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Canopy gaps are widely recognized as being crucial for maintaining the diversity of forest tree communities. But empirical studies have found mixed results because the differences in diversity between individual gaps and non‐gaps are often small and statistically undetectable. One overlooked factor, however, is how small individual gap versus non‐gap differences may accumulate across sites and potentially have a large effect on forest diversity at the plot scale. Our study investigated sapling richness, density, and composition in 124 treefall gaps, and 200 non‐gap sites in the 50‐ha tropical forest plot at Barro Colorado Island (BCI), Panama. Additionally, we analyzed species accumulation curves to understand how species richness increases with increasing stem numbers. We observed that sapling richness and density were only slightly higher in gaps 7 years after formation and statistically indistinguishable from non‐gaps after 12 years. However, species accumulation curves across multiple gaps were substantially higher than those across non‐gaps. Species composition showed small differences between individual gaps and non‐gaps but differed significantly between collections of gaps and non‐gaps. Specifically, 55 species specialized in 7‐year‐old gaps compared with 24 in non‐gaps; of these, 23 gap‐specialized species and zero non‐gap species were pioneers. Our results indicate that tree species richness is higher in gaps because of both higher stem density and the presence of gap‐specialized species. Our study has finally provided compelling evidence to support the idea that gaps enhance the overall diversity of tropical forest tree communities. [ABSTRACT FROM AUTHOR]

Hyperdiverse tropical forest tree communities illustrate a fundamental problem in ecology: How can many species coexist given relatively few limiting resources? Neutral theory provides a solution by positing that species have equal fitness and hence drift to extinction slowly. However, neutral theory seriously under‐predicts temporal changes in species abundances. This can be remedied by breaking neutrality and adding temporal environmental stochasticity (TES), but the mechanisms mediating the effects of TES on species richness remain unclear. Here, we make progress by analysing a local community model with species competing for a common resource under TES, to derive formulae partitioning species richness according to different mechanisms.By applying our formulae to generic parameter sets for tropical forest tree communities, we found that when the autocorrelation time of TES was short, the dominant mechanism driving species richness was non‐linear averaging of the interspecific competition term over time, which reduced the typical strength of interspecific competition and boosted richness relative to the neutral case. However, greater immigration to the community resulted in more species and hence weaker non‐linear averaging due to the law of large numbers. In contrast, when the autocorrelation time of TES was long, the dominant mechanism driving species richness was strong selection between changes in environmental conditions, which increased the typical strength of interspecific competition and reduced richness relative to the neutral case.By applying our formulae to a specific parameter set for a tropical forest tree community in Panama, we found that TES had minor effects on species richness because (i) the immigration rate was sufficiently large for non‐linear averaging of the interspecific competition term to be weak and (ii) the autocorrelation time was sufficiently short to suppress the effects of selection.Synthesis. We provide a novel mechanistic explanation of how TES affects tree species richness in tropical forests, in particular how TES often has minor effects on richness despite having substantial effects on temporal changes in species abundances. This provides a deeper insight into why a neutral model with added TES can accurately capture static and dynamic aspects of tree community diversity in the tropics. [ABSTRACT FROM AUTHOR]

Aim: Artificial island habitats such as human‐made wetlands are emerging novel ecosystems. Understanding the drivers of diversity in such artificial systems is essential for balancing the goals of biodiversity conservation and human socio‐economic needs. Location: Telangana state, India. Methods: We surveyed water birds in a network of 57 artificial wetlands and assessed four macroecological biodiversity patterns: spatial betadiversity, temporal betadiversity, species‐abundance distributions (SADs), and the species–area relationship (SAR). We employed a mix of phenomenological and mechanistic models to examine the four macroecological patterns. We hypothesized that the wetland bird communities are primarily structured by immigration–extinction dynamics and thus that spatial and temporal betadiversity would be high, the within‐wetland SADs would exhibit a large number of rare species and a monotonically declining overall shape, and that the SAR across wetlands would be strongly increasing. Results: Spatial and temporal betadiversity were both high and mostly attributable to turnover rather than nestedness. While the pooled SAD exhibited an interior mode, the SAD for individual wetlands was generally log‐series distributed, consistent with a model in which immigration among wetlands is high. The SAR exhibited an increasing trend, with the 'small‐island effect', which reflects constraints on immigration and is often observed for true island archipelagos, being absent. Main Conclusions: We tentatively conclude that bird diversity in this network of artificial wetlands is mainly structured by immigration–extinction dynamics, although we acknowledge that some of the patterns are also consistent with niche dynamics and future research should measure relevant biotic and abiotic variables in these wetlands. We encourage future work in which our rich dataset is used to fit dynamic models that permit more‐detailed quantitative inferences about mechanisms structuring diversity in this novel ecosystem, which can ultimately also inform conservation management. [ABSTRACT FROM AUTHOR]

Habitat complexity has been considered a key driver of biodiversity and other ecological phenomena for nearly a century. However, there is still no consensus over the definition of complexity or how to measure it. Up‐to‐date and clear guidance on measuring complexity is urgently needed, particularly given the rise of remote sensing and advent of technologies that allow environments to be scanned at unprecedented spatial extents and resolutions. Here we review how complexity is measured in ecology. We provide a framework for metrics of habitat complexity, and for the related concept of spatial heterogeneity. We focus on the two most commonly used complexity metrics in ecology: fractal dimension and rugosity. We discuss the pros and cons of these metrics using practical examples from our own empirical data and from simulations. Fractal dimension is particularly widely used, and we provide a critical examination of it drawing on research from other scientific fields. We also discuss informational metrics of complexity and their potential benefits. We chart a path forward for research on measuring habitat complexity by presenting, as a guide, sets of essential and desirable criteria that a metric of complexity should possess. Lastly, we discuss the applied significance of our review. [ABSTRACT FROM AUTHOR]

A long‐standing puzzle in ecology is coexistence of many species despite relatively few limiting resources. Studies using competitive community models have found that temporal environmental stochasticity (TES) can provide a solution by providing a rare‐species advantage, for example by creating temporal niches. However, this appears to contradict studies using population models, which have found that TES‐induced temporal fluctuations in species abundances increase the chance of species becoming rare and hence increase their extinction risk. Here, we clarified how TES affects species richness by analysing a competitive community model using a novel mean‐field approach. We found that when temporal correlation in TES was weak, the dominant effect of TES was to create temporal niches by decreasing the strength of interspecific competition via a temporal averaging effect, thereby increasing species richness relative to the neutral case without TES. In contrast, when temporal correlation was strong, the dominant effect of TES was to increase the strength of selection and hence interspecific competition, thereby decreasing species richness. Compared with these indirect community effects of TES, the direct effects of TES on species richness via temporal fluctuations in abundances of species populations were relatively minor. [ABSTRACT FROM AUTHOR]

