Fitting individual-based models of spatial population dynamics to long-term monitoring data.

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

Integrated distance sampling models for simple point counts.

Point counts (PCs) are widely used in biodiversity surveys but, despite numerous advantages, simple PCs suffer from several problems: detectability, and therefore abundance, is unknown; systematic spatiotemporal variation in detectability yields biased inferences, and unknown survey area prevents formal density estimation and scaling-up to the landscape level. We introduce integrated distance sampling (IDS) models that combine distance sampling (DS) with simple PC or detection/nondetection (DND) data to capitalize on the strengths and mitigate the weaknesses of each data type. Key to IDS models is the view of simple PC and DND data as aggregations of latent DS surveys that observe the same underlying density process. This enables the estimation of separate detection functions, along with distinct covariate effects, for all data types. Additional information from repeat or time-removal surveys, or variable survey duration, enables the separate estimation of the availability and perceptibility components of detectability with DS and PC data. IDS models reconcile spatial and temporal mismatches among data sets and solve the above-mentioned problems of simple PC and DND data. To fit IDS models, we provide JAGS code and the new "IDS()" function in the R package unmarked. Extant citizen-science data generally lack the information necessary to adjust for detection biases, but IDS models address this shortcoming, thus greatly extending the utility and reach of these data. In addition, they enable formal density estimation in hybrid designs, which efficiently combine DS with distance-free, point-based PC or DND surveys. We believe that IDS models have considerable scope in ecology, management, and monitoring.

Survival rates in the world's southernmost forest bird community.

The Magellanic sub-Antarctic Forest is home to the world's southernmost avian community and is the only Southern Hemisphere analogue to Northern Hemisphere temperate forests at this latitude. This region is considered among the few remaining pristine areas of the world, and shifts in environmental conditions are predominantly driven by climate variability. Thus, understanding climate-driven demographic processes is critical for addressing conservation issues in this system under future climate change scenarios. Here, we describe annual survival patterns and their association with climate variables using a 20-year mark-recapture data set of five forest bird species in the Cape Horn Biosphere Reserve. We develop a multispecies hierarchical survival model to jointly explore age-dependent survival probabilities at the community and species levels in a group of five forest passerines. At the community level, we assess the association of migratory behavior and body size with survival, and at the species level, we investigate the influence of local and regional climatic variables on temporal variations of survival. We found a positive effect of precipitation and a negative effect of El Niño Southern Oscillation on juvenile survival in the white-crested Elaenia and a consistent but uncertain negative effect of temperature on survival in juveniles and 80% of adults. We found only a weak association of climate variables with survival across species in the community and no temporal trends in survival for any of the species in either age class, highlighting apparent stability in these high austral latitude forests. Finally, our findings provide an important resource of survival probabilities, a necessary input for assessing potential impacts of global climate change in this unique region of the world.

The Cantabrian capercaillie: A population on the edge.

The capercaillie Tetrao urogallus - the world's largest grouse- is a circumboreal forest species, which only two remaining populations in Spain: one in the Cantabrian mountains in the west and the other in the Pyrenees further east. Both have shown severe declines, especially in the Cantabrian population, which has recently been classified as "Critically Endangered". To develop management plans, information on demographic parameters is necessary to understand and forecast population dynamics. We used spatial capture-recapture (SCR) modeling and non-invasive DNA samples to estimate the current population size in the whole Cantabrian mountain range. In addition, for the assessment of population status, we analyzed the population trajectory over the last 42 years (1978-2019) at 196 leks on the Southern slope of the range, using an integrated population model with a Dail-Madsen model at its core, combined with a multistate capture-recapture model for survival and a Poisson regression for productivity. For 2019, we estimate the size of the entire population at 191 individuals (95% BCI 165-222) for an estimated 60 (48-78) females and 131 (109-157) males. Since the 1970s, our study estimates a shrinkage of the population range by 83%. The population at the studied leks in 2019 was at about 10% of the size estimated for 1978. Apparent annual survival was estimated at 0.707 (0.677-0.735), and per-capita recruitment at 0.233 (0.207-0.262), and insufficient to maintain a stable population. We suggest work to improve the recruitment (and survival) and manage these mountain forests for capercaillie conservation. Also, in the future, management should assess the genetic viability of this population.

Can dynamic occupancy models improve predictions of species' range dynamics? A test using Swiss birds.

Predictions of species' current and future ranges are needed to effectively manage species under environmental change. Species ranges are typically estimated using correlative species distribution models (SDMs), which have been criticized for their static nature. In contrast, dynamic occupancy models (DOMs) explicitily describe temporal changes in species' occupancy via colonization and local extinction probabilities, estimated from time series of occurrence data. Yet, tests of whether these models improve predictive accuracy under current or future conditions are rare. Using a long-term data set on 69 Swiss birds, we tested whether DOMs improve the predictions of distribution changes over time compared to SDMs. We evaluated the accuracy of spatial predictions and their ability to detect population trends. We also explored how predictions differed when we accounted for imperfect detection and parameterized models using calibration data sets of different time series lengths. All model types had high spatial predictive performance when assessed across all sites (mean AUC > 0.8), with flexible machine learning SDM algorithms outperforming parametric static and DOMs. However, none of the models performed well at identifying sites where range changes are likely to occur. In terms of estimating population trends, DOMs performed best, particularly for species with strong population changes and when fit with sufficient data, while static SDMs performed very poorly. Overall, our study highlights the importance of considering what aspects of performance matter most when selecting a modelling method for a particular application and the need for further research to improve model utility. While DOMs show promise for capturing range dynamics and inferring population trends when fitted with sufficient data, computational constraints on variable selection and model fitting can lead to reduced spatial accuracy of predictions, an area warranting more attention.

