Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models

Understanding and predicting a species’ distribution across a landscape is of central importance in ecology, biogeography and conservation biology. However, it presents daunting challenges when populations are highly dynamic (i.e. increasing or decreasing their ranges), particularly for small populations where information about ecology and life history traits is lacking. Currently, many modelling approaches fail to distinguish whether a site is unoccupied because the available habitat is unsuitable or because a species expanding its range has not arrived at the site yet. As a result, habitat that is indeed suitable may appear unsuitable. To overcome some of these limitations, we use a statistical modelling approach based on spatio‐temporal log‐Gaussian Cox processes. These model the spatial distribution of the species across available habitat and how this distribution changes over time, relative to covariates. In addition, the model explicitly accounts for spatio‐temporal dynamics that are unaccounted for by covariates through a spatio‐temporal stochastic process. We illustrate the approach by predicting the distribution of a recently established population of Eurasian cranes Grus grus in England, UK, and estimate the effect of a reintroduction in the range expansion of the population. Our models show that wetland extent and perimeter‐to‐area ratio have a positive and negative effect, respectively, in crane colonisation probability. Moreover, we find that cranes are more likely to colonise areas near already occupied wetlands and that the colonisation process is progressing at a low rate. Finally, the reintroduction of cranes in SW England can be considered a human‐assisted long‐distance dispersal event that has increased the dispersal potential of the species along a longitudinal axis in S England. Spatio‐temporal log‐Gaussian Cox process models offer an excellent opportunity for the study of species where information on life history traits is lacking, since these are represented through the spatio‐temporal dynamics reflected in the model.     

Terrestrial biosphere model performance for inter‐annual variability of land‐atmosphere CO2 exchange

Interannual variability in biosphere‐atmosphere exchange of CO₂ is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the ‘acid test’ for terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter‐annual variability in observations. Herein, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long‐term measurements of biosphere‐atmosphere CO₂ exchange made with eddy‐covariance flux towers at 11 forested sites in North America. Instead of focusing on model‐data agreement we take a systematic, variability‐oriented approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study.     

Range expansion through fragmented landscapes under a variable climate

Ecological responses to climate change may depend on complex patterns of variability in weather and local microclimate that overlay global increases in mean temperature. Here, we show that high‐resolution temporal and spatial variability in temperature drives the dynamics of range expansion for an exemplar species, the butterfly Hesperia comma. Using fine‐resolution (5 m) models of vegetation surface microclimate, we estimate the thermal suitability of 906 habitat patches at the species' range margin for 27 years. Population and metapopulation models that incorporate this dynamic microclimate surface improve predictions of observed annual changes to population density and patch occupancy dynamics during the species' range expansion from 1982 to 2009. Our findings reveal how fine‐scale, short‐term environmental variability drives rates and patterns of range expansion through spatially localised, intermittent episodes of expansion and contraction. Incorporating dynamic microclimates can thus improve models of species range shifts at spatial and temporal scales relevant to conservation interventions.     

Multi-scale analysis of responses of stream macrobenthos to forestry activities and environmental context