Aim: Research in island biogeography has long focused mainly on present‐day island configurations. Recently, there has been an increasing focus on islands' past histories of land connection, shape and size. Moreover, continental islands (=shelf islands) have received less attention than oceanic islands, and species inventories from extremely small islands are lacking in many datasets. We examine the effects of sea‐level rise since the Last Glacial Maximum (LGM) on bird species diversity and composition of tropical shelf islands in Southeast Asia. Location: Sundaland. Taxon: Birds. Methods: We compiled avifaunal island inventories for 94 islands using an exhaustive literature review of historic surveys of larger islands combined with our own comprehensive island surveys from both small and large islands. Using generalised least‐squares models with spatial autocorrelation, we assessed the importance of traditional biogeographical parameters including area, maximum elevation, distance from mainland and geographical isolation, along with post‐LGM effects of change in island area and duration since isolation. We also compared the species composition on similar‐sized shelf islands from two categories—recently submerged and unsubmerged—using non‐metric multidimensional scaling. Results: Post‐LGM effects on species diversity are minimal and insular diversity is instead well explained by present‐day island characteristics, such as area, distance to mainland and proportion of land surrounding an island within a 10 km radius (Cox and Snell Pseudo‐R2 = 0.803). Avifaunal diversity is similar across recently submerged and unsubmerged small shelf islands. Main conclusion: Avifaunal diversity on tropical shelf islands equilibrates rapidly after isolation, indicating that both extinction and immigration rates are high. In particular, a high immigration rate of dispersive species maintains diversity, especially on small islands. Over‐water dispersal is generally restricted to short distances among Sundaic birds. Consequently, the diversity of an island can be maintained by the presence of large or stepping‐stone islands near it. [ABSTRACT FROM AUTHOR]

Theoretical ecologists have analysed a range of neutral models but few including stage structure. Here we introduce a stage‐structured neutral model, by extending the standard spatial neutral model to have two‐stage classes: a juvenile stage and a reproductive stage. We find that formulas for biodiversity patterns (e.g. species–area relationships and species abundance distributions) of reproductives in the stage‐structured model can be obtained from the corresponding standard formulas via a parameter rescaling, which involves calculating an effective speciation rate parameter and an effective dispersal parameter. This is useful because it means existing knowledge about the non‐stage‐structured model can be transferred to the stage‐structured model, providing that applications focus exclusively on the reproductive stage. One surprising implication is that the presence of a juvenile stage can substantially increase the species richness of reproductive individuals: a juvenile stage with a length fraction k that of the reproductive stage increases reproductive species richness by roughly the same factor. We apply our new formulas to a case study of tropical forest trees in Panama and find that while the stage‐structured model makes different predictions than the standard model, it does not fix known problems with cross‐scale predictions. We speculate that some of our results, in particular the result that the presence of a juvenile stage increases community diversity, likely apply to non‐neutral systems as well. [ABSTRACT FROM AUTHOR]

Bark contributes approximately 20% to the total above‐ground biomass of trees, yet bark is not properly accounted for when estimating carbon sequestered by trees. Current allometric functions estimate tree volume from diameter measured over the bark, and derive bark density and carbon content from estimates for wood. As the bark density of hardwood species is 40%–50% lower than the wood density, but nearly equivalent in conifers, bark carbon is overestimated for most species. The latter is further exacerbated by variation in bark volume with bark surface morphology.Fissured bark volume is overestimated by diameter over bark measurements by up to 40%. The vacant space in fissures can be accounted for by a bark fissure index (BFI). We calculate bark carbon for Australian species from a non‐destructive and effective BFI using bark thickness measured in the field.Bark volume, and in turn bark carbon, scaled inversely with tree size (diameter) so that bark volume comprised 42% of small trees (10 cm diameter at breast height, DBH) but 23% of large trees (50 cm DBH). Our BFI method using a bark thickness gauge (BGM) yielded similar results than using the less time‐efficient contour gauge method (CM) to estimate BFI (bias BGM‐CM −1.3%, non‐significant at p = 0.72). Both BGM and CM had an error of <4% compared to digitized BFI from destructive sampled stem disks. An average of 15 bark gauge measurements per tree estimated bark thickness (and inconsequence BFI) for both fissured and unfissured bark with <20% error relative to the exact value.Using the bark gauge method, BFI can be rapidly measured from large numbers of trees needed for estimating bark carbon at the community level and modelling carbon uptake, storage and cycling in woody biomes. [ABSTRACT FROM AUTHOR]

Bayesian methods have been developed for inferring the true year of extinction of a species from sighting records that have both certain and uncertain sightings. These methods typically make the restrictive assumption that all sighting types (i.e. certain, valid uncertain, invalid uncertain) derive from constant rate Poisson processes.In this study, the constant rate assumption in the homogeneous Poisson process is relaxed by allowing certain and uncertain sightings to follow independent non‐homogeneous Poisson processes. The model can thus identify whether any of the sighting rates were increasing, decreasing or constant. In addition, a change‐point is introduced to model the uncertain sightings, where the sighting rates before and after the change‐point vary.We have used Markov Chain Monte Carlo (MCMC) sampling to generate the posterior distributions for model parameters including species extinction time. The proposed method was applied to the sighting records of the black‐footed ferret Mustela nigripes and the ivory‐billed woodpecker Campephilus principalis (IBW) species.Based on a hypothesis test, the results of the model indicate that the species both went extinct in the years 1988 and 1956 respectively. Moreover, a decline in the certain sighting rate was also inferred for both these species, possibly indicating the decrease in the species abundance as it converges to extinction. Thus, earlier models that assume a constant sighting rate may well be biased. Uncertain sighting rates for the IBW were found to increase before extinction (indicating possibly some additional ecological attention received near extinction) and remained constant after extinction. [ABSTRACT FROM AUTHOR]

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Neutral theory proposes that some macroscopic biodiversity patterns can be explained in terms of drift, speciation and immigration, without invoking niches. There are many different varieties of neutral model, all assuming that the fitness of an individual is unrelated to its species identity. Variants that are spatially explicit provide a means for making quantitative predictions about spatial biodiversity patterns.We present software packages that make spatially explicit neutral simulations straightforward and efficient. The packages allow the user to customize both dispersal and landscape structure in a wide variety of ways. We provide a Python package pycoalescence and a functionally equivalent R package rcoalescence. In both packages, the core routines are written in C++ and make use of coalescence methods to optimize performance.We explain the technical details of the packages and give examples for their application, with a particular focus on two scenarios of ecological and evolutionary interest—a landscape with habitat fragmentation, and an archipelago of islands.Spatially explicit neutral models represent an important tool in ecology for understanding the processes of biodiversity generation and predicting outcomes at large scales. The effort required to implement these complex spatially explicit simulations efficiently has thus far been a barrier to entry. Our packages increase the accessibility of these models and encourage further investigation of the primary mechanisms underpinning biodiversity. [ABSTRACT FROM AUTHOR]

