John Silander

Regarding Jennifer's criticism of the \independence" assumption in expression (2) of the paper, we agree that this is surely not true though it may be roughly true. However, the \correlation" calculation in (2) is a bit more complicated than it initially appears in that the calculation is with respect to a uniform distribution over say, the locations in unit i.

Supplementary Data and R Markdown workflows for Slingsby et al. "Intensifying post-fire weather and biological invasion drive species loss in a Mediterranean-type biodiversity hotspot"<br>All data and repeatable R code workflows (R Markdown) used in the study are available here or as SI Datasets. Code and data are provided under the MIT license, but where possible we would appreciate users acknowledging "the South African Environmental Observation Network (SAEON) and partners" and citing this paper and/or the original data source outlined in the paper. We would also see it as a courtesy to inform the lead author of your intended use of the data. Co-authorship is not a prerequisite, but we would like to minimize duplication of effort and/or warn users where their plans for the data do not seem appropriate.

Forecasting ecological responses to climate change, invasion, and their interaction must rely on understanding underlying mechanisms. However, such forecasts require extrapolation into new locations and environments. We linked demography and environment using experimental biogeography to forecast invasive and native species’ potential ranges under present and future climate in New England, United States to overcome issues of extrapolation in novel environments. We studied two potentially nonequilibrium invasive plants’ distributions, Alliaria petiolata (garlic mustard) and Berberis thunbergii (Japanese barberry), each paired with their native ecological analogs to better understand demographic drivers of invasions. Our models predict that climate change will considerably reduce establishment of a currently prolific invader (A. petiolata) throughout New England driven by poor demographic performance in warmer climates. In contrast, invasion of B. thunbergii will be facilitated becaus...

Prolonged periods of extreme heat or drought in the first year after fire affect the resilience and diversity of fire-dependent ecosystems by inhibiting seed germination or increasing mortality of seedlings and resprouting individuals. This interaction between weather and fire is of growing concern as climate changes, particularly in systems subject to stand-replacing crown fires, such as most Mediterranean-type ecosystems. We examined the longest running set of permanent vegetation plots in the Fynbos of South Africa (44 y), finding a significant decline in the diversity of plots driven by increasingly severe postfire summer weather events (number of consecutive days with high temperatures and no rain) and legacy effects of historical woody alien plant densities 30 y after clearing. Species that resprout after fire and/or have graminoid or herb growth forms were particularly affected by postfire weather, whereas all species were sensitive to invasive plants. Observed differences in...

ABSTRACT Background/Question/Methods Maxent is one of the most popular species distribution modeling methods, with over 400 published applications in the just the last six years. Maxent users are confronted with a wide variety of options when fitting their models, from the multiple options and settings available in the software to which input datasets to choose. However, the default settings are often chosen as a consequence of unfamiliarity with maximum entropy models, even though alternatives may often be more appropriate when connected to specific ecological questions. To explore Maxent’s assumptions, we demonstrate the variability in model output that can result from altering model settings and offer suggestions for choosing these settings. Results/Conclusions Maxent models are capable of predicting the relative probability of occurrence, but not the absolute probability of occurrence. Considering this, and the quality of opportunistically collected occurrence data and coarse resolution, remotely sensed environmental data, predictions must be interpreted cautiously when creating range maps and using them to answer complex ecological or evolutionary questions. In many cases, Maxent is best suited for hypothesis generation and asking better questions, not answering them. To date, variable selection methods explored for Maxent derive almost exclusively from machine learning perspectives, which focus more on complex pattern recognition than on producing easily interpreted models. We outline a more general approach, based on constructing simpler models motivated by specific ecological questions. In Maxent, different prior assumptions (null models) are reflected by methods of background sample selection and accounting for sampling bias. We relate these subtle cases of prior specification to the more general case, where ‘ecological’ priors can be used to incorporate different ecological assumptions or output from other models. Ecological priors are used to improve predictions of models for invasive ranges using native range data and models accounting for dispersal limitation. When ecological priors are applied to understand the spread Celastrus orbiculatus, an invasive liana, across New England (USA) over the last century, we find that greater spread is predicted in northern New England than models using default settings.

