Barro Colorado Island Research Articles

Nutrient Dynamics in a Coupled Terrestrial Biosphere and Land Model (ELM‐FATES‐CNP)

AbstractWe present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon‐nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre‐existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant‐soil dynamics for a large number of size‐ and functional‐type‐resolved plant cohorts within a time‐since‐disturbance‐resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high‐light‐availability canopy positions allocate more carbon to fine roots than plants in low‐light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon‐nutrient coupling with vegetation demography within Earth system models.

Climbing aroids in a Panamanian lowland forest: We should reconsider our categories

AbstractBackgroundIn contrast to woody climbers, information on community composition or vertical extension within the forest is scarce for herbaceous climbers, even in well‐studied field sites like Barro Colorado Island. Moreover, questions regarding ontogenetic patterns (site of germination, changes in root/shoot connection with the soil) are unresolved because of a lack of field data.LocationBarro Colorado Island, Panama.MethodsIn 17 plots of 400 m2 each, which were distributed all over the island, we recorded all potential hosts (trees, palms, lianas) with a diameter at breast height larger than 1 cm, and all climbing aroids attached to them. For aroids, we recorded species identity, number of shoots, root connections to the ground, and vertical shoot extension. By distinguishing three size classes for each species in our analyses we deduced the site of germination and ontogenetic changes in the root/shoot connection with the soil.ResultsOnly 16% of all potential hosts were occupied by climbing aroids. We recorded 1196 individuals of 17 species. Aroids preferred larger trees and old‐growth forest. Species differed strongly in vertical distribution. Hemiepiphytic species germinate epiphytically, often high up in tree crowns and later establish root contact with the soil, while the majority of species establish on or close to the ground and reach moderate heights of 5–15 m (forest height ca. 35 m). In all of these species, we observed dieback of the proximal portion of the shoot to a varying extent but contact with the soil was invariably retained via adventitious roots.ConclusionsWe provide rare quantitative data on species richness and abundance of herbaceous climbers in a tropical lowland forest. Few species fall neatly into the categories of vines, nomadic vines and hemiepiphytes. This highlights the need for longitudinal observational and experimental studies to resolve the current debate on the appropriate grouping of these climbers.

Illuminating arthropod diversity in a tropical forest: Assessing biodiversity by automatic light trapping and DNA metabarcoding

AbstractAlthough studies of insect decline have recently dominated headlines worldwide, their interpretation requires caution since for most species, we lack long‐term population baselines. In the tropics, where most insect species thrive, our knowledge is even more limited and so reliable insect assessments must originate from well‐established long‐term monitoring efforts. Combining the extensive monitoring data from the Arthropod Program of the Smithsonian Tropical Research Institute (STRI) on Barro Colorado Island (BCI), Panama, we compare whether known arthropod diversity can be detected through metabarcoding of bulk insect samples obtained through automatic light‐trapping. Our study detected 4402 species based on Barcode Index Numbers (BIN) and detected fine‐scale differences between wet and dry seasons and sampling localities. We further refined our analysis to indicate which families and genera explained seasonal turnover. Using samples collected in parallel, but sorted manually as part of the ongoing arthropod monitoring program, we compared these methods. Out of 538 BINs recovered through manual sorting, there was a 70% overlap with the metabarcoding data; however, it represented 30% of the total BINs detected through metabarcoding. Expecting higher detection through metabarcoding, we also compare the results with the 14 years of sampling in BCI to better understand how well the monitoring program has captured the diversity of focal groups. Our results revealed a ~50% overlap between both methods and similar total catch. Barcode Index Numbers manually detected but not recovered by metabarcoding highlight some of the limitations of molecular detection methods such as primer bias. Contrastingly, BINs detected with metabarcoding, but not recovered by the traditional monitoring scheme, highlight the importance of local and regional barcode reference libraries.

