Search

Populations of forest trees exhibit large temporal fluctuations, but little is known about the synchrony of these fluctuations across space, including their sign, magnitude, causes and characteristic scales. These have important implications for metapopulation persistence and theoretical community ecology. Using data from permanent forest plots spanning local, regional and global spatial scales, we measured spatial synchrony in tree population growth rates over sub-decadal and decadal timescales and explored the relationship of synchrony to geographical distance. Synchrony was high at local scales of less than 1 km, with estimated Pearson correlations of approximately 0.6–0.8 between species' population growth rates across pairs of quadrats. Synchrony decayed by approximately 17–44% with each order of magnitude increase in distance but was still detectably positive at distances of 100 km and beyond. Dispersal cannot explain observed large-scale synchrony because typical seed dispersal distances (<100 m) are far too short to couple the dynamics of distant forests on decadal timescales. We attribute the observed synchrony in forest dynamics primarily to the effect of spatially synchronous environmental drivers (the Moran effect), in particular climate, although pests, pathogens and anthropogenic drivers may play a role for some species. [ABSTRACT FROM AUTHOR]

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]

Numerous studies have shown reduced performance in plants that are surrounded by neighbours of the same species1,2, a phenomenon known as conspecific negative density dependence (CNDD)3. A long-held ecological hypothesis posits that CNDD is more pronounced in tropical than in temperate forests4,5, which increases community stabilization, species coexistence and the diversity of local tree species6,7. Previous analyses supporting such a latitudinal gradient in CNDD8,9 have suffered from methodological limitations related to the use of static data10–12. Here we present a comprehensive assessment of latitudinal CNDD patterns using dynamic mortality data to estimate species-site-specific CNDD across 23 sites. Averaged across species, we found that stabilizing CNDD was present at all except one site, but that average stabilizing CNDD was not stronger toward the tropics. However, in tropical tree communities, rare and intermediate abundant species experienced stronger stabilizing CNDD than did common species. This pattern was absent in temperate forests, which suggests that CNDD influences species abundances more strongly in tropical forests than it does in temperate ones13. We also found that interspecific variation in CNDD, which might attenuate its stabilizing effect on species diversity14,15, was high but not significantly different across latitudes. Although the consequences of these patterns for latitudinal diversity gradients are difficult to evaluate, we speculate that a more effective regulation of population abundances could translate into greater stabilization of tropical tree communities and thus contribute to the high local diversity of tropical forests.An analysis of tree survival data from forest sites worldwide shows that in the tropics, rare tree species experience stronger stabilizing density dependence than common species, wheras no correlation of stabilizing density dependence and abundance exists in the temperate zone. [ABSTRACT FROM AUTHOR]

There is an urgent need for reliable data on the impacts of deforestation on tropical biodiversity. The city-state of Singapore has one of the most detailed biodiversity records in the tropics, dating back to the turn of the 19th century. In 1819, Singapore was almost entirely covered in primary forest, but this has since been largely cleared. We compiled more than 200 y of records for 10 major taxonomic groups in Singapore (>50,000 individual records; >3,000 species), and we estimated extinction rates using recently developed and novel statistical models that account for “dark extinctions,” i.e., extinctions of undiscovered species. The estimated overall extinction rate was 37% (95% CI [31 to 42%]). Extrapolating our Singapore observations to a future business-as- usual deforestation scenario for Southeast Asia suggests that 18% (95% CI [16 to 22%]) of species will be lost regionally by 2100. Our extinction estimates for Singapore and Southeast Asia are a factor of two lower than previous estimates that also attempted to account for dark extinctions. However, we caution that particular groups such as large mammals, forest-dependent birds, orchids, and butterflies are disproportionately vulnerable. [ABSTRACT FROM AUTHOR]

Many infectious diseases exist as multiple variants, with interactions between variants potentially driving epidemiological dynamics. These diseases include dengue, which infects hundreds of millions of people every year and exhibits complex multi-serotype dynamics. Antibodies produced in response to primary infection by one of the four dengue serotypes can produce a period of temporary cross-immunity (TCI) to infection by other serotypes. After this period, the remaining antibodies can facilitate the entry of heterologous serotypes into target cells, thus enhancing severity of secondary infection by a heterologous serotype. This represents antibody-dependent enhancement (ADE). In this study, we analyze an epidemiological model to provide novel insights into the importance of TCI and ADE in producing cyclic outbreaks of dengue serotypes. Our analyses reveal that without TCI, such cyclic outbreaks are synchronous across serotypes and only occur when ADE produces high transmission rates. In contrast, the presence of TCI allows asynchronous cycles of serotypes by inducing a time lag between recovery from primary infection by one serotype and secondary infection by another, with such cycles able to occur without ADE. Our results suggest that TCI is a fundamental driver of asynchronous cycles of dengue serotypes and possibly other multi-variant diseases. [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]