Leaf‐wood separation in terrestrial LiDAR data is a prerequisite for non‐destructively estimating biophysical forest properties such as standing wood volumes and leaf area distributions. Current methods have not been extensively applied and tested on tropical trees. Moreover, their impacts on the accuracy of subsequent wood volume retrieval were rarely explored.We present LeWoS, a new fully automatic tool to automate the separation of leaf and wood components, based only on geometric information at both the plot and individual tree scales. This data‐driven method utilizes recursive point cloud segmentation and regularization procedures. Only one parameter is required, which makes our method easily and universally applicable to data from any LiDAR technology and forest type.We conducted a twofold evaluation of the LeWoS method on an extensive dataset of 61 tropical trees. We first assessed the point‐wise classification accuracy, yielding a score of 0.91 ± 0.03 in average. Second, we evaluated the impact of the proposed method on 3D tree models by cross‐comparing estimates in wood volume and branch length with those based on manually separated wood points. This comparison showed similar results, with relative biases of less than 9% and 21% on volume and length respectively.LeWoS allows an automated processing chain for non‐destructive tree volume and biomass estimation when coupled with 3D modelling methods. The average processing time on a laptop was 90s for 1 million points. We provide LeWoS as an open‐source tool with an end‐user interface, together with a large dataset of labelled 3D point clouds from contrasting forest structures. This study closes the gap for stand volume modelling in tropical forests where leaf and wood separation remain a crucial challenge. [ABSTRACT FROM AUTHOR]

Positive species interactions are ubiquitous in natural communities, but the mechanisms through which they operate are poorly understood. One proposed mechanism is resource conversion – the conversion by a benefactor species of a resource from a resource state that is inaccessible to a potential beneficiary species into a resource state that is accessible. Such conversion often occurs as a byproduct of resource consumption, and sometimes in exchange for non‐resource benefits to the benefactor species. At least five known classes of interactions, including both facilitative and mutualistic ones, may be classified as resource conversion interactions. We formulated a generalizable mathematical model for resource conversion interactions and examined two model variants that represent processing chain and nurse plant interactions. We examined the conditions under which these conformed to the stress‐gradient hypothesis (SGH), which predicts increased interaction benefits in more stressful environments. These yielded four key insights: 1) resource conversion interactions can be positive (towards the resource recipient) only when facilitator‐mediated resource conversion is more efficient than the baseline, spontaneous, facilitator‐independent resource conversion; 2) the sign of resource conversion interaction outcomes never switches (e.g. from net positive to net negative) with changing levels of resource availability, when all other parameters are kept constant; 3) processing chain interactions at equilibrium can never be positive in a manner that conforms to the SGH; 4) nurse plant interactions can be positive and conform to the SGH, although the manner in which they do depends largely on how resource stress is defined, and the environmental supply rate of surface soil moisture. The first two insights are likely to be generalizable across all resource conversion interactions. The general agreement of the model with empirical studies suggest that resource conversion is the mechanism underlying the aforementioned interactions, and an ecologically meaningful way of classifying these previously unassociated positive species interactions. [ABSTRACT FROM AUTHOR]

The coexistence of many species within ecological communities poses a long‐standing theoretical puzzle. Modern coexistence theory (MCT) and related techniques explore this phenomenon by examining the chance of a species population growing from rarity in the presence of all other species. The mean growth rate when rare, E[r], is used in MCT as a metric that measures persistence properties (like invasibility or time to extinction) of a population. Here we critique this reliance on E[r] and show that it fails to capture the effect of temporal random abundance variations on persistence properties. The problem becomes particularly severe when an increase in the amplitude of stochastic temporal environmental variations leads to an increase in E[r], since at the same time it enhances random abundance fluctuations and the two effects are inherently intertwined. In this case, the chance of invasion and the mean extinction time of a population may even go down as E[r] increases. [ABSTRACT FROM AUTHOR]

Among the local processes that determine species diversity in ecological communities, fluctuation‐dependent mechanisms that are mediated by temporal variability in the abundances of species populations have received significant attention. Higher temporal variability in the abundances of species populations can increase the strength of temporal niche partitioning but can also increase the risk of species extinctions, such that the net effect on species coexistence is not clear. We quantified this temporal population variability for tree species in 21 large forest plots and found much greater variability for higher latitude plots with fewer tree species. A fitted mechanistic model showed that among the forest plots, the net effect of temporal population variability on tree species coexistence was usually negative, but sometimes positive or negligible. Therefore, our results suggest that temporal variability in the abundances of species populations has no clear negative or positive contribution to the latitudinal gradient in tree species richness. [ABSTRACT FROM AUTHOR]

In a critique of our recent review on measuring habitat complexity in ecology, Madin et al. (2023) advocate the use of fractal dimension in ecology and defend their geometric constraint theory of habitat complexity. We explain the flaws in their arguments and highlight points where they misinterpreted our statements. [ABSTRACT FROM AUTHOR]

Habitat loss leads to species extinctions, both immediately and over the long term as 'extinction debt' is repaid. The same quantity of habitat can be lost in different spatial patterns with varying habitat fragmentation. How this translates to species loss remains an open problem requiring an understanding of the interplay between community dynamics and habitat structure across temporal and spatial scales. Here we develop formulas that characterise extinction debt in a spatial neutral model after habitat loss and fragmentation. Central to our formulas are two new metrics, which depend on properties of the taxa and landscape: 'effective area', measuring the remaining number of individuals and 'effective connectivity', measuring individuals' ability to disperse through fragmented habitat. This formalises the conventional wisdom that habitat area and habitat connectivity are the two critical requirements for long‐term preservation of biodiversity. Our approach suggests that mechanistic fragmentation metrics help resolve debates about fragmentation and species loss. [ABSTRACT FROM AUTHOR]

Symbiotic nitrogen (N)‐fixing trees can provide large quantities of new N to ecosystems, but only if they are sufficiently abundant. The overall abundance and latitudinal abundance distributions of N‐fixing trees are well characterised in the Americas, but less well outside the Americas.Here, we characterised the abundance of N‐fixing trees in a network of forest plots spanning five continents, ~5,000 tree species and ~4 million trees. The majority of the plots (86%) were in America or Asia. In addition, we examined whether the observed pattern of abundance of N‐fixing trees was correlated with mean annual temperature and precipitation.Outside the tropics, N‐fixing trees were consistently rare in the forest plots we examined. Within the tropics, N‐fixing trees were abundant in American but not Asian forest plots (~7% versus ~1% of basal area and stems). This disparity was not explained by mean annual temperature or precipitation. Our finding of low N‐fixing tree abundance in the Asian tropics casts some doubt on recent high estimates of N fixation rates in this region, which do not account for disparities in N‐fixing tree abundance between the Asian and American tropics.Synthesis. Inputs of nitrogen to forests depend on symbiotic nitrogen fixation, which is constrained by the abundance of N‐fixing trees. By analysing a large dataset of ~4 million trees, we found that N‐fixing trees were consistently rare in the Asian tropics as well as across higher latitudes in Asia, America and Europe. The rarity of N‐fixing trees in the Asian tropics compared with the American tropics might stem from lower intrinsic N limitation in Asian tropical forests, although direct support for any mechanism is lacking. The paucity of N‐fixing trees throughout Asian forests suggests that N inputs to the Asian tropics might be lower than previously thought. [ABSTRACT FROM AUTHOR]