An early Miocene (Aquitanian) mangrove fossil forest buried by a volcanic lahar at Barro Colorado Island, Panama

We describe remains on an Early Miocene mangrove forest, based on a fossil wood assemblage, discovered on Barro Colorado Island, Panama. Sedimentological and stratigraphic analysis suggests that the fossil trees grew in marginal marine to coastal fluvial settings and were buried under a volcanic lahar flow in a single event. Radiometric analyses of a tuff associated with the fossils gives a date of ∼22.79 Ma indicating an assignment to the Aquitanian stage of Early Miocene. At this time, central Panama was part of a long and narrow peninsula with intense volcanic activity that connected it with North America and was separated from South America by the Central American Seaway. A total of 121 fossil wood specimens were located. Wood anatomy indicates that most of the identifiable specimens belonged to the same morphotype, which has anatomical traits similar to Sonneratia (Lythraceae), a mangrove tree that is native to Southeast Asia. We named this morphotype as a new fossil species: Sonneratioxylon barrocoloradoensis Pérez-Lara., sp. nov. Biomechanical estimates indicate that S. barrocoloradoensis had a mean tree height of 25 m with some specimens reaching 40 m, in contrast to modern Sonneratia and other extant mangrove forests, which generally have lower mean heights. The dominance of S. barrocoloradoensis, its similarity to Sonneratia, and the depositional setting suggest that the fossil wood assemblage on Barro Colorado Island comprised a mangrove forest growing along the coast of the volcanic chain of central Panama.

Decadal survival of tropical pioneer seeds in the soil seed bank is accompanied by fungal infection and dormancy release

Abstract Pioneer trees require high‐light environments for successful seedling establishment. Consequently, seeds of these species often persist in the soil seed bank (SSB) for periods ranging from several weeks to decades. How they survive despite extensive pressure from seed predators and soil‐borne pathogens remains an intriguing question. This study aims to test the hypotheses that decades‐old seeds collected from the SSB in a lowland tropical forest remain viable by (i) escaping infection by fungi, which are major drivers of seed mortality in tropical soils, and/or (ii) maintaining high levels of seed dormancy and seed coat integrity when compared to inviable seeds. We collected seeds of Trema micrantha and Zanthoxylum ekmanii at Barro Colorado Island, Panama, from sites where adult trees previously occurred in the past 30 years. We used carbon dating to measure seed age and characterized seed coat integrity, seed dormancy and fungal communities. Viable seeds from the SSB ranged in age from 9 to 30 years for T. micrantha, and 5 to 33 years for Z. ekmanii. We found no evidence that decades‐old seeds maintain high levels of seed dormancy or seed coat integrity. Fungi were rarely detected in fresh seeds (no soil contact), but phylogenetically diverse fungi were detected often in seeds from the SSB. Although fungal infections were more commonly detected in inviable seeds than in viable seeds, a lack of differences in fungal diversity and community composition between viable and inviable seeds suggested that viable seeds are not simply excluding fungal species to survive long periods in the SSB. Synthesis. Our findings reveal the importance of a previously understudied aspect of seed survival, where the impact of seed–microbial interactions may be critical to understand long‐term persistence in the SSB. Read the free Plain Language Summary for this article on the Journal blog.

Generative spatial generalized dissimilarity mixed modelling (spGDMM): An enhanced approach to modelling beta diversity

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

Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)

