A frequent challenge encountered in real-world applications is data having a high proportion of zeros. Focusing on ecological abundance data, much attention has been given to zero-inflated count data. Models for non-negative continuous abundance data with an excess of zeros are rarely discussed. Work presented here considers the creation of a point mass at zero through a left-censoring approach or through a hurdle approach. We incorporate both mechanisms to capture the analogue of zero-inflation for count data. Additionally, primary attention has been given to univariate zero-inflated modeling (e.g., single species), whereas data often arise jointly (e.g., a collection of species). With multivariate abundance data, a key issue is to capture dependence among the species at a site, both in terms of positive abundance as well as absence. Therefore, our contribution is a model for multivariate zero-inflated continuous data that are non-negative. Working in a Bayesian framework, we discuss the issue of separating the two sources of zeros and offer model comparison metrics for multivariate zero-inflated data. In an application, we model the total biomass for five tree species obtained from plots established in the Forest Inventory Analysis database in the Northeast region of the United States.

Abstract Turnover, or change in the composition of species over space and time, is one of the primary ways to define beta diversity. Inferring what factors impact beta diversity is not only important for understanding biodiversity processes but also for conservation planning. At present, a popular approach to understanding the drivers of compositional turnover is through generalized dissimilarity modelling (GDM). We argue that the current GDM approach suffers several limitations and provide an alternative modelling approach that remedies these issues. We propose using generative spatial random effects models implemented in a Bayesian framework. We offer hierarchical specifications to yield full regression and spatial predictive inference, both with associated full uncertainties. The approach is illustrated by examining dissimilarity in three datasets: tree survey data from Panama's Barro Colorado Island (BCI), plant occurrence data from southwest Australia and plant abundance surveys from the Greater Cape Floristic Region (GCFR) of South Africa. We select a best model using out‐of‐sample predictive performance. We find that the form of the best model differs across the three datasets, but our models provide performance ranging from comparable to significant improvement over GDMs. Within the GCFR, the spatial random effects play a more important role in the modelling than all the environmental variables. We have proposed a model that provides several improvements to the current GDM framework. This includes advantages such as a flexible spatially varying mean function, spatial random effects that capture dependence unaccounted for by explanatory variables, and spatially heterogeneous variance structure. All these features are offered in a model that can adequately handle a large incidence of total dissimilarity through ‘one‐inflation’, as would be expected from highly biodiverse areas with steep turnover gradients.

Ecological modelling often involves addressing challenges such as dependence in responses, e.g., spatial and/or temporal correlation, heterogeneity of variance, and hierarchical structures inherent in ecological processes and data. A constant challenge is the inadequacy of the data to well address the questions of interest. What is observable may not be sufficiently informative. What has been observed may not have been well designed. Carefully conceived modelling can take us to improved inference compared with adopting standard inference tools like basic regression and analysis of variance. Hierarchical modelling techniques provide a powerful framework for capturing these complexities by explicitly modelling the multi-level structure of ecological systems.In this paper we focus on good modelling practice in the hierarchical Bayesian framework. We discuss good modelling practice in the form of 10 steps, elaborated with a running example, to aid quantitative ecologists interested in employing Bayesian modelling. Two of the authors are statisticians who view themselves primarily as stochastic modellers. The other two are quantitative ecologists who view such stochastic tools as essential for advancing ecological knowledge. Hence, implicitly, we pay careful attention to good modelling practice. Further, we argue the benefits that accrue to working in a Bayesian framework as the paper is developed.It is worth noting that the steps we propose, though presented in the context of ecological modelling, are appropriate for effective model building across most fields of application.

Societal Impact Statement Madagascar is famous for its unique forests and their fauna. Most of the island is covered by flammable grassy ecosystems long considered to be of human origin and threatening the remaining forests. Yet new studies show that many plants and animals of the grassy systems are unique to Madagascar and restricted to these open habitats. Open grassy ecosystems have markedly different management requirements from forests and bring different contributions to society. We argue that the grassy ecosystems can benefit Madagascar if understood and managed wisely using expanded knowledge bases that also include collaboration with locals. Summary Until recently, nearly all research and interests in Madagascar focused on forested habitats. To help place Madagascar's grassy ecosystems in context, we provide a summary of the origin, development, and evolution of open tropical, C4 grassy ecosystems elsewhere, especially those from Africa; we summarize similarities and differences with the distribution of C3 and C4 grasses in the Malagasy landscape, their plant traits, and inferences on the evolutionary legacy of grasses. We also discuss the animal communities that use and have coevolved in these grassy systems; to help resolve controversies over the pre‐settlement extent of grassy ecosystems, we suggest a variety of complementary geochemical, palaeobotanical, and molecular genetic tools that have been effectively used elsewhere to untangle forest/grassy ecosystem mosaics and the ecological and evolutionary processes that influence them. Many of these tools can and should be employed in Madagascar to fully understand the spatio‐temporal dynamics of open, grassy, and closed forest systems across the island; as regards conservation, we discuss the ecosystem services provided by grassy systems, which are too often ignored in general, not only as a biome, vis‐à‐vis forests, but also for their global importance as a carbon sink and role they play in water management and providing goods to local villagers. We conclude by outlining the necessary research to better manage open ecosystems across Madagascar without threatening endangered forest ecosystems.