1. Forestry activities can greatly modify the structure and function of invertebrate communities in streams, but the ability to detect effects of forestry may depend on the spatial scale considered, the choice of response metric and the environmental context. In this study, a multi‐scale, multi‐metric approach was used to compare the usefulness of proximate and larger‐scale measurements of forestry activity for understanding the impacts of forestry on stream macrobenthos. 2. Site‐specific responses of macrobenthic communities to forestry activities measured at four spatial scales (sub‐basin and 8‐, 2‐ and 0.5‐km radii upstream of study sites) were examined for 90 riffle sites distributed among 22 tributary streams (Strahler order 1–5) of the Cascapedia River basin, Quebec, Canada. 3. Multiple regression models and canonical correspondence analysis were used to relate six biological metrics (taxonomic richness, numerical density, biomass density, normalised biomass spectrum, individual body mass and community structure) to variables quantifying logging 1–19 years prior to the study and road density. Environmental predictors (variables quantifying local habitat or landscape features) were included in all analyses to statistically account for environmental context and increase the likelihood of detecting potentially subtle forestry impacts. 4. Forestry activities measured at the larger (sub‐basin and 8 km) scales were linked to decline in taxonomic richness, increase in numerical and biomass densities and shift in size structure of benthic macroinvertebrates, indicating that analyses encompassing larger areas, up to the full basin, may allow for more sensitive detection of effects than those of more limited span. 5. These responses primarily reflected marked increases in the abundance of chironomids and decline in the number of trichopteran taxa with increasing areal coverage of recent (≤2–4 years) cuts, suggesting that larger, longer‐lived and possibly more specialised taxa were more vulnerable to forestry impacts than smaller, multivoltine, generalist invertebrates. After partialling out the influence of other variables, rapid decline in richness occurred even when <1% of the basin had been clear cut in the year prior to the study. 6. Effects of forestry were detected after statistically accounting for natural environmental variability, which may have otherwise concealed those effects. The combined use of multiple biological metrics, partialling out of environmental effects and measurement of impacts at multiple spatial scales may be a broadly applicable approach for enhancing sensitivity and facilitating interpretation in studies of anthropogenic effects on macroinvertebrate communities.     

Climate change,genetic markers and species distribution modelling

Ecologists and biogeographers are currently expending great effort forecasting shifts in species geographical ranges that may result from climate change. However, these efforts are problematic because they have mostly relied on presence‐only data that ignore within‐species genetic diversity. Technological advances in high‐throughput sequencing have now made it cost‐effective to survey the genetic structure of populations sampled throughout the range of a species. These data can be used to delineate two or more genetic clusters within the species range, and to identify admixtures of individuals within genetic clusters that reflect different patterns of ancestry. Species distribution models (SDMs) applied to the presence and absence of genetic clusters should provide more realistic forecasts of geographical range shifts that take account of genetic variability. High‐throughput sequencing and spatially explicit models may be used to further refine these projections.     

Uncertainty in predicting range dynamics of endemic alpine plants under climate warming

Correlative species distribution models have long been the predominant approach to predict species’ range responses to climate change. Recently, the use of dynamic models is increasingly advocated for because these models better represent the main processes involved in range shifts and also simulate transient dynamics. A well‐known problem with the application of these models is the lack of data for estimating necessary parameters of demographic and dispersal processes. However, what has been hardly considered so far is the fact that simulating transient dynamics potentially implies additional uncertainty arising from our ignorance of short‐term climate variability in future climatic trends. Here, we use endemic mountain plants of Austria as a case study to assess how the integration of decadal variability in future climate affects outcomes of dynamic range models as compared to projected long‐term trends and uncertainty in demographic and dispersal parameters. We do so by contrasting simulations of a so‐called hybrid model run under fluctuating climatic conditions with those based on a linear interpolation of climatic conditions between current values and those predicted for the end of the 21st century. We find that accounting for short‐term climate variability modifies model results nearly as differences in projected long‐term trends and much more than uncertainty in demographic/dispersal parameters. In particular, range loss and extinction rates are much higher when simulations are run under fluctuating conditions. These results highlight the importance of considering the appropriate temporal resolution when parameterizing and applying range‐dynamic models, and hybrid models in particular. In case of our endemic mountain plants, we hypothesize that smoothed linear time series deliver more reliable results because these long‐lived species are primarily responsive to long‐term climate averages.     

Analyzing wearable device data using marked point processes

This paper introduces two sets of measures as exploratory tools to study physical activity patterns: active‐to‐sedentary/sedentary‐to‐active rate function (ASRF/SARF) and active/sedentary rate function (ARF/SRF). These two sets of measures are complementary to each other and can be effectively used together to understand physical activity patterns. The specific features are illustrated by an analysis of wearable device data from National Health and Nutrition Examination Survey (NHANES). A two‐level semiparametric regression model for ARF and the associated activity magnitude is developed under a unified framework using the marked point process formulation. The inactive and active states measured by accelerometers are treated as a 0‐1 point process, and the activity magnitude measured at each active state is defined as a marked variable. The commonly encountered missing data problem due to device nonwear is referred to as “window censoring,” which is handled by a proper estimation approach that adopts techniques from recurrent event data. Large sample properties of the estimator and comparison between two regression models as measurement frequency increases are studied. Simulation and NHANES data analysis results are presented. The statistical inference and analysis results suggest that ASRF/SARF and ARF/SRF provide useful analytical tools to practitioners for future research on wearable device data.     