Abstract. Vegetation plays a key role in the global carbon cycle and thus is an important component within Earth system models (ESMs) that project future climate. Many ESMs are adopting methods to resolve plant size and ecosystem disturbance history, using vegetation demographic models. These models make it feasible to conduct more realistic simulation of processes that control vegetation dynamics. Meanwhile, increasing understanding of the processes governing plant water use, and ecosystem responses to drought in particular, has led to the adoption of dynamic plant water transport (i.e., hydrodynamic) schemes within ESMs. However, the extent to which variations in plant hydraulic traits affect both plant water stress and the risk of mortality in trait-diverse tropical forests is understudied. In this study, we report on a sensitivity analysis of an existing hydrodynamic scheme (HYDRO) model that is updated and incorporated into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) (FATES–HYDRO V1.0). The size- and canopy-structured representation within FATES is able to simulate how plant size and hydraulic traits affect vegetation dynamics and carbon–water fluxes. To better understand this new model system, and its functionality in tropical forest systems in particular, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We assembled 942 observations of plant hydraulic traits on 306 tropical plant species for stomata, leaves, stems, and roots and determined the best-fit statistical distribution for each trait, which was used in model parameter sampling to assess the parametric sensitivity. We showed that, for simulated leaf water potential and loss of hydraulic conductivity across different plant organs, the four most important traits were associated with xylem conduit taper (buffers increasing hydraulic resistance with tree height), stomatal sensitivity to leaf water potential, maximum stem hydraulic conductivity, and the partitioning of total hydraulic resistance above vs. belowground. Our analysis of individual ensemble members revealed that trees at a high risk of hydraulic failure and potential tree mortality generally have a lower conduit taper, lower maximum xylem conductivity, lower stomatal sensitivity to leaf water potential, and lower resistance to xylem embolism for stem and transporting roots. We expect that our results will provide guidance on future modeling studies using plant hydrodynamic models to predict the forest responses to droughts and future field campaigns that aim to better parameterize plant hydrodynamic models.

Inferring single- and multi-species distributional aggregation using quadrat sampling

Ecologists often employ cost-effective strategies such as quadrat sampling for field-based biodiversity assessments. While the negative binomial distribution (NBD) is widely used to characterize quadrat sampling-derived biodiversity data in single-species settings, an equivalent model for accurately modeling community-level distributional aggregation levels, especially for quadrat sampling data, is still lacking. Here, we recommend the application of symmetric Dirichlet-multinomial (SDM) distribution to characterize aggregation patterns for single- or multi-species. Theoretically, we proved that the likelihood formulae for the SDM and NBD models were nearly equivalent, and consequently, the shape parameters of both models were almost identical. Numerical simulations demonstrated that the SDM model consistently outperformed the NBD model except for cases with large numbers of small-sized quadrats. Empirical tests using the SDM model showed that trees of the forest plot on the Barro Colorado Island shifted from aggregated to even and then back to aggregated again from 1982 to 2015 because of stand competition dynamics driven by tree mortality and recruitment balance. Functionally, the aggregation parameter of the SDM model reflects more purely this biological mechanism-driven aggregation trend, whereas classical nonparametric aggregation metrics failed. In summary, the proposed SDM can be an indispensable tool for inferring species- and community-level distributional aggregation patterns.

Probabilistic approaches for investigating species co-occurrence from presence-absence maps.

In this research, we propose probabilistic approaches to identify pairwise patterns of species co-occurrence by using presence-absence maps only. In particular, the two-by-two contingency table constructed from a presence-absence map of two species would be sufficient to compute the test statistics and perform the statistical tests proposed in this article. Some previous studies have investigated species co-occurrence through incidence data of different survey sites. We focus on using presence-absence maps for a specific study plot instead. The proposed methods are assessed by a thorough simulation study. A Chi-squared test is used to determine whether the distributions of two species are independent. If the null hypothesis of independence is rejected, the Chi-squared method can not distinguish positive or negative association between two species. We propose six different approaches based on either the binomial or Poisson distribution to obtain p-values for testing the positive (or negative) association between two species. When we test to investigate a positive (or negative) association, if the p-value is below the predetermined level of significance, then we have enough evidence to support that the two species are positively (or negatively) associated. A simulation study is conducted to demonstrate the type-I errors and the testing powers of our approaches. The probabilistic approach proposed by Veech (2013) is served as a benchmark for comparison. The results show that the type-I error of the Chi-squared test is close to the significance level when the presence rate is between 40% and 80%. For extremely low or high presence rate data, one of our approaches outperforms Veech (2013)'s in terms of the testing power and type-I error rate. The proposed methods are applied to a tree data of Barro Colorado Island in Panama and a tree data of Lansing Woods in USA. Both positive and negative associations are found among some species in these two real data.