A central goal in ecology is to understand what maintains species diversity in local communities. Classic ecological theory1,2 posits that niches dictate the maximum number of species that can coexist in a community and that the richness of observed species will be below this maximum only where immigration is very low. A new alternative theory3,4 is that niches, instead, dictate the minimum number of coexisting species and that the richness of observed species will usually be well above this because of ongoing immigration. We conducted an experimental test to discriminate between these two unified theories using a manipulative field experiment with tropical intertidal communities. We found, consistent with the new theory, that the relationship of species richness to immigration rate stabilized at a low value at low immigration rates and did not saturate at high immigration rates. Our results suggest that tropical intertidal communities have low niche diversity and are typically in a dispersal-assembled regime where immigration is high enough to overfill the niches. Observational data from other studies3,5 suggest that these conclusions may generalize to other ecological systems. Our new experimental approach can be adapted for other systems and be used as a ‘niche detector’ and a tool for assessing when communities are niche versus dispersal assembled.This study shows through a field experiment that tropical intertidal communities switch from a niche- to a dispersal-assembled regime as immigration rates rise. [ABSTRACT FROM AUTHOR]

The question of whether biodiversity conservation and carbon conservation can be synergistic hinges on the form of the biodiversity–productivity relationship (BPR), a fundamental ecological pattern. The stakes are particularly high when it comes to forests, which at a global level comprises a large fraction of both biodiversity and carbon. And yet, in forests, the BPR is relatively poorly understood. In this review, we critically evaluate research on forest BPRs, focussing on the experimental and observational studies of the last 2 decades. We find general support for a positive forest BPR, suggesting that biodiversity and carbon conservation are synergistic to a degree. However, we identify several major caveats: (i) although, on average, productivity may increase with biodiversity, the highest-yielding forests are often monocultures of very productive species; (ii) productivity typically saturates at fewer than ten species; (iii) positive BPRs can be driven by some third variable, in particular stem density, instead of a causal arrow from biodiversity to productivity; (iv) the BPR's sign and magnitude varies across spatial grains and extents, and it may be weak at scales relevant to conservation; and (v) most productivity estimates in forests are associated with large errors. We conclude by explaining the importance of these caveats for both conservation programmes focussed on protection of existing forests and conservation programmes focussed on restoring or replanting forests. [ABSTRACT FROM AUTHOR]

Ecology lacks a holistic approach that can model phenomena across temporal and spatial scales, largely because of the challenges in modelling systems with a large number of interacting constituents. This hampers our understanding of complex ecosystems and the impact that human interventions (e.g., deforestation, wildlife harvesting and climate change) have on them. Here we use density functional theory, a computational method for many-body problems in physics, to develop a computational framework for ecosystem modelling. Our methods accurately fit experimental and synthetic data of interacting multi-species communities across spatial scales and can project to unseen data. As the key concept we establish and validate a cost function that encodes the trade-offs between the various ecosystem components. We show how this single general modelling framework delivers predictions on par with established, but specialised, approaches for systems from predatory microbes to territorial flies to tropical tree communities. Our density functional framework thus provides a promising avenue for advancing our understanding of ecological systems. Modelling diverse ecological phenomena across scales with a single mathematical framework is challenging. Here, the authors draw on density functional theory to develop a framework that bridges between mechanistic theories at fine scales and statistical models at large scales. [ABSTRACT FROM AUTHOR]