Native and naturalized range size in Pinus: relative importance of biogeography,introduction effort and species traits

Aim Pine trees (genus Pinus) represent an ancient lineage, naturally occurring almost exclusively in the Northern Hemisphere, but introduced and widely naturalized in both hemispheres. As large trees of interest to forestry, they attract much attention and their distribution is well documented in both indigenous and naturalized ranges. This creates an opportunity to analyse the relationship between indigenous and naturalized range sizes in the context of different levels of human usage, biological traits and the characteristics of the environments of origin. Location Global. Methods We combined and expanded pre‐existing data sets for pine species distributions and pine species traits, and used a variety of regression techniques (including generalized additive models and zero‐inflated Poisson models) to assess which variables explained naturalized and indigenous range sizes. Results Indigenous and naturalized range sizes are positively correlated but there are many notable exceptions. Some species have large indigenous ranges but small or no naturalized ranges, whereas others have small indigenous ranges, but have naturalized in many regions. Indigenous range is correlated to factors such as seed size (?), age at first reproduction (?), and latitude (+, supporting Rapoport's rule), but also to the extent of coverage of species in the forestry literature (+). Naturalized range size is strongly influenced by the extent of coverage of species in the forestry literature (+), a proxy for propagule pressure. Naturalization was also influenced by average elevation in the indigenous range (?) and age at first reproduction (?). Main conclusions The macroecological and evolutionary pressures facing plant groups are not directly transferable between indigenous and naturalized ranges. In particular, there are strong biases in species naturalization and expansion in invasive ranges that are unrelated to factors determining indigenous range size. At least for Pinus, a new set of macroecological patterns are emerging which are profoundly influenced by humans.     

Predicting the Geographic Distribution of a Species from Presence‐Only Data Subject to Detection Errors

Summary Several models have been developed to predict the geographic distribution of a species by combining measurements of covariates of occurrence at locations where the species is known to be present with measurements of the same covariates at other locations where species occurrence status (presence or absence) is unknown. In the absence of species detection errors, spatial point‐process models and binary‐regression models for case‐augmented surveys provide consistent estimators of a species’ geographic distribution without prior knowledge of species prevalence. In addition, these regression models can be modified to produce estimators of species abundance that are asymptotically equivalent to those of the spatial point‐process models. However, if species presence locations are subject to detection errors, neither class of models provides a consistent estimator of covariate effects unless the covariates of species abundance are distinct and independently distributed from the covariates of species detection probability. These analytical results are illustrated using simulation studies of data sets that contain a wide range of presence‐only sample sizes. Analyses of presence‐only data of three avian species observed in a survey of landbirds in western Montana and northern Idaho are compared with site‐occupancy analyses of detections and nondetections of these species.     

Latitudinal gradients in butterfly population variability are influenced by landscape heterogeneity

The variability of populations over time is positively associated with their risk of local extinction. Previous work has shown that populations at the high‐latitude boundary of species’ ranges show higher inter‐annual variability, consistent with increased sensitivity and exposure to adverse climatic conditions. However, patterns of population variability at both high‐ and low‐latitude species range boundaries have not yet been concurrently examined. Here, we assess the inter‐annual population variability of 28 butterfly species between 1994 and 2009 at 351 and 18 sites in the United Kingdom and Catalonia, Spain, respectively. Local population variability is examined with respect to the position of the species’ bioclimatic envelopes (i.e. whether the population falls within areas of the ‘core’ climatic suitability or is a climatically ‘marginal’ population), and in relation to local landscape heterogeneity, which may influence these range location – population dynamic relationships. We found that butterfly species consistently show latitudinal gradients in population variability, with increased variability in the more northerly UK. This pattern is even more marked for southerly distributed species with ‘marginal’ climatic suitability in the UK but ‘core’ climatic suitability in Catalonia. In addition, local landscape heterogeneity did influence these range location – population dynamic relationships. Habitat heterogeneity was associated with dampened population dynamics, especially for populations in the UK. Our results suggest that promoting habitat heterogeneity may promote the persistence of populations at high‐latitude range boundaries, which may potentially aid northwards expansion under climate warming. We did not find evidence that population variability increases towards southern range boundaries. Sample sizes for this region were low, but there was tentative evidence, in line with previous ecological theory, that local landscape heterogeneity may promote persistence in these retracting low‐latitude range boundary populations.     