Differential tree demography mediated by water stress and functional traits in a moist tropical forest

Abstract Climate‐induced changes in tree mortality, recruitment and growth have been extensively observed in forests worldwide. However, there is still a lack of quantitative understanding regarding how tree demography responds to environmental factors, particularly different water conditions, and how plant functional traits contribute to interspecific differences in this response. Using data collected from a forest monitoring plot, meteorological observations and trait measurements on Barro Colorado Island in Panama—one of the most studied sites, we investigated the mortality, recruitment, growth and population dynamics of 165 tree species from 1990 to 2014 and examined the influence of functional traits in determining the interspecific differences in tree demography. We also employed parametric accelerated failure time models to assess the impact of environmental conditions, functional traits and individual tree characteristics on tree survival time. We find that water stress drives the temporal dynamics of tree demography. During the high water stress period, the increase in mortality rates and decrease in recruitment rates caused negative population changes for species. In contrast, the increase in recruitment rates and decline in mortality rates during the low water stress period positively affected the population of species. Wood density and P50 (xylem water potential at 50% loss of maximum hydraulic conductivity) are significantly correlated with species‐level mortality, recruitment and growth rates, indicating that traits related to resource allocation and hydraulic safety dictate the interspecific differences in demographic rates. Our results demonstrate that water stress and traits related to resource allocation and hydraulic safety jointly mediate tree demography in this tropical forest. High mortality and low recruitment under high water stress may especially cause a decline in fast‐growing species. Therefore, timely recovery of recruitment under favourable water conditions is crucial for the forest dynamics. Read the free Plain Language Summary for this article on the Journal blog.

Seedling performance in a dioecious tree species is similar near female and male conspecific adults despite differences in colonization by arbuscular mycorrhizal fungi

Plant–soil feedbacks (PSFs) are a key driver of species diversity and composition in plant communities worldwide; however, the factors that may cause feedbacks to vary within species are rarely examined. In dioecious species, the strength of feedbacks may differ near female plants that produce seed versus near male plants (which do not) because repeated inputs of seeds and high seedling densities near females may cause accumulation of host‐specific soil microbes that influence seedling performance. To test whether conspecific seedling performance is reduced near seed‐producing female trees relative to male or heterospecific trees, we conducted shadehouse and field experiments with a dioecious tropical tree species, Virola surinamensis (Myristicaceae), on Barro Colorado Island, Panama. The shadehouse experiment isolated the effect of soil microbial communities on seedling growth and allowed us to quantify colonization by mutualistic arbuscular mycorrhizal (AM) fungi, while the field experiment allowed us to assess seedling survival and growth in the presence of nearby conspecific adults and seedlings. In both experiments, seedling performance was similar between seedlings grown in the soil microbial communities and field environments underneath female conspecific, male conspecific, and heterospecific trees. However, contrary to expectation, seedling colonization by AM fungi was higher in male conspecific soil microbial communities than in female or heterospecific soil microbial communities at the end of the shadehouse experiment. Together, our experiments show that while differences among female and male plants in dioecious species may influence the association of conspecific seedlings with AM fungi in their soils, this variation does not necessarily translate directly to differences in seedling performance, at least over the time frame of our experiments. Studies of additional dioecious species are needed to help determine differences in soil microbial communities beneath male and female plants and to assess the role of seed input versus adult root systems in driving PSFs.

Long-term seedling and small sapling census data from the Barro Colorado Island 50 ha Forest Dynamics Plot, Panama.