The investigation of leaf-level traits in response to varying environmental conditions has immense importance for understanding plant ecology. Remote sensing technology enables measurement of the reflectance of plants to make inferences about underlying traits along environmental gradients. While much focus has been placed on understanding how reflectance and traits are related at the leaf-level, the challenge of modelling the dependence of this relationship while accounting for environmental gradients has limited this line of inquiry. Here, we take up the problem of jointly modeling traits and reflectance given environment. Our objective is to assess not only response to environmental regressors but also dependence between trait levels and the reflectance spectrum in the context of this regression. We jointly model the response vector of traits with reflectance, which is a function of wavelength. To conduct this investigation, we employ a dataset from a global biodiversity hotspot, the Greater Cape Floristic Region in South Africa.

For most of the 20th century, the hypothesis was accepted that Madagascar's extensive grass-dominated ecosystems were of anthropogenic origin, carved out of pristine forests after a mere two millennia of human settlement. We tested an alternative hypothesis that these C-4 grassy ecosystems were part of the general Late Miocene expansion of tropical grassy biomes, using diverse data from published sources (Bond et al. 2008). Joseph and Seymour (2021) criticised this paper, which they see as seminal to subsequent studies on the grasslands. Here we respond to their critique of our study. We also briefly note diverse studies since 2008 pointing to the ancient origin of Madagascar's C-4 grasses and the ecosystems they dominate. We conclude with key research needs that will help promote open-minded research on these long neglected grassy biomes. The answers would be of considerable scientific and public interest but may also contribute to enlightened management of forest/grassland mosaics.

A frequent challenge encountered with ecological data is how to interpret, analyze, or model data having a high proportion of zeros. Much attention has been given to zero-inflated count data, whereas models for non-negative continuous data with an abundance of 0s are much fewer. We consider zero-inflated data on the unit interval and provide modeling to capture two types of 0s in the context of a Beta regression model. We model 0s due to missing by chance through left-censoring of a latent regression and 0s due to unsuitability using an independent Bernoulli specification. We extend the model by introducing spatial random effects. We specify models hierarchically, employing latent variables, and fit them within a Bayesian framework. Our motivating dataset consists of percent cover abundance of two plant families at a collection of sites in the Cape Floristic Region of South Africa. We find that environmental features enable learning about both types of 0s as well as positive percent cover. We also show that the spatial random effects model improves predictive performance. The proposed modeling enables ecologists to extract a better understanding of an organism’s absence due to unsuitability vs. missingness by chance, as well as abundance behavior when present. Supplementary materials accompanying this paper appear online.

Plant reflectance spectra - the profile of light reflected by leaves across different wavelengths - supply the spectral signature for a species at a spatial location to enable estimation of functional and taxonomic diversity for plants. We consider leaf spectra as "responses" to be explained spatially. These spectra/reflectances are functions over a wavelength band that respond to the environment. Our motivating data are gathered for several families from the Cape Floristic Region (CFR) in South Africa and lead us to develop rich novel spatial models that can explain spectra for genera within families. Wavelength responses for an individual leaf are viewed as a function of wavelength, leading to functional data modeling. Local environmental features become covariates. We introduce wavelength - covariate interaction since the response to environmental regressors may vary with wavelength, so may variance. Formal spatial modeling enables prediction of reflectances for genera at unobserved locations with known environmental features. We incorporate spatial dependence, wavelength dependence, and space-wavelength interaction (in the spirit of space-time interaction). We implement out-of-sample validation to select a best model, discovering that the model features listed above are all informative for the functional data analysis. We then supply interpretation of the results under the selected model.