ABSTRACT First flowering events in cherry trees are believed to be closely related to temperature patterns during the winter and spring months. Earlier works have incorporated the idea of temperature thresholds, defining chill and heat functions based on these thresholds. However, selection of the thresholds is often arbitrary and shared across species and locations. We propose a survival model with spatially and temporally varying covariates having functional forms representing chill and heat accumulation leading up to first flowering events. Thresholds are chosen utlizing the ranked probability scores, selecting the threshold pair that minimizes the difference between the predicted and observed cumulative probability curves. We first apply the model using temporally varying covariates to analyze 29 years of flowering data for four cherry species (Cerasus spp.) grown in Hachioji, Japan. This allows us to investigate whether relationship with temperature may vary between earlier and later flowering species. Next, the model is applied to 52 years of flowering data for 45 Cerasus spachiana × C. speciosa trees grown across Japan's Honshu Island using spatially and temporally varying covariates and spatial random effects. By exploring flowering dates across locations, we can explore how the relationship between temperature and first flowering events varies through space. Copyright © 2013 John Wiley & Sons, Ltd.

. To take advantage of the high spectral resolution of Landsat TM images and the high spatial resolution of SPOT panchromatic images (SPOT PAN), we present a wavelet transform method to merge the two data types. In a pyramidal fashion, each TM reflective band or SPOT ...

Phenotypic plasticity may be an important contributor to the success of many invasive plant species. Shifts in genotype induced by the recipient environment can also lead to a range of phenotypic expression not seen in the native range. Selection for these novel genotypes could lead to local adaptation in the introduced environment. To investigate plasticity and local adaptation in an invaded region, we established three reciprocal transplant gardens using clonal replicates from two common invasive species in New England, Berberis ...

ABSTRACT Background/Question/Methods As part of a multi-scale project to predict invasive plant spread in response to climate and land-use change, we used experimental biogeography to evaluate a suite of environmental conditions, mitigated by demography, which may facilitate the establishment of three invasive alien species (IAS). Species distribution models have highlighted areas of northern New England, currently lacking IAS, as places where these species may potentially thrive. However, as these IAS move northward, they will encounter novel conditions affecting establishment and growth. How species’ population dynamics respond to these novel conditions will influence their further spread and potential impact across the region. We established regional transplant experiments to test species distribution model results and to estimate the colonization potential for representative IAS, Berberis thunbergii and Alliaria petiolata, compared to two native analogues, Lindera benzoin and Arabis glabra. We investigated the response of each species’ vital rates to the environmental gradients using linear regression models. We then built demographic Integral Projection Models (IPMs) for our study species from these regressions to assimilate demographic information and make population-level predictions. Results/Conclusions Results indicate that the invasive species tolerate a broad climate gradient across New England. The invasives were able to germinate, survive, grow and, in some cases, reproduce in northern New England outside of their current known distributions, validating the results of previous model predictions. Across the species pairs, the invasives had a much higher rate of germination in the field than their native analogs. Of those that germinated, the invasives had a greater rate of survival to seedling regardless of environment. At later demographic stages, the invasives overall also had greater rates of survival and growth. The resulting population growth rates varied by environment, in some cases drastically. These results highlight the necessity in taking microsite variation into account when calculating population dynamics. The observed responses for each vital rate enable us to make predictions about survival, reproduction, and population growth rate and assess how IAS may respond to a changing environment.

There is much spatial and temporal variation for reproductive output in white clover (Trifolium repens L.), yet little is known about the control of this variation or whether there exist tradeoffs among components of seed yield. To examine these issues, seed yield components and vegetative biomass were measured on replicates of seven white clover genotypes planted in a common garden plot. Significant genetic differences among clones were found for biomass and for five of seven reproductive characters, including number of inflorescences, number of florets per inflorescence, number of fruits per infructescence, number of (late-maturing) seed per fruit, and seed weight in early-maturing fruits. Thus, there is considerable potential for natural or artificial selection to act on vegetative and reproductive characters in white clover. In addition to these genetic effects, we observed temporal variation for number of florets per inflorescence, number of fruits per infructescence, and seed weight in late-maturing fruits. Finally, analyses of phenotypic, genetic, and microenvironmental correlation coefficients revealed few pairs of traits with significant negative correlations. This suggests that few tradeoffs in resource allocation patterns existed for the phenotypic characters examined. Key words: Trifolium repens, genetic variation, seed yield components, tradeoffs, phenotypic correlation, genetic correlation.