Tropical forests are well known for their high woody plant diversity. Processes occurring at early life stages are thought to play a critical role in maintaining this high diversity and shaping the composition of tropical tree communities. To evaluate hypothesized mechanisms promoting tropical tree species coexistence and influencing composition, we initiated a census of woody seedlings and small saplings in the permanent 50 ha Forest Dynamics Plot (FDP) on Barro Colorado Island (BCI), Panama. Situated in old-growth, lowland tropical moist forest, the BCI FDP was originally established in 1980 to monitor trees and shrubs ≥1 cm diameter at 1.3 m above ground (dbh) at ca. 5-year intervals. However, critical data on the dynamics occurring at earlier life stages were initially lacking. Therefore, in 2001 we established a 1-m2 seedling plot in the center of every 5 × 5 m section of the BCI FDP. All freestanding woody individuals ≥20 cm tall and <1 cm dbh (hereafter referred to as seedlings) were tagged, mapped, measured, and identified to species in 19,313 1-m2 seedling plots. Because seedling dynamics are rapid, we censused these seedling plots every 1-2 years. Here, we present data from the 14 censuses of these seedling plots conducted between the initial census in 2001 to the most recent census, in 2018. This data set includes nearly 1 M observations of ~185,000 individuals of >400 tree, shrub, and liana species. These data will permit spatially-explicit analyses of seedling distributions, recruitment, growth, and survival for hundreds of woody plant species. In addition, the data presented here can be linked to openly-available, long-term data on the dynamics of trees and shrubs ≥1 cm dbh in the BCI FDP, as well as existing data sets from the site on climate, canopy structure, phylogenetic relatedness, functional traits, soil nutrients, and topography. This data set can be freely used for non-commercial purposes; we request that users of these data cite this data paper in all publications resulting from the use of this data set.

Cost-effective and accurate monitoring of flowering across multiple tropical tree species over two years with a time series of high-resolution drone imagery and deep learning

Detection of flowering and quantification of flowering phenology are key to monitoring the reproduction of tropical trees and their response to global change. However, effective monitoring of flowering over various scales from individuals to forest ecosystem levels is lacking due to the relatively small sizes of flowers, diverse flowering strategies across species, and the short duration of flowering, making accurate flower detection difficult. Drone-based surveys require less time and human resources than traditional ground-based flower surveys and thus may be able to help address this in a cost-effective manner but remain underexplored in species-rich tropical forest ecosystems. Here, we developed a method that integrated the Residual Networks 50 (ResNet50) deep learning algorithm with high resolution imagery (c. 0.05 m) from monthly drone surveys done in a 50-ha tropical forest plot on Barro Colorado Island (BCI), Panamá, over 2018–2020 to detect a diversity of flowering species in this tree community and to track the timing of flowering throughout the year. We built a comprehensive training library of canopy components (flower, leaf, branch, and shade) from this forest plot throughout the study period, trained a single deep learning model across all drone imagery, and validated it using five-fold cross validation at the pixel scale. We further generated image- and tree-crown-specific supervised classifications to evaluate the deep learning model at the tree-crown scale. Our deep learning method accurately classified flowers (User’s accuracy = 95.3 %, Producer’s accuracy = 85.8 %) while maintaining high predictive power for the other three classes (Overall accuracy = 98.4 %). Our results also demonstrated high consistency against tree-crown-specific supervised classifications for flower (r2 = 0.85), leaf (r2 = 0.84), and branch (r2 = 0.92) components, with lower agreement observed for the shade component (r2 = 0.59). These results demonstrate the effectiveness of our method in advancing fine-scale flower monitoring in the tropics, with potential to be extended to other regions or other remote sensing platforms with frequent high-resolution monitoring. The method will allow us to better monitor flowering in tropical forests and improve our understanding of how phenology and reproductive success may be affected by climate change.

Multivariate geometric anisotropic Cox processes

AbstractThis paper introduces a new modeling and inference framework for multivariate and anisotropic point processes. Building on recent innovations in multivariate spatial statistics, we propose a new family of multivariate anisotropic random fields, and from them a family of anisotropic point processes. We give conditions that make the proposed models valid. We also propose a Palm likelihood‐based inference method for this type of point process, circumventing issues of likelihood tractability. Finally we illustrate the utility of the proposed modeling framework by analyzing spatial ecological observations of plants and trees in the Barro Colorado Island data.