Probabilistic modelling of gaps for light–canopy interactions has long served as a theoretical basis to estimate vegetation structural parameters—leaf area index (LAI) and leaf angle distribution (LAD)—from optical measurements such as hemispherical photos. Direct inversion of such probabilistic models provides a reliable statistical algorithm for parameter estimation, but this inferential paradigm has been seldom explored. Even worse, many classical LAI algorithms implicitly assume "wrong" statistical models inconsistent with the underlying probabilistic gap models—a subtle issue not articulated before but known to cause practical issues.Here, we clarified how to improve LAI and LAD estimation by directly inverting binary gap/non‐gap data of hemispherical photos via binary nonlinear regression (BNR). We implemented the new BNR method and some classical algorithms in an R package "hemiphoto2LAI", comprising a total of 135 models for LAI estimation.Compared to classical algorithms, BNR features many theoretical advantages and allows estimating LAI and LAD simultaneously. BNR can address questions difficult to answer by classical algorithms (e.g. how better is one LAD than another?). We demonstrated the utility of the BNR paradigm based on both synthetic and real data.Overall, BNR is statistically more justifiable but its potential has been under‐appreciated. We encourage the community to embrace this new paradigm for reliable analyses of hemispherical photos or other gap data for canopy research. [ABSTRACT FROM AUTHOR]

To understand long‐term ecosystem dynamics, several concepts have been recently proposed that consider 'basins of attraction' to express resilience and 'tipping points' that express sharp change in an ecosystem's behaviour. However, these temporal features remain difficult to identify and quantify because current models usually focus only on a part of the whole ecosystem behaviour, whereas a holistic approach should be preferred.We propose an original family of models based on discrete systems and designed to comprehensively characterize ecosystem dynamics holistically and over the long term. We developed a qualitative model based on Petri nets, made up of a relational graph (interaction network) that was then rigorously handled using transition rules. Unlike traditional modelling and graph theory approaches, transition rules can strongly modify the graph structure (i.e. a dynamic topology occurs).We examined the value of Petri nets when applied to the simple ecosystem of a termite colony. A termite colony comprises of abiotic and biotic components and processes that we explored along all of their possible trajectories.Several temporal features, such as basins of attraction (i.e. strongly connected states), tipping points (critical transitions along trajectories) and various kinds of collapses (functioning systems whose structures were nevertheless fixed), were easily detected and quantified. We propose that Petri nets developed for more complex ecosystems will provide original insights into their holistic behaviour. [ABSTRACT FROM AUTHOR]

Diversity partitioning, which decomposes gamma diversity into alpha and beta components, is commonly used to obtain measures that quantify spatial/temporal diversity and compositional similarity or dissimilarity among assemblages. We focus on the decomposition of diversity as measured by Hill numbers (parameterized by a diversity order q ≥ 0).At least three diversity‐partitioning schemes based on Hill numbers have been proposed. These schemes differ in the way they formulate alpha diversity. We focus on comparing two classes of alpha diversities, developed, respectively, by Routledge (1979) and Chiu et al. (2014). Both are defined for all diversity orders q ≥ 0. Because these two approaches to quantifying alpha have not been compared in the literature; it has been unclear how to choose a proper alpha formulation for practical applications.We review the two classes of alpha diversities and discuss the properties of their corresponding beta and (dis)similarity measures. Our research offers clear guidelines regarding the choice of an alpha formula: (a) If the goal is to assess compositional (dis)similarity among (unweighted) species relative abundance datasets, then the two alpha formulas are identical, leading to the same beta and (dis)similarity measures. (b) If the goal is to assess compositional (dis)similarity among (unweighted) species raw abundance datasets, then Chiu et al.'s approach should be used. Their beta can be monotonically transformed to various (dis)similarity measures in the range [0, 1]. (c) If each assemblage is weighted by its absolute, total abundance (i.e. assemblage size), but the goal is to assess compositional (dis)similarity among species relative abundance datasets, then Routledge's approach should be used. In this case, construction of legitimate (dis)similarity measures among species relative abundance datasets for unequal assemblage sizes/weights, for any order q ≥ 0, has not been addressed in the literature. Here we propose non‐monotonic transformations of Routledge's beta to fill this gap.The extension of our analysis to phylogenetic diversity partitioning is generally parallel. We apply various species/taxonomic and phylogenetic dissimilarity measures to Taiwan's plant data; the results provide insights into the assessment of a reintroduction programme of Formosan sika deer into a forest area. Pertinent sampling and related issues are also discussed. [ABSTRACT FROM AUTHOR]

Models for predicting ecological behaviour typically require large volumes of data for parameterisation, which is a problem because data are scarce. Qualitative modelling (QM) provides an alternative by exploring the entire range of possible parameter values. When a parameter value is completely unknown, QM typically invokes the Principle of Indifference (PoI), for example by sampling the parameter from a uniform prior distribution. However, if PoI is invoked in this probabilistic way, there may be multiple possible methods for defining a parameter space and sampling values from it, and, worryingly, two different but equally defensible methods can lead to different predictions about ecosystem responses.We investigated how probabilistic PoI can give rise to problems in QM, and developed an alternative method based on Boolean PoI that does not suffer the same limitations. We used a case study that involved predicting the responses of multiple species to the suppression of a pest. The unknown model parameters were interaction strengths between species. For the standard probabilistic method, we drew the parameters randomly from uniform (PoI) and other distributions. For our new Boolean PoI method, we instead simply specified the ranges of "possible" parameter values, and developed a Boolean analysis technique to summarise model predictions.As expected, invoking probabilistic QM yielded different predictions (species‐response probabilities) for different but equally defensible parameterisation and sampling schemes. Sometimes differences were large enough to impact decision‐making. In contrast, our new Boolean PoI approach simply classifies outcomes (species responses) as certain, possible, or impossible. Encouragingly, some species responses that were not consistently resolved under probabilistic PoI were shown by our method to be in fact governed by simple rules. Our method can also identify key species whose responses determine whole‐system outcomes.Our non‐probabilistic representation of uncertainty circumvents the philosophical problems in standard implementations of PoI for QM. Our Boolean analysis method summarises results in a way that is interpretable and potentially useful to conservation decision makers. A priority for future research is to increase the efficiency of our Boolean approach to allow it to deal with problems of higher complexity (more interactions). [ABSTRACT FROM AUTHOR]

Abstract: Local adaptation to rare habitats is difficult due to gene flow, but can occur if the habitat has higher productivity. Differences in offspring phenotypes have attracted little attention in this context. We model a scenario where the rarer habitat improves offspring's later competitive ability – a carryover effect that operates on top of local adaptation to one or the other habitat type. Assuming localised dispersal, so the offspring tend to settle in similar habitat to the natal type, the superior competitive ability of offspring remaining in the rarer habitat hampers immigration from the majority habitat. This initiates a positive feedback between local adaptation and trait divergence, which can thereafter be reinforced by coevolution with dispersal traits that match ecotype to habitat type. Rarity strengthens selection on dispersal traits and promotes linkage disequilibrium between locally adapted traits and ecotype‐habitat matching dispersal. We propose that carryover effects may initiate isolation by ecology. [ABSTRACT FROM AUTHOR]