ABSTRACT Background/Question/Methods Phenology has proven to be an effective metric for assessing how climate change is impacting organisms around the world. In response to warmer temperatures and altered precipitation, plants and animals have adjusted their phenologies to various degrees. Here, we investigated how a suite of insect species from throughout Japan has responded to changes in climate both spatially and temporally. Forty years of data on emergence dates of 14 insect species from 102 observatories of the Japan Meteorological Agency (JMA) were used for this research along with basic natural history traits. The results of analysis of this insect dataset were then compared to datasets of plant and bird phenology to evaluate how the phenologies of organisms at different trophic levels are changing. If, for example, organisms at each trophic level are responding differently to changes in climate, then there is the potential for ecological mismatches. In such a scenario, the phenologies of associated organisms are no longer synchronous and the success of future populations may be put in danger. Extensive datasets exist on the phenology of plants, insects, and birds in Massachusetts and these were analyzed with additional data from the JMA to address this larger question. Results/Conclusions The emergence dates of insects in Japan are closely correlated with temperature, with individuals of most species emerging earlier in warmer years. Surprisingly however, insects are emerging later over time in a country that is growing progressively warmer. This apparent disparity may be due to extreme declines in populations due to factors outside of climate, such as land use change or urbanization. Insects in Massachusetts are also responding to temperature, with some showing a temporal response and others not. Looking across trophic levels, plant flowering and leaf-out have the strongest response to temperature as well as the greatest amount of variation explained by temperature. Bird migration phenology exhibits the weakest pattern of change with temperature, along with the weakest explanatory power. Insects fall in the middle and generally have a strong response to temperature, yet weak explanatory power. Although the animals in these studies are often generalist feeders, the fact that each trophic level is impacted differently by climate change is cause for concern. The finding that organisms at a given trophic level are responding similarly in different parts of the world gives additional weight to this study as altered relationships among species are not geographically confined.

Changes in spring and autumn phenology of temperate plants in recent decades have become iconic bio-indicators of rapid climate change. These changes have substantial ecological and economic impacts. However, autumn phenology remains surprisingly little studied. Although the effects of unfavorable environmental conditions (e.g., frost, heat, wetness, and drought) on autumn phenology have been observed for over 60 y, how these factors interact to influence autumn phenological events remain poorly understood. Using remotely sensed phenology data from 2001 to 2012, this study identified and quantified significant effects of a suite of environmental factors on the timing of fall dormancy of deciduous forest communities in New England, United States. Cold, frost, and wet conditions, and high heat-stress tended to induce earlier dormancy of deciduous forests, whereas moderate heat- and drought-stress delayed dormancy. Deciduous forests in two eco-regions showed contrasting, nonlinear resp...

Conservation of biodiversity and natural resources in a changing climate requires understanding what controls ecosystem resilience to disturbance. This understanding is especially important in the fire-prone Mediterranean systems of the world. The fire frequency in these systems is sensitive to climate, and recent climate change has resulted in more frequent fires over the last few decades. However, the sensitivity of postfire recovery and biomass/fuel load accumulation to climate is less well understood than fire frequency despite its importance in driving the fire regime. In this study, we develop a hierarchical statistical framework to model postfire ecosystem recovery using satellite-derived observations of vegetation as a function of stand age, topography, and climate. In the Cape Floristic Region (CFR) of South Africa, a fire-prone biodiversity hotspot, we found strong postfire recovery gradients associated with climate resulting in faster recovery in regions with higher soil ...

Background/Question/Methods Species distribution models generally fail to account for the multiple processes driving species distributions, the different spatial scales at which these act, and the inherent ambiguity of species absence data. This has led to criticism of species distribution models as possessing weak statistical power and producing unrealistically narrow estimates of potential range. This is an important problem because such models are widely used to design reserves and predict species responses to climate change. We present hierarchical Bayesian regression models that improve on single-level models by distinguishing, at different model levels, among three distinct factors influencing presence/absence and abundance: local environment, land use, and neighborhood or spatial processes. Our goal is to integrate the increasingly available large data sets on species distributions to build down from the broad regional spatial scale of species distribution data down toward po...