A new LDA formulation with covariates

The Latent Dirichlet Location (LDA) model is a popular method for creating mixed-membership clusters. Despite having been originally developed for text analysis, LDA has been used for a wide range of other applications. We propose a new formulation for the LDA model which incorporates covariates. In this model, a negative binomial regression is embedded within LDA, enabling straight-forward interpretation of the regression coefficients and the analysis of the quantity of cluster-specific elements in each sampling units (instead of the analysis being focused on modeling the proportion of each cluster, as in Structural Topic Models). We use slice sampling within a Gibbs sampling algorithm to estimate model parameters. We rely on simulations to show how our algorithm is able to successfully retrieve the true parameter values and the ability to make predictions for the abundance matrix using the information given by the covariates. The model is illustrated using real data sets from three different areas: text-mining of Coronavirus articles, analysis of grocery shopping baskets, and ecology of tree species on Barro Colorado Island (Panama). This model allows the identification of mixed-membership clusters in discrete data and provides inference on the relationship between covariates and the abundance of these clusters.

Testing for stationary dynamics in the Barro Colorado Island forest

We analyse population dynamics in Barro Colorado Island (Panama) using census data of a 50 ha forest plot spanning 35 years, and address the question whether this community is in a stationary state. Individual species abundances show large fluctuations, but assessing stationariety requires discriminating random fluctuations from actual trends. This requires evaluating mean quantities as well as the structure (i.e.the correlations) of the fluctuations around this mean. We argue that a species average is the best surrogate for the theoretically required but unfeasible history average. We define the overlap, a species-averaged measure of composition similarity, which reveals that the BCI population dynamics is stationary but not static, displaying fluctuations with a characteristic time of around 15 years, two orders of magnitude less than previously estimated.

Mineralogical control on physically protected soil organic matter in a neotropical moist forest

Context Minerals and organic matter physically associate in many soils, yet the precise nature, either via a hierarchy of aggregate particles or by build-up of organo–mineral associations, remains obscure, especially in tropical forest environments. Aims We investigated physically protected organic matter by comparing soils with contrasting parent material, topography, and pedogenesis, but with similar tropical moist forest on Barro Colorado Island in the lowlands of Panama. Methods Bulk soil from 10 sites was separated by size into free-floating particles, macroaggregates (&gt;250 μm), microaggregates (53–250 μm), and &lt;53-μm particles. A subsample of macroaggregates was disintegrated and separated into coarse particles (&gt;250 μm), occluded microaggregates, and occluded &lt;53-μm particles. Concentrations and natural abundance of stable isotopes for carbon (C) and nitrogen (N) were determined for each fraction, and ratios (C:N, stable isotopes) were used to characterise organic matter for each fraction. Key results Macroaggregates were the largest fraction of bulk soil (71%) and were 15% greater in kaolinite- than smectite-dominated soils. Macroaggregates were composed of coarse particles (14%), occluded microaggregates (62%), and occluded &lt;53-μm particles (24%). Concentrations of C and N widely varied among fractions but the variation was not related to clay mineralogy. The C:N ratio and stable N isotope ratio indicated more decomposed organic matter in kaolinite- than smectite-dominated soils. Conclusions and implications Macroaggregates composed of plant detritus and microaggregates in the Barro Colorado Island soils imply that the aggregate hierarchy route ultimately protects soil organic matter in this tropical forest environment.

A Framework to Calibrate Ecosystem Demography Models Within Earth System Models Using Parallel Surrogate Global Optimization