Abstract: To estimate species loss from habitat destruction, ecologists typically use species–area relationships, but this approach neglects the spatial pattern of habitat fragmentation. Here, we provide new, easily applied, analytical methods that place upper and lower bounds on immediate species loss at any spatial scale and for any spatial pattern of habitat loss. Our formulas are expressed in terms of what we name the ‘Preston function’, which describes triphasic species–area relationships for contiguous regions. We apply our method to case studies of deforestation and tropical tree species loss at three different scales: a 50 ha forest plot in Panama, the tropical city‐state of Singapore and the Brazilian Amazon. Our results show that immediate species loss is somewhat insensitive to fragmentation pattern at small scales but highly sensitive at larger scales: predicted species loss in the Amazon varies by a factor of 16 across different spatial structures of habitat loss. [ABSTRACT FROM AUTHOR]

1. The ability to provide reliable projections for the current and future distribution of land- cover is fundamental if we wish to protect and manage our diminishing natural resources. Two inter- related revolutions make map productions feasible at unprecedented resolutions--the availability of high- resolution remotely sensed data and the development of machine- learning algorithms. However, ground- truthed data needed for training models is in most cases spatially and temporally clustered. Therefore, map production requires extrapolation of models from one place to another and the uncertainty cost of such extrapolation is rarely explored. In other words, the focus has mainly been on projections, and less on quantifying their reliability. 2. Using the concept of "forecast horizon", we suggest that the predictability of landcover classification models should be quantitatively explored as a continuum against distances measured along multiple dimensions--space, time, environmental and spectral. Focusing on ten agricultural sites from England and using models specifically designed to predict multivariate decay- curves, we ask: how does a model's predictive performance decay with distance? More specifically, we explored if we could predict the proficiency (kappa statistics) of a model trained in one site when making predictions in another site based on the spatial, temporal, spectral and environmental distances between sites. 3. We found that model proficiency decays with distance between sites in each dimension. More importantly, we found for the first time, that it is possible to predict the performance a model transferred to or from a novel site will have, based on its distances from known sites. The spectral distance variables were the most important when predicting model transferability. 4. Exploring model transferability as a continuum may have multiple usages including predicting uncertainty values in space and time, prioritisation of strategies for ground- truth data collection, and optimising model characteristics for defined tasks. [ABSTRACT FROM AUTHOR]

Species management requires decision-making under uncertainty. Given a management objective and limited budget, managers need to decide what to do, and where and when to do it. A schedule of management actions that achieves the best performance is an optimal policy. A popular optimisation technique used to find optimal policies in ecology and conservation is stochastic dynamic programming ( SDP). Most SDP approaches can only accommodate actions of equal durations. However, in many situations, actions take time to implement or cannot change rapidly. Calculating the optimal policy of such problems is computationally demanding and becomes intractable for large problems. Here, we address the problem of implementing several actions of different durations simultaneously., We demonstrate analytically that synchronising actions and their durations provide upper and lower bounds of the optimal performance. These bounds provide a simple way to evaluate the performance of any policy, including rules of thumb. We apply this approach to the management of a dynamic ecological network of Aedes albopictus, an invasive mosquito that vectors human diseases. The objective is to prevent mosquitoes from colonising mainland Australia from the nearby Torres Straits Islands where managers must decide between management actions that differ in duration and effectiveness., We were unable to compute an optimal policy for more than eight islands out of 17, but obtained upper and lower bounds for up to 13 islands. These bounds are within 16% of an optimal policy. We used the bounds to recommend managing highly populated islands as a priority., Our approach calculates upper and lower bounds for the optimal policy by solving simpler problems that are guaranteed to perform better and worse than the optimal policy respectively. By providing bounds on the optimal solution, the performance of policies can be evaluated even if the optimal policy cannot be calculated. Our general approach can be replicated for problems where simultaneous actions of different durations need to be implemented. [ABSTRACT FROM AUTHOR]

Assessing the consequences of biodiversity changes for ecosystem functioning requires separating the net effect of biodiversity from potential confounding effects such as the identity of the gained or lost species. Additive partitioning methods allow factoring out these species identify effects by comparing species' functional contributions against the predictions of a null model under which functional contributions are independent of biodiversity., Classic additive partitioning methods quantify biodiversity effects based on a linear relationship between species deviations from the null model and their functional traits. However, based on ecological theory, nonlinear relationships are also possible., Here, we demonstrate how additive-partitioning methods can be extended to describe such nonlinear relationships, and explain how nonlinear biodiversity effects can be interpreted., We apply both linear and nonlinear partitioning methods to the Cedar Creek Biodiversity II experiment. Nonlinear relationships were detected in the majority of plots, and increased with diversity. Nonlinear partitioning thereby identified a convex relationship between species functional traits and their deviations from the null model, driven by strong positive effects of both species with low and high functional trait values trait values on ecosystem functioning., The presented nonlinear extension of additive partitioning methods is therefore essential for revealing more complex biodiversity effects on ecosystem functioning, that are likely to occur in biodiversity experiments. [ABSTRACT FROM AUTHOR]

Ecosystem-based management requires predictive models of ecosystem dynamics. There are typically insufficient empirical data available to parameterise these complex models, and so decision-makers commonly rely on beliefs elicited from experts. However, such expert beliefs are necessarily limited because (i) only a small proportion of ecosystem components and dynamics have been observed; (ii) uncertainty about ecosystem dynamics can result in contradictory expert judgements and (iii) elicitation time and resources are limited., We use an ensemble of dynamic ecosystem models to extrapolate a limited set of stated expert beliefs into a wider range of revealed beliefs about how the ecosystem will respond to perturbations and management. Importantly, the method captures the expert uncertainty and propagates it through to predictions. We demonstrate this process and its potential value by applying it to the conservation of the threatened malleefowl ( Leipoa ocellata) in the Murray mallee ecosystems of southern Australia., In two workshops, we asked experts to construct a qualitative ecosystem interaction network and to describe their beliefs about how the ecosystem will respond to particular perturbations. We used this information to constrain an ensemble of 109 community models, leaving a subset that could reproduce stated expert beliefs. We then interrogated this ensemble of models to reveal experts' implicit beliefs about management scenarios that were not a part of the initial elicitation exercises., Our method uses straightforward questions to efficiently elicit expert beliefs, and then applies a flexible modelling approach to reveal those experts' beliefs about the dynamics of the entire ecosystem. It allows rapid planning of ecosystem-based management informed by expert judgement, and provides a basis for value-of-information analyses and adaptive management. [ABSTRACT FROM AUTHOR]