Human cooperation (paying a cost to benefit others) is puzzling from a Darwinian perspective, particularly in groups with strangers who cannot repay nor are family members. The beneficial effects of cooperation typically increase nonlinearly with the number of cooperators, e.g., increasing returns when cooperation is low and diminishing returns when cooperation is high. Such nonlinearity can allow cooperation between strangers to persist evolutionarily if a large enough proportion of the population are already cooperators. However, if a lone cooperator faces a conflict between the group's and its own interests (a social dilemma), that raises the question of how cooperation arose in the first place. We use a mathematically tractable evolutionary model to formalise a chronological narrative that has previously only been investigated verbally: given that ancient humans interacted mostly with family members (genetic homophily), cooperation evolved first by kin selection, and then persisted in situations with nonlinear benefits as homophily declined or even if interactions with strangers became the norm. The model also predicts the coexistence of cooperators and defectors observed in the human population (polymorphism), and may explain why cooperators in behavioural experiments prefer to condition their contribution on the contributions of others (conditional cooperation in public goods games). [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]

Estimating the total number of species on Earth has been a longstanding pursuit. Models project anywhere between 2 and 10 million species, and discovery of new species continues to the present day. Despite this, we hypothesized that our current knowledge of phylogenetic diversity (PD) may be almost complete because new discoveries may be less phylogenetically distinct than past discoveries. Focusing on birds, which are well studied, we generated a robust phylogenetic tree for most extant species by combining existing published trees and calculated each discovery's marginal contribution to known PD since the first formal species descriptions in 1758. We found that PD contributions began to plateau in the early 1900s, about half a century earlier than species richness. Relative contributions of each phylogenetic order to known PD shifted over the first 150 years, with a growing contribution of the hyper-diverse perching birds (Passeriformes) in particular, but after the early 1900s this has remained relatively stable. Altogether, this suggests that our knowledge of the evolutionary history of extant birds is mostly complete, with few discoveries of high evolutionary novelty left to be made, and that conclusions of studies using avian phylogenies are likely to be robust to future species discoveries. [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]

Theoretical and empirical studies suggest that as immigration increases, ecological communities transition from a niche-structured regime to an immigration-structured regime. The niche-structured regime is the domain of classic niche models; the immigration-structured regime is the domain of island biogeography and related theories. A recent unified model predicted a biphasic species–area relationship (SAR) arising from the transition between the two regimes, but the generality and scope of this relationship remain unclear. Here we study the transition further to address three key questions: (1) Can MacArthur and Wilson's classic graphical paradigm of intersecting immigration and extinction curves be adapted to capture the niche-structured regime that occurs at low-immigration rates? (2) Do different ecological models predict a similar biphasic SAR? (3) Can the biphasic island SAR be reconciled with the classic triphasic SAR observed in mainland biogeography? On the first point, we find that the transition can be incorporated into MacArthur and Wilson's graphical paradigm by forcing the extinction curves sharply downwards at low species richness, reflecting the stabilizing effect of niche processes. On the second point, we confirm that a variety of simple ecological models produce qualitatively similar biphasic SARs. On the third point, we find that a unified model predicts an overall four-phase SAR with the second phase being a shallow niche-structured phase that is rarely observed in mainland SARs, which we hypothesize is because local communities on mainlands are usually in the immigration-structured regime. [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]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A goal of ecology is to identify the stabilizing mechanisms that maintain species diversity in the face of competitive exclusion and drift. For tropical forest tree communities, it has been hypothesized that high diversity is maintained via Janzen-Connell effects, whereby host-specific natural enemies prevent any one species from becoming too abundant. Here we explore the plausibility of this hypothesis with theoretical models. We confirm a previous result that when added to a model with drift but no competitive exclusion—that is, a neutral model where intrinsic fitnesses are perfectly equalized across species—Janzen-Connell effects maintain very high species richness that scales strongly with community size. However, when competitive exclusion is introduced—that is, when intrinsic fitnesses vary across species—the number of species maintained by Janzen-Connell effects is substantially reduced and scales much less strongly with community size. Because fitness variation is pervasive in nature, we conclude that the potential of Janzen-Connell effects to maintain diversity is probably weak and that the mechanism does not yet provide a sufficient explanation for the observed high diversity of tropical forest tree communities. We also show that, surprisingly, dispersal limitation can further reduce the ability of Janzen-Connell effects to maintain diversity. [ABSTRACT FROM AUTHOR]

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]