Background/Question/Methods Plant and animal species phenology, the timing of natural events, have been one of the mechanisms most affected by current climate change. Within this realm, trends taking place in the last few decades of global warming are being used to make forecasts into the future. However, they are limited in their scope as they are only available for a few species in one or a few locations. Here we present an analysis of the phenological response (spring, summer and fall) to climate variation of several plant and animal species, whose phenological events were monitored at 176 meteorological stations in Japan and South Korea from 1953 to 2005. For that we developed a hierarchical Bayesian model to examine the complex interactions of temperature, site effects, and latitude on phenology. Results/Conclusions Results show species-specific variation in the magnitude and the direction of their responses to increasing temperature, which also differ from site to site. At mos...

Background/Question/Methods Ecological relationships may be disrupted when species respond in different ways to climate change. However, evidence for such mismatches in response is generally lacking because of the difficulty of obtaining informative data. Here we present an analysis of the phenological responses to climate variation of twelve species over a three month period from mid-winter to spring: included are first flowering or budburst of six plant species (Camellia, Ginkgo, Morus, Prunus, Taraxacum, Wisteria), and the first appearance or singing of six animal species, including two insects (Pieris, Polistes), a frog (Rana) and three birds (Alauda, Cettia, Hirundo). These species are not directly linked ecologically, but they are distributed among three different trophic levels. Phenology was monitored at 176 government meteorological stations varying in latitudes and elevations across Japan and South Korea from 1953 to 2005, and in some cases even longer. We developed a hier...

Background/Question/Methods: Anthropogenic environmental changes include increased landscape fragmentation in a CO2-enriched atmosphere that may increase species invasiveness. Land fragmentation increase light availability by creating open fields and forest edges that provide disturbed habitat for invasive species. Because the direct effects of elevated CO2 on plant growth is species specific, and enhanced CO2 can mitigate other environmental stresses, we wanted to assess the potential response of invasive species in projected future environments to better forecast their future ranges. Using a multi-factorial design, we compared pairs of invasive and native species from New England that were grown in controlled environment growth chambers at the Duke University Phytotron. Two species of each biennials, vines, and shrubs were used to assess if invasive of various growth forms respond similarly. After germination, small seedlings were grown at current ambient (380 µmol mol-1) and elev...

Background/Question/Methods As part of a multi-scale project to predict invasive plant spread in response to climate and land-use change, we are evaluating the environmental conditions that facilitate the establishment of three invasive alien species (IAS) in New England. Previously developed predictive models have highlighted areas of northern New England currently lacking IAS as places where these species may potentially thrive. As these IAS move northward, they will encounter novel conditions affecting establishment and growth. How these species respond will influence their further spread and impact across the region. We established a regional transplant experiment to test predictive model results and to estimate the colonization potential for three common IAS in New England: Berberis thunbergii, Celastrus orbiculatus, and Alliaria petiolata. Native analogs to each of these species were selected to quantify response differences; Lindera benzoin, Vitis labrusca, and Arabis glabra....

Background/Question/Methods As plant invasions continue to tax ecosystems and threaten biodiversity, it is increasingly important to develop general mechanistic models that can explore the roles of different factors affecting spread. Among the most aggressive invasive species are those dispersed by birds. These plants can move large distances in very short times and, depending on bird habits, be preferentially deposited in favorable habitats. Both birds and plants respond in important ways to heterogeneous habitats, which is critical to predicting the dynamics of invasion. We develop a cellular automata model that uses bird and plant habitat preferences in conjunction with a mechanistically derived seed dispersal kernel to predict broad scale spread patterns. We apply the model to the spread of Celastrus orbiculatus (Oriental bittersweet) by Sturnus vulgaris (European starling) in New England. We parameterize the model with explicit starling movement data and bittersweet survival da...