The comparable and integrated nature of plant functional types and advances in high-spectral-resolution remote sensing techniques (i.e. imaging spectroscopy) make their combination highly interesting for spatially continuous and repeatable large-scale ecosystem monitoring. Depending on physical environment and stress, plants invest in covarying biochemical and structural traits, influencing spectral characteristics of vegetation. These traits are assumed to bear a more direct causal relationship to plant functional types than to plant life/growth forms. However, the connection between a vegetation community's functional and spectral response remains to be established., We assessed the correlation structure between (i) biochemical and structural vegetation traits (biomass, dry matter content, nitrogen content, neutral detergent fibre content), (ii) plant life/growth forms and (iii) seven plant functional types of two categories (strategy types, indicator values) collected in heterogeneous alpine grassland. We then used airborne imaging spectroscopy data from the same area to model and predict plant life/growth forms and plant functional types at the vegetation community level using partial least squares regression and validated our models based on an independent data set., We found high correlations between many of the biochemical and structural vegetation traits, plant life/growth forms and plant functional types tested. Using airborne imaging spectroscopy data, we successfully modelled and predicted most plant life/growth forms ( R2 max. = 0·56) and all plant functional types ( R2 max. = 0·62). However, model performance for plant life/growth forms decreased substantially during external validation and overall model consistency was low (average change in R2 = 72%), while plant functional type models were much more consistent (average change in R2 = 20%). Based on our findings, we developed a conceptual framework using the theory and methodology of vegetation ecology and imaging spectroscopy to link the vegetation community's functional to its spectral signature., Our results encourage the use of plant functional types in imaging spectroscopy in order to aid the large-scale monitoring of ecosystems, which is particularly important given the increased availability of airborne data and the prospective launches of spaceborne instruments in the near future. [ABSTRACT FROM AUTHOR]

To generate realistic predictions, species distribution models require the accurate coregistration of occurrence data with environmental variables. There is a common assumption that species occurrence data are accurately georeferenced; however, this is often not the case. This study investigates whether locational uncertainty and sample size affect the performance and interpretation of fine-scale species distribution models., This study evaluated the effects of locational uncertainty across multiple sample sizes by subsampling and spatially degrading occurrence data. Distribution models were constructed for kelp ( Ecklonia radiata), across a large study site (680 km2) off the coast of southeastern Australia. Generalized additive models were used to predict distributions based on fine-resolution (2·5 m cell size) seafloor variables, generated from multibeam echosounder data sets, and occurrence data from underwater towed video. The effects of different levels of locational uncertainty in combination with sample size were evaluated by comparing model performance and predicted distributions., While locational uncertainty was observed to influence some measures of model performance, in general this was small and varied based on the accuracy metric used. However, simulated locational uncertainty caused changes in variable importance and predicted distributions at fine scales, potentially influencing model interpretation. This was most evident with small sample sizes., Results suggested that seemingly high-performing, fine-scale models can be generated from data containing locational uncertainty, although interpreting their predictions can be misleading if the predictions are interpreted at scales similar to the spatial errors. This study demonstrated the need to consider predictions across geographic space rather than performance alone. The findings are important for conservation managers as they highlight the inherent variation in predictions between equally performing distribution models, and the subsequent restrictions on ecological interpretations. [ABSTRACT FROM AUTHOR]

How many species have gone extinct in modern times before being described by science? To answer this question, and thereby get a full assessment of humanity's impact on biodiversity, statistical methods that quantify undetected extinctions are required. Such methods have been developed recently, but they are limited by their reliance on parametric assumptions; specifically, they assume the pools of extant and undetected species decay exponentially, whereas real detection rates vary temporally with survey effort and real extinction rates vary with the waxing and waning of threatening processes. We devised a new, nonparametric method for estimating undetected extinctions. As inputs, the method requires only the first and last date at which each species in an ensemble was recorded. As outputs, the method provides estimates of the proportion of species that have gone extinct, detected, or undetected and, in the special case where the number of undetected extant species in the present day is assumed close to zero, of the absolute number of undetected extinct species. The main assumption of the method is that the per-species extinction rate is independent of whether a species has been detected or not. We applied the method to the resident native bird fauna of Singapore. Of 195 recorded species, 58 (29.7%) have gone extinct in the last 200 years. Our method projected that an additional 9.6 species (95% CI 3.4, 19.8) have gone extinct without first being recorded, implying a true extinction rate of 33.0% (95% CI 31.0%, 36.2%). We provide R code for implementing our method. Because our method does not depend on strong assumptions, we expect it to be broadly useful for quantifying undetected extinctions. [ABSTRACT FROM AUTHOR]

The sum of Akaike weights (SW) is often used to quantify relative variable importance (RVI) within the information-theoretic (IT) multimodel inference framework. A recent study (Galipaud et al. 2014, Methods in Ecology and Evolution 5: 983) questioned the validity of the SW approach. Regrettably, this study is flawed because SW was evaluated with an inappropriate benchmark. Irrespective of this study's methodological issues, RVI metrics based on the relative contribution of explanatory variables in explaining the variance in the response variable (partitioned R2-based) are lacking in multimodel inference., We re-evaluated the validity of SW by repeating Galipaud et al.'s experiment but with an appropriate benchmark. When explanatory variables are uncorrelated, the quantity that SW estimates (i.e. the probability that a variable is included in the actual best IT model) is monotonically related to squared zero-order correlation coefficients ( r2) between explanatory variables and the response variable. The degree of correspondence between SW and r2 rankings (not values) of variables in data sets with uncorrelated explanatory variables was therefore used as a benchmark to evaluate the validity of SW as a RVI metric. To address the lack of partitioned R2-based RVI metrics in multimodel inference, we proposed 2 metrics: (a) I weighted, the average model probability-weighted partitioned R2; and (b) I best, the partitioned R2 derived from the best IT model. We performed Monte Carlo simulations to evaluate the utility of I weighted and I best versus partitioned R2 derived from the global model ( I global)., SW rankings matched r2 rankings of variables; therefore, SW is a valid measure of RVI. Among partitioned R2-based metrics, I weighted and I global were generally more accurate in estimating the population partitioned R2. I weighted performed better when explanatory variables were uncorrelated, whereas I global was better in smaller data sets with correlated explanatory variables. To improve the utility of I weighted in such data sets, we proposed approaches to eliminate or reduce the influence of correlated variables., Despite recent criticisms, our results show that SW is a valid RVI metric. To quantify RVI in terms of the R2 explained by each variable, I weighted and I global are the preferred RVI metrics. [ABSTRACT FROM AUTHOR]

The compositional complexity of an assemblage is not expressible as a single number; standard measures such as diversities (Hill numbers) and entropies (Rényi entropies and Tsallis entropies) vary in their order q which determines the measures' emphasis on rare or common species. Ranking and comparing assemblages depend on the choice of q. Rather than selecting one or a few measures to describe an assemblage, it is preferable to convey the complete story by presenting a continuous profile, a plot of diversity or entropy as a function of q ≥ 0. This makes it easy to visually compare the compositional complexities of multiple assemblages and to judge the evenness of the relative abundance distributions of the assemblages. In practice, the profile is plotted for all values of q from 0 to q = 3 or 4 (beyond which it generally changes little)., These profiles are usually generated by substituting species sample proportions into the complexity measures. However, this empirical approach typically underestimates the true profile for low values of q, because samples usually miss some of the assemblage's species due to under-sampling. Although bias-reduction methods exist for individual measures of order q = 0, 1 and 2, there has been no analytic method that unifies these bias-corrected estimates into a continuous profile., For incomplete sampling data, this work proposes a novel analytic method to obtain accurate, continuous, low-bias diversity and entropy profiles with focus on low orders of q (0 ≤ q ≤ 3). Our approach is based on reformulating the diversity and entropy of any order q in terms of the successive discovery rates of new species with respect to sample size, that is the successive slopes of the species accumulation curve. A bootstrap method is applied to obtain approximate variances of our proposed profiles and to construct the associated confidence intervals., Extensive simulations from theoretical models and real surveys show that the proposed profiles greatly reduce under-sampling bias and have substantially lower bias and mean-squared error than the empirical profile, especially for q ≤ 1. Our method is also extended to deal with incidence data. [ABSTRACT FROM AUTHOR]