Aim With plant biodiversity under global threat, there is an urgent need to monitor the spatial distribution of multiple axes of biodiversity. Remote sensing is a critical tool in this endeavour. One remote sensing approach for detecting biodiversity is based on the hypothesis that the spectral diversity of plant communities is a surrogate of multiple dimensions of biodiversity. We investigated the generality of this 'surrogacy' for spectral, species, functional and phylogenetic diversity across 1,267 plots in the Greater Cape Floristic Region (GCFR), a hyper-diverse region comprising several biomes and two adjacent global biodiversity hotspots. Location The GCFR centred in south-western and western South Africa. Time period All data were collected between 1978-2014. Major taxa studied Vascular plants within the GCFR. Methods Spectral diversity was calculated using leaf reflectance spectra (450-950 nm) and was related to other dimensions of biodiversity via linear models. The accuracy of different spectral diversity metrics was compared using 10-fold cross-validation. Results We found that a distance-based spectral diversity metric was a robust predictor of species, functional and phylogenetic biodiversity. This result serves as a proof-of-concept that spectral diversity is a potential surrogate of biodiversity across a hyper-diverse biogeographic region. While our results support the generality of spectral diversity as a biodiversity surrogate, we also find that relationships vary between different geographic subregions and biomes, suggesting that differences in broad-scale community composition can affect these relationships. Main conclusions Spectral diversity was shown to be a robust surrogate of multiple dimensions of biodiversity across biomes and a widely varying biogeographic region. We also extend these surrogacy relationships to ecological redundancy to demonstrate the potential for additional insights into community structure based on spectral reflectance.

Shifts in the timing of autumnal leaf coloration and leaf drop in temperate forests with climate change can have substantial impacts on community and ecosystem processes (e.g. altered carbon/nitrogen cycling and biotic interactions). However, the environmental control of autumn phenology remains significantly understudied in striking contrast to spring phenology. In this study, we used linear mixed effects model with ground-based phenology observations in northeastern USA and found that both weather stressors (e.g. heat- and drought-stress and heavy rainfall) during the growing season and spring phenology significantly affected inter-annual variation in autumn phenology of twelve dominant deciduous tree species. While warm temperatures and drought lead to later fall phenology for most species, heavy rainfall and heat stress lead to earlier leaf coloration and leaf drop. We also found that the phenological sensitivities to weather stressors are diversely species-specific. Under future climate change projections, we predicted that greater summer heat-stress in the future will cause abbreviated leaf coloration seasons for most species. Our mixed-effects modeling framework suggested that accounting for phenological variations among individual trees, species and sites largely improved model predictions, which should not be overlooked in phenological model development. Our study improves our understanding of how species-specific autumnal phenology responds to weather stresses, and describes a new modeling framework to investigate both inter-annual phonological changes and local variations among trees, species, and sites. Our predictions on autumn phonological shifts will help in assessing the effects of climate change on forest community and ecosystem processes in the future.

Abstract Plant leaf phenology is typically observed either via ground‐based visual observations on individuals or via remote sensing of land surface vegetation. To integrate phenological information from both data sources, collected at different spatial scales using different observational protocols, digital cameras were deployed spanning canopy areas with enough spatial resolution to identify temporal changes in individual deciduous tree species with continuous observations. Comparisons of phenology between camera photography and in situ observations have been reported in prior studies; however, it is still unclear that how these camera images relate to field observations at individual and species levels, and how the metrics from those images provide comparable species‐specific phenological responses to environmental variation. We set a suite of digital time‐lapse cameras to acquire continuous photographs of deciduous tree canopies and conducted ground‐based visual observations in Connecticut, USA, from 2012 to 2014. Comparisons between image‐derived dates and observed phenological dates showed that both green and red color indices could be matched to ground observations, and red color indices showed good performance in matching autumn phenology across our group of eight tree species that dominate the southern New England forests. Linear mixed‐effects models were applied to investigate the relationships between climatic/weather conditions and the timing of peak and of intensity of red color in fall foliage for each species. Model results suggested that temperature, precipitation, drought stress in autumn, and heat stress in summer are all important factors to the timing of peak fall foliage color and that higher minimum temperatures (or lower cold degree‐day accumulation) in the autumn are linked to higher intensity of red coloration at least in sugar maples. This study improves our understanding of temporal and spatial variation in the phenology of deciduous trees captured by digital cameras. As well, this provides insights into relating species‐specific information on phenology from visual observations in the field to near‐surface remote sensing and points to the need for further research on autumn phenology using the change in redness of tree canopies.