Background/Question/Methods Invasive species’ distributions are often not at equilibrium in their invasive ranges. Therefore, inferring population dynamics based on current locations of populations may under- or over-estimate population growth rates and, thus, potential spread. We took an experimental approach to investigating the demographic processes that underlay population dynamics across a range of environmental conditions hypothesized to be invasible. Linking the environment to demography is important to determine species range limits and to provide a mechanistic basis for understanding invasions. This link is particularly important for understanding invasive species distributions and for designing appropriate management actions because only mechanistic models allow us to confidently extrapolate population level patterns to a new set of conditions or novel landscape. We investigated establishment dynamics of the woody invasive species Berberis thunbergii and a native ecologica...

Background/Question/Methods Observed phenological changes in recent decades across temperate forest regions, such as earlier leafing in the spring or later leaf senescence in the fall, provide a dramatic indication of biological response to climate change. While mechanisms of spring phenology (bud burst, leafing out, and flowering) have been well studied and integrated into predictive models of future responses to climate change, it is still unclear how fall phenology in plants (leaf senescence and dormancy) responds to environmental variation. Although delayed leaf coloration and abscission in deciduous forest trees have been observed in Europe and North America in recent decades, the role of different environmental triggers and how they interact in different species to produce observed fall phenological patterns remains unknown. There is a long list of environmental changes or stressors during growing season that may affect phenological change in fall. Based on remotely sensed phe...

Background/Question/Methods Invasive species’ geographic distributions are often not at equilibrium in their invasive ranges. Therefore, invasion risk based on current occurrence patterns may lead to biased estimates of the locations at risk of invasion. We took an experimental biogeographical approach to investigating the underlying demographic processes that drive population dynamics across a range of potentially invasible environmental conditions. By linking demographic rates (growth, survival, fecundity) to explanatory environmental variables we obtained a mechanistic basis for determining species range limits. These mechanisms are particularly important for understanding invasive species distributions to improve early detection abilities for species with nonequilibrium distributions. We investigated population dynamics of two invasive species: a monocarpic biennial (Alliaria petiolata; garlic mustard) and a woody shrub (Berberis thunbergii; Japanese barberry) and two native eco...

Background/Question/Methods Knowledge of a species' range limit is important when triaging for non-native invasive species management. With invasive species, we often have limited data at the early stages of invasion as species are not at equilibrium. These species may be beyond eradication or control by the time enough demographic data are collected. Inferring population dynamics based on current locations of invasive populations may under- or over-estimate population growth rates and, thus, potential spread. Understanding how environmental variation affects demographic parameters is important in determining species range limits and to providing a mechanistic basis for understanding invasions. Mechanistic models increase our confidence when extrapolating population level patterns to a new set of conditions or novel landscape. We investigated establishment dynamics of two non-native invasive species; the woody Berberis thunbergii and the monocarpic biennial Alliaria petiolata, a...

Background/Question/Methods The annual timing of leafing, flowering, and fruiting of invasive plants relative to natives may explain some of their success. Species-level differences may also help predict responses to climate change. Earlier springtime leafing of invasives may lead to an increase in resource acquisition, and by extension may bolster population growth. Later leaf coloration and drop effectively extends the growing season. Phenological separation between species may vary by life stage, such that mature plants exhibit different patterns than seedlings. If phenological separation of species is sensitive to ontogeny, the benefit may depend on the stage of invasion, such that phenological novelty of seedlings aids establishing populations and mature plant phenological separation better facilitates spread. We used a combination of experimental plantings and wild populations over two years to estimate the phenological separation in leafing events in two pairs of native and i...