Many questions in ecology and evolutionary biology consider response variables that are functions (e.g. species-abundance distributions) rather than a single scalar value (e.g. species richness). Although methods for analysing function-valued data have been available for several decades, ecological and evolutionary applications are rare., We outline methods for regression when the response variable is a function ('function regression') and introduce the r package FREE, which focuses on straightforward implementation and interpretation of function regression analyses. Several computational methods are implemented, including machine learning and several Bayesian methods. We compare different methods using simulated data and real ecological data on individual-size distributions ( ISDs) of fish and trees., No single method performed best overall, with several performing equally well for a given data set. Which method to use depends on sample sizes and the questions being considered; in many cases, a consensus approach should be used to combine or compare fitted models., Function regression allows the direct modelling of many function-valued data (e.g. species-abundance distributions) rather than having to reduce those functions to a single scalar response variable (e.g. species diversity or functional diversity indices). Our ecological examples using ISD data show that larger rivers support more-even fish-size distributions than smaller rivers and that low initial planting densities lead to more-even tree-size distributions than high initial planting densities. Function regression provided more informative and intuitive interpretations of these data than conventional non-function-valued approaches. [ABSTRACT FROM AUTHOR]

The α-, β-, γ-diversity decomposition methodology is commonly used to investigate changes in diversity over space or time but rarely conjointly. However, with the ever-increasing availability of large-scale biodiversity monitoring data, there is a need for a sound methodology capable of simultaneously accounting for spatial and temporal changes in diversity., Using the properties of Chao's index, we adapted Rao's framework of diversity decomposition between orthogonal dimensions to a multiplicative α-, β-, γ-decomposition of functional or phylogenetic diversity over space and time, thereby combining their respective properties. We also developed guidelines for interpreting both temporal and spatial β-diversities and their interaction., We characterized the range of β-diversity estimates and their relationship to the nested decomposition of diversity. Using simulations, we empirically demonstrated that temporal and spatial β-diversities are independent from each other and from α- and γ-diversities when the study design is balanced, but not otherwise. Furthermore, we showed that the interaction term between the temporal and the spatial β-diversities lacked such properties., We illustrated our methodology with a case study of the spatio-temporal dynamics of functional diversity in bird assemblages in four regions of France. Based on these data, our method makes it possible to discriminate between regions experiencing different diversity changes in time. Our methodology may therefore be valuable for comparing diversity changes over space and time using large-scale data sets of repeated surveys. [ABSTRACT FROM AUTHOR]

Individual species are distributed inhomogeneously over space and time, yet, within large communities of species, aggregated patterns of biodiversity seem to display nearly universal behaviour. Neutral models assume that an individual's demographic prospects are independent of its species identity. They have successfully predicted certain static, time-independent patterns. But they have generally failed to predict temporal patterns, such as species ages or population dynamics. We construct a new, multispecies framework incorporating competitive differences between species, and assess the impact of this competition on static and dynamic patterns of biodiversity. We solve this model exactly for the special case of a Red Queen hypothesis, where fitter species are continually arising. The model predicts more realistic species ages than neutral models, without greatly changing predictions for static species abundance distributions. Our modelling approach may allow users to incorporate a broad range of ecological mechanisms. [ABSTRACT FROM AUTHOR]

Long-term surveys of entire communities of species are needed to measure fluctuations in natural populations and elucidate the mechanisms driving population dynamics and community assembly. We analysed changes in abundance of over 4000 tree species in 12 forests across the world over periods of 6-28 years. Abundance fluctuations in all forests are large and consistent with population dynamics models in which temporal environmental variance plays a central role. At some sites we identify clear environmental drivers, such as fire and drought, that could underlie these patterns, but at other sites there is a need for further research to identify drivers. In addition, cross-site comparisons showed that abundance fluctuations were smaller at species-rich sites, consistent with the idea that stable environmental conditions promote higher diversity. Much community ecology theory emphasises demographic variance and niche stabilisation; we encourage the development of theory in which temporal environmental variance plays a central role. [ABSTRACT FROM AUTHOR]

Artificial illumination of the night is increasing globally. There is growing evidence of a range of ecological impacts of artificial light and awareness of light pollution as a significant environmental issue. In urban and suburban areas, complex spatial patterns of light sources, structures and vegetation create a highly heterogeneous night-time light environment for plants and animals., We developed a method for modelling the night-time light environment at a high spatial resolution in a small urban area for ecological studies. We used the position and height of street lights, and digital terrain and surface models, to predict the direct light intensity at different wavelengths at different heights above the ground surface., Validation against field measurements of night-time light showed that modelled light intensities in the visible and ultraviolet portions of the spectrum were accurate., We show how this model can be used to map biologically relevant lightscapes across an urban landscape. We also illustrate the utility of the model using night-time light maps as resistance surfaces in the software package circuitscape to predict potential movement of model nocturnal species between habitat patches and to identify key corridors and barriers to movement and dispersal., Understanding the ecological effects of artificial light requires knowledge of the light environment experienced by organisms throughout the diurnal and annual cycles, during periods of activity and rest and during different life stages. Our approach to high-resolution mapping of artificial lightscapes can be adapted to the sensitivity to light of different species and to other urban, suburban, rural and industrial landscapes. [ABSTRACT FROM AUTHOR]

The relationship between species richness and ecosystem function, as measured by productivity or biomass, is of long-standing theoretical and practical interest in ecology. This is especially true for forests, which represent a majority of global biomass, productivity and biodiversity., Here, we conduct an analysis of relationships between tree species richness, biomass and productivity in 25 forest plots of area 8-50 ha from across the world. The data were collected using standardized protocols, obviating the need to correct for methodological differences that plague many studies on this topic., We found that at very small spatial grains (0.04 ha) species richness was generally positively related to productivity and biomass within plots, with a doubling of species richness corresponding to an average 48% increase in productivity and 53% increase in biomass. At larger spatial grains (0.25 ha, 1 ha), results were mixed, with negative relationships becoming more common. The results were qualitatively similar but much weaker when we controlled for stem density: at the 0.04 ha spatial grain, a doubling of species richness corresponded to a 5% increase in productivity and 7% increase in biomass. Productivity and biomass were themselves almost always positively related at all spatial grains., Synthesis. This is the first cross-site study of the effect of tree species richness on forest biomass and productivity that systematically varies spatial grain within a controlled methodology. The scale-dependent results are consistent with theoretical models in which sampling effects and niche complementarity dominate at small scales, while environmental gradients drive patterns at large scales. Our study shows that the relationship of tree species richness with biomass and productivity changes qualitatively when moving from scales typical of forest surveys (0.04 ha) to slightly larger scales (0.25 and 1 ha). This needs to be recognized in forest conservation policy and management. [ABSTRACT FROM AUTHOR]

