, and all models underestimate the frequency of reversals. 6. The single state model we introduce may be adequate to describe movement data at many resolutions and can be interpreted easily. Multiscalar analyses of movement such as the ones presented here are a useful means of identifying inconsistencies in our understanding of movement.     

The Goldilocks effect: intermittent streams sustain more plant species than those with perennial or ephemeral flow

1. Drylands worldwide are typified by extreme variability in hydrologic processes, which structures riparian communities at various temporal and spatial scales. One key question is how underlying differences in hydrology over the length of interrupted perennial rivers influence spatial and temporal patterns in species richness and species composition. 2. We examined effects of differences in dry season hydrology on species richness, composition and cover of herbaceous plant communities in the streamside zone (the zone influenced directly by low flows in the channel). Data were collected at ephemeral, intermittent and perennial flow reaches on three rivers of the desert Southwest (Arizona, U.S.A.): Lower Cienega Creek, Hassayampa River and Lower San Pedro River. 3. Patterns of species richness varied with temporal scale of analysis, that is between single‐year and multi‐year time frames. At the annual timescale, quadrat species richness (m^?2) and herbaceous cover were higher at sites with perennial flow than at either intermittent or ephemeral sites. In contrast to this single‐year pattern, the highest long‐term richness occurred at intermittent sites. 4. Quadrat species richness, total species richness at a site (per 18 1‐m² plots) and cover were more variable year to year at non‐perennial sites than at perennial flow sites. On two of the three rivers, ephemeral sites had the highest inter‐annual compositional variance, while the perennial sites had the lowest. 5. Compositional differences between the hydrologic site types were dominated by species turnover, not nestedness. The perennial sites had more wetland and perennial species than the other two site types. The intermittent sites had more annual species than did the other two types. 6. High long‐term species richness and distinct species composition of intermittent sites are probably sustained by pronounced temporal variability in environmental conditions (i.e. frequent and persistent flow events, and dry periods). Plants at these sites take advantage of greater moisture than those at ephemeral sites and also experience less competition from resident species than those at perennial sites. 7. Conservation of desert riparian diversity depends upon the protection of consistently wet conditions at perennial flow sites, as well as the maintenance of the processes that cause fluctuations in environmental conditions at non‐perennial sites.     

PaleoView: a tool for generating continuous climate projections spanning the last 21 000 years at regional and global scales

It has been difficult to access projections of global‐scale climate change with high temporal resolution spaning the late Pleistocene and Holocene. This has limited our ability to discern how climate fluctuations have affected species’ range dynamics and extinction processes, turn‐over in ecological communities and changes in genetic diversity. PaleoView is a new freeware tool, which provides a comprehensive but easy‐to‐use way to generate and view paleoclimate data at temporal and spatial resolutions suitable for detecting biotic responses to major climate shifts since the last glacial maximum. Regional to global scale simulations of temperature, precipitation, humidity and mean sea level pressure can be generated from PaleoView as gridded or time series data at time intervals as short as a decade for any period during the last 21 000 yr. They can be viewed using a built‐in geographical user interface or saved as data files. Modelled climate reconstructions are based on daily simulation output from the Community Climate System Model ver. 3 (CCSM3). This global coupled atmosphere–ocean–sea ice–land general circulation model accurately reproduces major climatic features associated with the most recent deglaciation event, and predicts present‐day patterns of climate conditions with verified hindcast skill. By providing a portal for readily accessing climate reconstructions at high temporal resolutions, PaleoView can help to better establish the consequences of past climate fluctuations on macro‐ecological patterns of biological and genetic diversity.