The Greater Cape Floristic Region (GCFR) of South Africa includes marine and terrestrial biomes with species diversity rivaling mega-diverse tropical rainforests in a compact area (300x700km). Extinction risk studies suggest that GCFR species are among the most vulnerable to climate change over the next 50 years. I present a scoping proposal commissioned by NASA to develop a field campaign to measure and monitor the distribution and abundance of biodiversity with new remotely-sensed data and the rich historical data in this region. I will summarize the central questions to be addressed by this field campaign and lay out the proposed study design to integrate satellite, airborne, and in situ data collection. Our plan centers around the collection of new hyperspectral imagery from AVIRIS-NG, PRISM, and HyTES spectrometers combined with the LVIS laser altimeter. These data will be collected at approximately 20 m spatial resolution across much of the GCFR and nearby aquatic and marine ecosystems. These data will then be combined with existing and new observations of the spatial distribution of community composition and functional traits to enable high resolution mapping and modeling of several essential biodiversity variables (EBVs) including species distributions, functional traits (including leaf properties), and three-dimensional canopy structure. Given the wealth of available independent in situ data available that can be brought to bear, the GCFR is an ideal system to fully evaluate the capabilities of remote-sensing technology to characterize biodiversity patterns across diverse landscapes in a relatively compact geographic area. In combination with the rich historical data and well-developed ecological understanding in this region, these new observations will enable detailed exploration into the drivers and mechanisms of change including the feedbacks from changing biodiversity to regional climate, disturbance, post-fire recovery, freshwater provisioning, and other ecosystem services.

Understanding and predicting how species traits are shaped by prevailing environmental conditions is an important yet challenging task in ecology. Functional trait-based approaches can replace potentially idiosyncratic species-specific response models in learning about community behaviour across environmental gradients. Customarily, models for traits given environment consider only trait means to predict species and functional diversity, as intra-taxon variability in traits is often thought to be negligible. A growing body of literature indicates that intra-taxon trait variability is substantial and critical in structuring plant communities and assessing ecosystem function. We propose flexible joint trait distribution models given environment and across species that incorporate intra-taxon variability as well as inter-site/plot variability. Using a Bayesian framework, our joint trait distribution models allow for mixed continuous, binary and ordinal trait variables and incorporate dependence among traits enabling both joint and conditional trait prediction at unobserved sites. The models can be used to inform about the well-known fourth-corner problem, which attempts to interpret trait-by-environment matrices. We demonstrate the utility of our methodology through joint predictive trait distributions for individual species as well as joint community-weighted trait distributions for environments while incorporating intra-taxon trait variability. Explicit details on the probabilistic interpretations of the random trait-by-environment matrices obtained arising under our model are also provided to address the fourth-corner problem. Finally, our joint trait distribution model is applied to simulated and real vegetation data collected from the Greater Cape Floristic Region of South Africa. The proposed methodology places a fully model-based foundation on explaining intra-taxon trait variation given environment. It extends the utility and interpretability of commonly applied techniques for investigating community-weighted traits and illuminates randomness in the fourth-corner problem.

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"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.

Although natural forests worldwide declined by 6% between 1990 and 2015, temperate forests, which currently cover only about 32–47% of their current potential extent and are more altered and reduced in original extent globally than boreal or tropical forests, have not suffered a net loss in surface area during that same time period. In fact the extant of temperate forests globally has increased slightly over that period. Nevertheless, very few, if any, remaining temperate forests have avoided human impact, and thus few temperate forests are old growth. Only about 1% of the remaining Northern Hemisphere temperate broad-leaved forests is substantially unaltered and can be considered old growth natural forest; the vast majority are either managed for wood production, have been transformed into plantations, or show the long-term effects of human land use practices. Because some of the highest human population densities are found in the temperate forest biome, some of the lowest forest areas per capita globally occur in that biome. Only the most isolated, inaccessible patches of temperate forest remain unaltered by humans.

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 because of higher growth and germination in warmer climates, with higher likelihood to establish farther north and in closed canopy habitats in the south. Invasion success is in high fecundity for both invasive species and demographic compensation for A. petiolata relative to native analogs. For A. petiolata, simulations suggest that eradication efforts would require unrealistic efficiency; hence, management should focus on inhibiting spread into colder, currently unoccupied areas, understanding source-sink dynamics, and understanding community dynamics should A. petiolata (which is allelopathic) decline. Our results-based on considerable differences with correlative occurrence models typically used for such biogeographic forecasts-suggest the urgency of incorporating mechanism into range forecasting and invasion management to understand how climate change may alter current invasion patterns.