Carbon credits are a potential source of funding for restoration initiatives that contribute to achieving conservation targets in important biodiversity areas. Here we investigated whether fallowing sequesters carbon; a first step in assessing the viability of using carbon financing to promote restoration of threatened vegetation in agricultural landscapes. We used renosterveld, a critically endangered shrubland vegetation of the Cape Floristic Region, as a case study. Carbon stocks of soil and biomass in active fields, fallow fields and intact renosterveld were compared. The total carbon stocks measured in fallow fields (82 Mg C ha−1) show that fallowing can sequester carbon lost in the conversion from intact renosterveld (84 Mg C ha−1) to active fields (69 Mg C ha−1) and that revenues of US$ 10 - 48 ha−1 yr−1 from carbon credits could accrue. Our findings suggest that carbon financing could be used to incentivise ecological restoration in marginal agricultural landscapes. [ABSTRACT FROM AUTHOR]

The article presents a study regarding the use of info-gap theory to choose between two alternative land uses such as the establishment of an exotic timber plantation and native vegetation conservation. The study made use the linear equations by ordinary least-squares regression to define the reward functions for the two alternative land-use decisions. The result of the study shows the sensitivity of the info-gap analyses to the choice of uncertainty model.

The critical weight range (CWR) hypothesis for Australian mammals states that extinctions and declines have been concentrated in species with body mass between 35 g and 5.5 kg. The biological basis for this hypothesis is that species of intermediate size are disproportionately impacted by introduced predators. The CWR hypothesis has received support from several statistical studies over the past decade, although the evidence is weaker or non-existent for certain groups such as mesic-zone mammals and arboreal mammals. In this study, we employ an information-theoretic model selection approach to gain further insights into the relationship between body mass and extinction risk in Australian mammals. We find evidence, consistent with the CWR hypothesis, that extinction risk peaks at intermediate body masses for marsupials, rodents and ground-dwelling species, but not for arboreal species. In contrast to previous studies, we find that the CWR describes extinction patterns in the mesic zone as well as the arid zone. In the mesic zone, there is also a weaker tendency for large species above the CWR to be more vulnerable, consistent with extinction patterns on other continents. We find that a more biological plausible Gaussian distribution consistently fits the data better than the polynomial models that have been used in previous studies. Our results justify conservation programmes targeted at species within the CWR across Australia. [ABSTRACT FROM AUTHOR]

In the classic spatially implicit formulation of Hubbell’s neutral theory of biodiversity a local community receives immigrants from a metacommunity operating on a relatively slow timescale, and dispersal into the local community is governed by an immigration parameter m. A current problem with neutral theory is that m lacks a clear biological interpretation. Here, we derive analytical expressions that relate the immigration parameter m to the geometry of the plot defining the local community and the parameters of a dispersal kernel. Our results facilitate more rigorous and extensive tests of the neutral theory: we conduct a test of neutral theory by comparing estimates of m derived from fits to empirical species abundance distributions to those derived from dispersal kernels and find acceptable correspondence; and we generate a new prediction of neutral theory by investigating how the shapes of species abundance distributions change theoretically as the spatial scale of observation changes. We also discuss how our main analytical results can be used to assess the error in the mean-field approximations associated with spatially implicit formulations of neutral theory. [ABSTRACT FROM AUTHOR]

The critical weight range hypothesis for Australian terrestrial mammals states that species in the intermediate size range 35–5500 g are particularly susceptible to extinction. In a 2001 study Cardillo and Bromham found no statistically significant evidence for this hypothesis and suggested that research should instead focus on why small species are resistant to extinction. We used a similar data set of body sizes of Australian mammals grouped by conservation classification, but we used test statistics (mean deviation above and below the median body size) that are more statistically powerful than those of Cardillo and Bromham (quartiles, maxima, and minima of body size distributions). We found strong evidence in favor of the critical weight range hypothesis: the body size distribution of threatened species was more clustered toward the median body size from above and below. This pattern was statistically significant at the continental scale and in the arid zone, but not in the mesic zone. Confusing statistical significance with evidence of no effect, as Cardillo and Bromham did, can have negative implications for conservation biology because it can result in failure to act when action is warranted or provision of incorrect advice that affects policy and planning decisions. [ABSTRACT FROM AUTHOR]

The article focuses on a study related to incorporating landscape dynamics into population viability analysis (PVA). Landscape stochasticity contributes much to the variance of model outputs in some PVAs on stochastic dynamic landscapes. A dynamic landscape metapopulation (DLMP) model of the Brown Creeper in the boreal forests of northern Ontario was used in the study. It was found that in case of this model, most of the variance in the expected minimum population size (EMP) is associated with population stochasticity. The RAMAS Landscape computer software was also used during the study.

Ellner et al. (2020) state that identifying the mechanisms producing positive invasion growth rates (IGR) is useful in characterising species persistence. We agree about the importance of the sign of IGR as a binary indicator of persistence, but question whether its magnitude provides much information once the sign is given. [ABSTRACT FROM AUTHOR]

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This article discusses various issues related to the unified neutral theory of biodiversity and biogeography. The unified neutral theory of biodiversity and biogeography is a formal mathematical theory that seeks to explain observed patterns of relative species abundance (RSA) and species richness in ecological communities. The unified theory is developed by incorporating a speciation rate into the classical theory of island biogeography. Simulations of the theory's dynamics have to date suggested that at equilibrium the local community RSA distribution has a particular lognormal-like shape, one that is commonly observed in natural communities.

Isolating the effects of fragmentation per se (i.e., spatial configuration of habitat patches) on species richness is an ongoing challenge as habitat configuration often covaries with the amount of habitat. Consequently, there is a lack of experimental evidence for configurational effects on species richness in the whole landscape. Here, we developed a novel experimental system for testing the independent and interactive effects of habitat area and configuration on tropical intertidal species richness. Our results confirmed the expectation that average species richness would increase monotonically with habitat area. More intriguingly, we found mixed evidence for a non‐monotonic relationship between species richness and fragmentation per se, with the highest richness at intermediate fragmentation configuration, that is, when habitat tiles were placed in a "several‐small" configuration. The effect of habitat configuration was not due to passive sampling (since area was controlled for), variation in total individual abundance, or niche specialization of species to different landscape configurations. We postulate that a combination of processes, including local negative density dependence and dispersal limitation, could give rise to the observed pattern. We emphasize the importance of considering configurational effects on biodiversity at broader spatial scales and for more experimental research to delve into the mechanisms driving the patterns seen here. [ABSTRACT FROM AUTHOR]