The relationship between species richness and productivity changes with spatial scale, but the way in which it changes and the underlying mechanisms remain unclear. We address this critical knowledge gap using a new mechanistic model of the spatial scaling of species richness–productivity (SP) relationships for a local community. Our model is neutral and hence assumes that species dynamics are driven by dispersal limitation and demographic stochasticity. We showed analytically that SP relationships predicted by our model are typically unimodal. Consistent with previous simulation-based studies, the positive phase of our unimodal SP relationship was driven by a sampling effect ("more-individuals effect") whereas the negative phase was driven by relatively more propagules being of local as opposed to external origin ("dilution effect"). Our main novel finding related to the spatial scaling of the unimodal SP relationship: the peak shifted to the left with increasing spatial scale, such that the decreasing phase covered a greater range of productivity. This was driven by an increase in the strength of the dilution effect relative to the more-individuals effect, reflecting higher area/perimeter ratios at larger spatial scales. Our theoretical predictions are qualitatively consistent with the spatial scaling of SP relationships documented for trees in localized forest communities across the world. [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]

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]

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]

Natural communities at all spatiotemporal scales are subjected to a wide variety of environmental pressures, resulting in random changes in the demographic rates of species populations. Previous analyses have examined the effects of such environmental variance on the long-term growth rate and time to extinction of single populations, but studies of its effects on the diversity of communities remain scarce. In this study, we construct a new master-equation model incorporating demographic and environmental variance and use it to examine how statistical patterns of diversity, as encapsulated by species-abundance distributions (SADs), are altered by environmental variance. Unlike previous diffusion models with environmental variance uncorrelated in time (white noise), our model allows environmental variance to be correlated at different timescales (colored noise), thus facilitating representation of phenomena such as yearly and decadal changes in climate. We derive an exact analytical expression for SADs predicted by our model together with a close approximation, and use them to show that the main effect of adding environmental variance is to increase the proportion of abundant species, thus flattening the SAD relative to the log-series form found in the neutral case. This flattening effect becomes more prominent when environmental variance is more correlated in time and has greater effects on species' demographic rates, holding all other factors constant. Furthermore, we show how our model SADs are consistent with those from diffusion models near the white noise limit. The mathematical techniques we develop are catalysts for further theoretical work exploring the consequences of environmental variance for biodiversity. [ABSTRACT FROM AUTHOR]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

MacArthur and Wilson's theory of island biogeography predicts that island species richness should increase with island area. This prediction generally holds among large islands, but among small islands species richness often varies independently of island area, producing the so-called 'small-island effect' and an overall biphasic species-area relationship (SAR). Here, we develop a unified theory that explains the biphasic island SAR. Our theory's key postulate is that as island area increases, the total number of immigrants increases faster than niche diversity. A parsimonious mechanistic model approximating these processes reproduces a biphasic SAR and provides excellent fits to 100 archipelago datasets. In the light of our theory, the biphasic island SAR can be interpreted as arising from a transition from a niche-structured regime on small islands to a colonization-extinction balance regime on large islands. The first regime is characteristic of classic deterministic niche theories; the second regime is characteristic of stochastic theories including the theory of island biogeography and neutral theory. The data furthermore confirm our theory's key prediction that the transition between the two SAR regimes should occur at smaller areas, where immigration is stronger (i.e. for taxa that are better dispersers and for archipelagos that are less isolated). [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]

Termites are eusocial insects that evolved from solitary cockroaches. It is not known precisely what factors drove the evolution of termite eusociality, that is, skewed reproduction with distinct winged reproductive and wingless worker phenotypes. In other eusocial insects (bees and wasps), reproductive skew evolved first and phenotype differences evolved second. We propose that the reverse pattern occurred in termites, that is, that the winged-wingless diphenism evolved before eusociality. We discuss existing phylogenetic and pheromonal evidence supporting our hypothesis. We provide new experimental evidence from themost basal termite species (Mastotermes darwiniensis), suggesting that the ancestral state was indeed diphenic but presocial.Wepropose that the mechanismpromoting awinged-wingless diphenism—in the absence of eusociality—was greater predation of aerial than terrestrial dispersers, and we support this with a game theoretic model.Weaugment our hypothesis with a novel explanation for the evolution of the developmental pathways leading to winged and wingless phenotypes in termites. An added benefit of our hypothesis is that it neatly explains the origin of termite eusociality itself: in the pre-eusocial ancestral species, the poor dispersal ability of the wingless phenotype would have led to clustering of relatives around shared resources—a prerequisite for nonparental care of close relatives. [ABSTRACT FROM AUTHOR]