Scientific data and theories for salmonellosis dose–response assessment

An “expansive” risk assessment approach is illustrated, characterizing dose–response relationships for salmonellosis in light of the full body of evidence for human and murine superorganisms. Risk assessments often require analysis of costs and benefits for supporting public health decisions. Decision-makers and the public need to understand uncertainty in such analyses for two reasons. Uncertainty analyses provide a range of possibilities within a framework of present scientific knowledge, thus helping to avoid undesirable consequences associated with the selected policies. And, it encourages the risk assessors to scrutinize all available data and models, thus helping avoid subjective or systematic errors. Without the full analysis of uncertainty, decisions could be biased by judgments based solely on default assumptions, beliefs, and statistical analyses of selected correlative data. Alternative data and theories that incorporate variability and heterogeneity for the human and murine superorganisms, particularly colonization resistance, are emerging as major influences for microbial risk assessment. Salmonellosis risk assessments are often based on conservative default models derived from selected sets of outbreak data that overestimate illness. Consequently, the full extent of uncertainty of estimates of annual number of illnesses is not incorporated in risk assessments and the presently used models may be incorrect.     

Experimental Design and Statistical Inference for Cluster Point Processes – with Applications to the Fruit Dispersion of Anemochorous Forest Trees

This paper deals with experimental design and statistical inference for cluster point processes. The results are applied to fruit dispersion models of forest trees where the corresponding design of experiments is given by the positions of the traps containing the collected fruits. It is shown that consideration of anisotropic behaviour can lead to more realistic models. Modelling interactivity effects between trees seems to be of great interest. It is shown that an approach based on ordered weighted averages yields an notable improvement of model quality. The mathematical background of such models (Choquet integral, fuzzy measures) is sketched in the appendix. Finally, results for choosing a D‐optimal sub‐design are presented.     

Impact Assessment of Biodiversity and Carbon Pools from Land Use and Land Use Changes in Life Cycle Assessment,Exemplified with Forestry Operations in Norway

There is a strong need for methods within life cycle assessment (LCA) that enable the inclusion of all complex aspects related to land use and land use change (LULUC). This article presents a case study of the use of one hectare (ha) of forest managed for the production of wood for bioenergy production. Both permanent and temporary changes in above‐ground biomass are assessed together with the impact on biodiversity caused by LULUC as a result of forestry activities. The impact is measured as a product of time and area requirements, as well as by changes in carbon pools and impacts on biodiversity as a consequence of different management options. To elaborate the usefulness of the method as well as its dependency on assumptions, a range of scenarios are introduced in the study. The results show that the impact on climate change from LULUC dominates the results, compared to the impact from forestry operations. This clearly demonstrates the need to include LULUC in an LCA of forestry products. For impacts both on climate change and biodiversity, the results show large variability based on what assumptions are made; and impacts can be either positive or negative. Consequently, a mere measure of land used does not provide any meaning in LCA, as it is not possible to know whether this contributes a positive or negative impact.     