ABSTRACT Background/Question/Methods Species Distribution Models (SDMs; e.g. Maxent, GARP, GLMs, etc.) are typically used to describe the correlation between occurrence patterns and environmental covariates. Often, these methods are used because species’ presence, and sometimes absence, at locations on a landscape are the only available population-level data. While this class of SDMs is useful for exploring spatial occurrence data, they offer limited insights into the underlying population biology that generates these patterns because occurrence patterns entangle a variety of processes such as demography, dispersal, biotic interactions and historical effects. Here, we show how to use a limited amount of demographic data to produce Demographically Driven Distribution Models (DDDMs) using Integral Projection Models (IPMs) for stage-structured populations. By modeling vital rate functions such as survival, growth, and fecundity using regression, they can interpolate across missing size data and environmental conditions to compensate for limited data. Results/Conclusions DDDMs offer at least five advantages over occurrence-based SDMs: (1) DDDMs allow a mechanistic understanding of the demographic processes that generate spatial occurrence patterns; (2) DDDMs predict more biologically meaningful demographic summaries of population patterns such as population growth rate, life expectancy, or stage distributions; (3) DDDMs can avoid issues of aggregation by modeling how individuals respond to weather, rather than modeling how a species responds to climate; (4) DDDMs allow for more robust extrapolation under new environmental conditions (e.g. climate change) because one can more readily evaluate the consequences of extrapolating a vital rate representing single biological process than an occurrence probability that entangles multiple processes; (5) DDDMs can predict temporal dynamics, in contrast to the typically static predictions of SDMs. To illustrate these principles, we construct DDDMs for an overstory perennial shrub in the Proteaceae family in the Cape Floristic Region of South Africa by combining data from a variety of sources. We compare these models to SDMs that predict occurrence probability and illustrate inference about the demographic processes that drive differences in habitat suitability.

ABSTRACT Background/Question/Methods Springtime phenological events are often used as biological indicators of climate change and can be useful for predicting future community responses to global change. Assessment of phenological responsiveness is often accomplished using linear regression, with temperature or temperature proxies in the preceding months used to explain observed events. While linear regression approaches remain useful, they require somewhat arbitrary delineations of antecedent weather windows used to model observed events. Our objective is to explain observed differences in flowering time over the last 30 years among a group of 16 closely related cherry taxa (Cerasus spp.) at the Tama Forest Science Garden (Hachioji, Japan) using a survival analysis approach to better understand flowering cues and how these may vary among taxa. We utilize a survival regression approach that models phenological events over the observation intervals with both time-varying and time-invariant environmental covariates. By using this approach, we are able to quantify differences among species through time in their probability of flowering and incorporate temperature-derived variables (e.g., chill and heat units) on a fine time scale. By gaining a more detailed understanding of the timing of flowering, we hope to provide better forecasts of future flowering events and ultimately how species will respond to climate change. Results/Conclusions The cumulative probability curves of first flowering of Tama Forest cherries demonstrate clear taxon-level differences in shape and rate of these probability functions. Some species, such as Cerasus speciosa, show an extended early period of low flowering probability followed by a later, steeper cumulative probability curve. Others, such as C. jamasakura, show much later and compressed cumulative probability curves. Cox proportional hazards models indicate that taxa differ in their response to temperature cues and that the physical environment, such as slope and aspect, also influence flowering times. Variation in cumulative probability estimates is not consistent among species, indicating that some taxa may respond more coherently to temperature cues than others. These results allow the direct use of temperature-related covariates at the same temporal scale as observations, thereby allowing the data to dictate phenology-temperature relationships.

ABSTRACT Background/Question/Methods Maxent is one of the most popular species distribution modeling methods, with over 400 published applications in the just the last six years. Maxent users are confronted with a wide variety of options when fitting their models, from the multiple options and settings available in the software to which input datasets to choose. However, the default settings are often chosen as a consequence of unfamiliarity with maximum entropy models, even though alternatives may often be more appropriate when connected to specific ecological questions. To explore Maxent’s assumptions, we demonstrate the variability in model output that can result from altering model settings and offer suggestions for choosing these settings. Results/Conclusions Maxent models are capable of predicting the relative probability of occurrence, but not the absolute probability of occurrence. Considering this, and the quality of opportunistically collected occurrence data and coarse resolution, remotely sensed environmental data, predictions must be interpreted cautiously when creating range maps and using them to answer complex ecological or evolutionary questions. In many cases, Maxent is best suited for hypothesis generation and asking better questions, not answering them. To date, variable selection methods explored for Maxent derive almost exclusively from machine learning perspectives, which focus more on complex pattern recognition than on producing easily interpreted models. We outline a more general approach, based on constructing simpler models motivated by specific ecological questions. In Maxent, different prior assumptions (null models) are reflected by methods of background sample selection and accounting for sampling bias. We relate these subtle cases of prior specification to the more general case, where ‘ecological’ priors can be used to incorporate different ecological assumptions or output from other models. Ecological priors are used to improve predictions of models for invasive ranges using native range data and models accounting for dispersal limitation. When ecological priors are applied to understand the spread Celastrus orbiculatus, an invasive liana, across New England (USA) over the last century, we find that greater spread is predicted in northern New England than models using default settings.