AbstractThe climatic feedbacks from vegetation, particularly from tropical forests, can alter climate through land‐atmospheric interactions. Expected shifts in species composition can alter these interactions with profound effects on climate and terrestrial ecosystem dynamics. Ecosystem demographic (ED) models can explicitly represent vegetation dynamics and are a key component of next‐generation Earth System Models (ESMs). Although ED models exhibit greater fidelity and allow more direct comparisons with observations, their interacting parameters can be more difficult to calibrate due to the complex interactions among vegetation groups and physical processes. In addition, while representation of forest successional coexistence in ESMs is necessary to accurately capture forest‐climate interactions, few models can simulate forest coexistence and few studies have calibrated coexisted forest species. Furthermore, although both vegetation characteristics and soil properties affect vegetation dynamics, few studies have paid attention to jointly calibrating parameters related to these two processes. In this study, we develop a computationally‐efficient and physical model structure‐based framework that uses a parallel surrogate global optimization algorithm to calibrate ED models. We calibrate two typically coexisted tropical tree species, early and late successional plants, in a state‐of‐the‐art ED model that is capable of simulating successional diversity in forests. We concurrently calibrate vegetation and soil parameters and validate results against carbon, energy, and water cycle measurements collected in Barro Colorado Island, Panama. The framework can find optimal solutions within 4–12 iterations for 19‐dimensional problems. The calibration for tropical forests has important implications for predicting land‐atmospheric interactions and responses of tropical forests to environmental changes.

Prey tracking and predator avoidance in a Neotropical moist forest: a camera-trapping approach.

Whether prey species avoid predators and predator species track prey is a poorly understood aspect of predator-prey interactions, given measuring prey tracking by predators and predator avoidance by prey is challenging. A common approach to study these interactions among mammals in field situations is to monitor the spatial proximity of animals at fixed times, using GPS tags fitted to individuals. However, this method is invasive and only allows tracking of a subset of individuals. Here, we use an alternative, noninvasive camera-trapping approach to monitor temporal proximity of predator and prey animals. We deployed camera traps at fixed locations on Barro Colorado Island, Panama, where the ocelot (Leopardus pardalis) is the principal mammalian predator, and tested two hypotheses: (1) prey animals avoid ocelots; and (2) ocelots track prey. We quantified temporal proximity of predators and prey by fitting parametric survival models to the time intervals between subsequent prey and predator captures by camera traps, and then compared the observed intervals to random permutations that retained the spatiotemporal distribution of animal activity. We found that time until a prey animal appeared at a location was significantly longer than expected by chance if an ocelot had passed, and that the time until an ocelot appeared at a location was significantly shorter than expected by chance after prey passage. These findings are indirect evidence for both predator avoidance and prey tracking in this system. Our results show that predator avoidance and prey tracking influence predator and prey distribution over time in a field setting. Moreover, this study demonstrates that camera trapping is a viable and noninvasive alternative to GPS tracking for studying certain predator-prey interactions.

Towards a functional classification of poorly known tropical insects: The case of rhinoceros beetles (Coleoptera, Dynastinae) in Panama

AbstractThe population dynamics of most tropical insects are unknown and long‐term monitoring programmes are urgently needed to evaluate a possible insect decline in the tropics. In this context, functional groups can be used effectively to summarise time‐series for species‐rich taxa. Neotropical dung beetles have often been catalogued into functional groups, but close relatives also of ecological significance, the Dynastinae, are awaiting such a classification.Here, we examine the functional groups of Dynastinae at the regional (Panama: 147 species) and local (Barro Colorado Island, BCI: 56 species) scales. Our optimum classification of Panamanian species distinguished five groups, one of which is probably artificial and accounts for species ecologically poorly known.Ecological attributes or species traits mainly influencing the delineation of groups were geographical distribution, body length, seasonal aggregation, larval food and whether the adult may be present in decaying wood.Our analyses indicated that (1) missing trait values and the high percentage of ‘cryptic’ species (25%) influenced the delineation of groups; (2) the dendrogram similarity of functional groups versus phylogenetic tree was low, although some traits were phylogenetically conserved; and (3) the overall structure of functional groups was conserved when comparing regional and local data, suggesting no drastic loss of functional groups locally.To proceed with the functional classification of poorly known tropical insects, we recommend a cautious selection of traits a priori, inclusion of ‘cryptic’ species recognised by DNA barcoding, and building phylogenies, which may allow a careful taxonomic imputation to complete species‐traits matrices.