Selection for commercial forestry determines global patterns of alien conifer invasions

Question Are the patterns of alien conifer (Pinaceae, Cupressaceae) invasions different between continents, and how is invasion success influenced by commercial forestry practices? Location Temperate and subtropical countries and regions (n = 60) from five continents spanning both hemispheres. Methods We used generalized linear mixed models to test how continent identity, region area and use in commercial forestry affect probabilities of Pinaceae and Cupressaceae species to escape following introduction and cumulative logit regression models to assess how these predictors affect the likelihood that a species becomes naturalized or invasive. Results Sixty Pinaceae of a global total of 232 and 26 Cupressaceae of a total of 142 species have escaped from cultivation across the study regions examined. Average numbers of both alien Pinaceae and Cupressaceae species per region were highest in Oceania, followed by Africa. Moreover, the probability of alien Cupressaceae and Pinaceae becoming naturalized or invasive was particularly high in these two continents. For both families, species used in commercial forestry have a significantly higher probability of escape than those which are only introduced for ornamental or other purposes. In the case of Pinaceae, forestry species also become naturalized or invasive more frequently than non‐forestry species, while no such effect was detectable for Cupressaceae. Conclusions We found that non‐native conifers are more likely to escape from cultivation, naturalize and turn into invasive weeds on the continents of the Southern Hemisphere. In addition to this biogeographic signal, introduction effort strongly determines the behaviour of introduced Pinaceae, and less so, Cupressaceae. A clear conflict exists between the economic benefits of conifer forestry and the risks to the environment from invasions. Future expansion of commercial forestry should address spatial planning to ecosystems vulnerable to invasion and adopt comprehensive risk assessment procedures.     

Joint species distribution modelling for spatio‐temporal occurrence and ordinal abundance data

Aim

Species distribution models are important tools used to study the distribution and abundance of organisms relative to abiotic variables. Dynamic local interactions among species in a community can affect abundance. The abundance of a single species may not be at equilibrium with the environment for spreading invasive species and species that are range shifting because of climate change. Innovation : We develop methods for incorporating temporal processes into a spatial joint species distribution model for presence/absence and ordinal abundance data. We model non‐equilibrium conditions via a temporal random effect and temporal dynamics with a vector‐autoregressive process allowing for intra‐ and interspecific dependence between co‐occurring species. The autoregressive term captures how the abundance of each species can enhance or inhibit its own subsequent abundance or the subsequent abundance of other species in the community and is well suited for a ‘community modules’ approach of strongly interacting species within a food web. R code is provided for fitting multispecies models within a Bayesian framework for ordinal data with any number of locations, time points, covariates and ordinal categories.

Main conclusions

We model ordinal abundance data of two invasive insects (hemlock woolly adelgid and elongate hemlock scale) that share a host tree and were undergoing northwards range expansion in the eastern U.S.A. during the period 1997–2011. Accounting for range expansion and high inter‐annual variability in abundance led to improved estimation of the species–environment relationships. We would have erroneously concluded that winter temperatures did not affect scale abundance had we not accounted for the range expansion of scale. The autoregressive component revealed weak evidence for commensalism, in which adelgid may have predisposed hemlock stands for subsequent infestation by scale. Residual spatial dependence indicated that an unmeasured variable additionally affected scale abundance. Our robust modelling approach could provide similar insights for other community modules of co‐occurring species.     

Combining field experiments and predictive models to assess potential for increased plant diversity to climate‐proof intensive agriculture

Agricultural production systems face increasing threats from more frequent and extreme weather fluctuations associated with global climate change. While there is mounting evidence that increased plant community diversity can reduce the variability of ecosystem functions (such as primary productivity) in the face of environmental fluctuation, there has been little work testing whether this is true for intensively managed agricultural systems. Using statistical modeling techniques to fit environment–productivity relationships offers an efficient means of leveraging hard‐won experimental data to compare the potential variability of different mixtures across a wide range of environmental contexts. We used data from two multiyear field experiments to fit climate–soil–productivity models for two pasture mixtures under intensive grazing—one composed of two drought‐sensitive species (standard), and an eight‐species mixture including several drought‐resistant species (complex). We then used these models to undertake a scoping study estimating the mean and coefficient of variation (CV) of annual productivity for long‐term climate data covering all New Zealand on soils with low, medium, or high water‐holding capacity. Our results suggest that the complex mixture is likely to have consistently lower CV in productivity, irrespective of soil type or climate regime. Predicted differences in mean annual productivity between mixtures were strongly influenced by soil type and were closely linked to mean annual soil water availability across all soil types. Differences in the CV of productivity were only strongly related to interannual variance in water availability for the lowest water‐holding capacity soil. Our results show that there is considerable scope for mixtures including drought‐tolerant species to enhance certainty in intensive pastoral systems. This provides justification for investing resources in a large‐scale distributed experiment involving many sites under different environmental contexts to confirm these findings.     