ABSTRACT Background/Question/Methods As part of a multi-scale project to predict invasive plant spread in response to climate and land-use change, we used experimental biogeography to evaluate a suite of environmental conditions, mitigated by demography, which may facilitate the establishment of three invasive alien species (IAS). Species distribution models have highlighted areas of northern New England, currently lacking IAS, as places where these species may potentially thrive. However, as these IAS move northward, they will encounter novel conditions affecting establishment and growth. How species’ population dynamics respond to these novel conditions will influence their further spread and potential impact across the region. We established regional transplant experiments to test species distribution model results and to estimate the colonization potential for representative IAS, Berberis thunbergii and Alliaria petiolata, compared to two native analogues, Lindera benzoin and Arabis glabra. We investigated the response of each species’ vital rates to the environmental gradients using linear regression models. We then built demographic Integral Projection Models (IPMs) for our study species from these regressions to assimilate demographic information and make population-level predictions. Results/Conclusions Results indicate that the invasive species tolerate a broad climate gradient across New England. The invasives were able to germinate, survive, grow and, in some cases, reproduce in northern New England outside of their current known distributions, validating the results of previous model predictions. Across the species pairs, the invasives had a much higher rate of germination in the field than their native analogs. Of those that germinated, the invasives had a greater rate of survival to seedling regardless of environment. At later demographic stages, the invasives overall also had greater rates of survival and growth. The resulting population growth rates varied by environment, in some cases drastically. These results highlight the necessity in taking microsite variation into account when calculating population dynamics. The observed responses for each vital rate enable us to make predictions about survival, reproduction, and population growth rate and assess how IAS may respond to a changing environment.

ABSTRACT Knowledge of species' geographic distributions is critical for understanding and forecasting population dynamics, responses to environmental change, biodiversity patterns, and conservation planning. While many suggestive correlative occurrence models have been used to these ends, progress lies in understanding the underlying population biology that generates patterns of range dynamics. Here, we show how to use a limited quantity of demographic data to produce demographic distribution models (DDMs) using integral projection models for size-structured populations. By modeling survival, growth, and fecundity using regression, integral projection models can interpolate across missing size data and environmental conditions to compensate for limited data. To accommodate the uncertainty associated with limited data and model assumptions, we use Bayesian models to propagate uncertainty through all stages of model development to predictions. DDMs have a number of strengths: 1) DDMs allow a mechanistic understanding of spatial occurrence patterns; 2) DDMs can predict spatial and temporal variation in local population dynamics; 3) DDMs can facilitate extrapolation under altered environmental conditions because one can evaluate the consequences for individual vital rates. To illustrate these features, we construct DDMs for an overstory perennial shrub in the Proteaceae family in the Cape Floristic Region of South Africa. We find that the species' population growth rate is limited most strongly by adult survival throughout the range and by individual growth in higher rainfall regions. While the models predict higher population growth rates in the core of the range under projected climates for 2050, they also suggest that the species faces a threat along arid range margins from the interaction of more frequent fire and drying climate. The results (and uncertainties) are helpful for prioritizing additional sampling of particular demographic parameters along these gradients to iteratively refine projections. In the appendices, we provide fully functional R code to perform all analyses.

As a consequence of warming temperatures around the world, spring and autumn phenologies have been shifting, with corresponding changes in the length of the growing season. Our understanding of the spatial and interspecific variation of these changes, however, is limited. Not all species are responding similarly, and there is significant spatial variation in responses even within species. This spatial and interspecific variation complicates efforts to predict phenological responses to ongoing climate change, but must be incorporated in order to build reliable forecasts. Here, we use a long-term dataset (1953–2005) of plant phenological events in spring (flowering and leaf out) and autumn (leaf colouring and leaf fall) throughout Japan and South Korea to build forecasts that account for these sources of variability. Specifically, we used hierarchical models to incorporate the spatial variability in phenological responses to temperature to then forecast species' overall and site-s...