An empirical comparison of models for the phenology of bird migration

Bird migration phenology shows strong responses to climate change. Studies of trends and patterns in phenology are typically based on annual summarizing metrics, such as means and quantiles calculated from raw daily count data. However, with irregularly sampled data and large day‐to‐day variation, such metrics can be biased and noisy, and may be analysed using phenological functions fitted to the data. Here we use count data of migration passage from a Finnish bird observatory to compare different models for the phenological distributions of spring migration (27 species) and autumn migration (57 species). We assess parsimony and goodness‐of‐fit in a set of models, with phenological functions of different complexity, optionally with covariates accounting for day‐to‐day variability. The covariates describe migration intensities of related species or relative migration intensities the previous day (autocovariates). We found that parametric models are often preferred over the more flexible generalized additive models with constrained degrees of freedom. Models corresponding to a mixture of two distinct passing populations were frequently preferred over simpler ones, but usually no more complex models are needed. Slightly more complex models were favoured in spring compared to autumn. Related species’ migration activity effectively improves the model by accounting for the large day‐to‐day variation. Autocovariates were usually not that relevant, implying that autocorrelation is generally not a major concern if phenology is modelled properly. We suggest that parametric models are relatively good for studying single‐population migration phenology, or a mix of two groups with distinct phenologies, especially if daily variation in migration intensity can be controlled for. Generalized additive models may be useful when the migrating population composition is unknown. Despite these guidelines, choosing an appropriate model involves case‐by‐case assessment or the biological relevance and rationale for modelling phenology.     

Geographic range estimates and environmental requirements for the harpy eagle derived from spatial models of current and past distribution

Understanding species–environment relationships is key to defining the spatial structure of species distributions and develop effective conservation plans. However, for many species, this baseline information does not exist. With reliable presence data, spatial models that predict geographic ranges and identify environmental processes regulating distribution are a cost‐effective and rapid method to achieve this. Yet these spatial models are lacking for many rare and threatened species, particularly in tropical regions. The harpy eagle (Harpia harpyja) is a Neotropical forest raptor of conservation concern with a continental distribution across lowland tropical forests in Central and South America. Currently, the harpy eagle faces threats from habitat loss and persecution and is categorized as Near‐Threatened by the International Union for the Conservation of Nature (IUCN). Within a point process modeling (PPM) framework, we use presence‐only occurrences with climatic and topographical predictors to estimate current and past distributions and define environmental requirements using Ecological Niche Factor Analysis. The current PPM prediction had high calibration accuracy (Continuous Boyce Index = 0.838) and was robust to null expectations (pROC ratio = 1.407). Three predictors contributed 96% to the PPM prediction, with Climatic Moisture Index the most important (72.1%), followed by minimum temperature of the warmest month (15.6%) and Terrain Roughness Index (8.3%). Assessing distribution in environmental space confirmed the same predictors explaining distribution, along with precipitation in the wettest month. Our reclassified binary model estimated a current range size 11% smaller than the current IUCN range polygon. Paleoclimatic projections combined with the current model predicted stable climatic refugia in the central Amazon, Guyana, eastern Colombia, and Panama. We propose a data‐driven geographic range to complement the current IUCN range estimate and that despite its continental distribution, this tropical forest raptor is highly specialized to specific environmental requirements.     

Chemically controlled aggregation of pluripotent stem cells

Heterogeneity in pluripotent stem cell (PSC) aggregation leads to variability in mass transfer and signaling gradients between aggregates, which results in heterogeneous differentiation and therefore variability in product quality and yield. We have characterized a chemical‐based method to control aggregate size within a specific, tunable range with low heterogeneity, thereby reducing process variability in PSC expansion. This method enables controlled, scalable, stirred suspension‐based manufacturing of PSC cultures that are critical for the translation of regenerative medicine strategies to clinical products.