Small cats in big trouble? Diet, activity, and habitat use of jungle cats and leopard cats in threatened dry deciduous forests, Cambodia.

Dry deciduous dipterocarp forests (DDF) cover about 15%-20% of Southeast Asia and are the most threatened forest type in the region. The jungle cat (Felis chaus) is a DDF specialist that occurs only in small isolated populations in Southeast Asia. Despite being one of the rarest felids in the region, almost nothing is known about its ecology. We investigated the ecology of jungle cats and their resource partitioning with the more common leopard cats (Prionailurus bengalensis) in a DDF-dominated landscape in Srepok Wildlife Sanctuary, Cambodia. We used camera-trap data collected from 2009 to 2019 and DNA-confirmed scats to determine the temporal, dietary and spatial overlap between jungle cats and leopard cats. The diet of jungle cats was relatively diverse and consisted of murids (56% biomass consumed), sciurids (15%), hares (Lepus peguensis; 12%), birds (8%), and reptiles (8%), whereas leopard cats had a narrower niche breadth and a diet dominated by smaller prey, primarily murids (73%). Nonetheless, dietary overlap was high because both felid species consumed predominantly small rodents. Both species were primarily nocturnal and had high temporal overlap. Two-species occupancy modelling suggested jungle cats were restricted to DDF and had low occupancy, whereas leopard cats had higher occupancy and were habitat generalists. Our study confirmed that jungle cats are DDF specialists that likely persist in low numbers due to the harsh conditions of the dry season in this habitat, including annual fires and substantial decreases in small vertebrate prey. The lower occupancy and more diverse diet of jungle cats, together with the broader habitat use of leopard cats, likely facilitated the coexistence of these species. The low occupancy of jungle cats in DDF suggests that protection of large areas of DDF will be required for the long-term conservation of this rare felid in Southeast Asia.

Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference.

The estimation of abundance and distribution and factors governing patterns in these parameters is central to the field of ecology. The continued development of hierarchical models that best utilize available information to inform these processes is a key goal of quantitative ecologists. However, much remains to be learned about simultaneously modeling true abundance, presence, and trajectories of ecological communities.Simultaneous modeling of the population dynamics of multiple species provides an interesting mechanism to examine patterns in community processes and, as we emphasize herein, to improve species-specific estimates by leveraging detection information among species. Here, we demonstrate a simple but effective approach to share information about observation parameters among species in hierarchical community abundance and occupancy models, where we use shared random effects among species to account for spatiotemporal heterogeneity in detection probability.We demonstrate the efficacy of our modeling approach using simulated abundance data, where we recover well our simulated parameters using N-mixture models. Our approach substantially increases precision in estimates of abundance compared with models that do not share detection information among species. We then expand this model and apply it to repeated detection/non-detection data collected on six species of tits (Paridae) breeding at 119 1 km2 sampling sites across a P. montanus hybrid zone in northern Switzerland (2004-2020). We find strong impacts of forest cover and elevation on population persistence and colonization in all species. We also demonstrate evidence for interspecific competition on population persistence and colonization probabilities, where the presence of marsh tits reduces population persistence and colonization probability of sympatric willow tits, potentially decreasing gene flow among willow tit subspecies.While conceptually simple, our results have important implications for the future modeling of population abundance, colonization, persistence, and trajectories in community frameworks. We suggest potential extensions of our modeling in this paper and discuss how leveraging data from multiple species can improve model performance and sharpen ecological inference.

Survival varies seasonally in a migratory bird: Linkages between breeding and non-breeding periods.

Migratory species form an important component of biodiversity; they link ecosystems across the globe, but are increasingly threatened by global environmental change. Understanding and mitigating threats requires knowledge of how demographic processes operate throughout the annual cycle, but this can be difficult to achieve when breeding and non-breeding grounds are widely separated. Our goal is to quantify the importance of variability in survival during the breeding and non-breeding seasons in determining variation in annual survival using a single population and, more broadly, the extent to which annual survival across species reflects variation in probability of surviving the migratory period. We use a 25-year dataset in which individuals of a long-distance migratory bird, the alpine swift Tachymarptis melba, were captured towards the beginning and end of each breeding season to estimate age- and season-specific survival probabilities and incorporate explicit estimation of the correlations in survival between age-classes and seasons. Monthly survival was higher during the breeding period than during the rest of the year and strongly affected by conditions in the breeding season; effects that remained apparent in the following non-breeding season, but not subsequently. Recruitment of juveniles was dependent on the timing of breeding, being higher if egg-laying commenced before the median date, and substantially lower if not. Across migratory bird species, variation in annual survival largely reflects variation in the probability of surviving the migratory period. Using a double-capture approach, even within a single season, provides valuable insights into the demography of migratory species, which will help understand the extent and impacts of the threats they face in a changing world.

Full-annual demography and seasonal cycles in a resident vertebrate.

Wildlife demography is typically studied at a single point in time within a year when species, often during the reproductive season, are more active and therefore easier to find. However, this provides only a low-resolution glimpse into demographic temporal patterns over time and may hamper a more complete understanding of the population dynamics of a species over the full annual cycle. The full annual cycle is often influenced by environmental seasonality, which induces a cyclic behavior in many species. However, cycles have rarely been explicitly included in models for demographic parameters, and most information on full annual cycle demography is restricted to migratory species. Here we used a high-resolution capture-recapture study of a resident tropical lizard to assess the full intra-annual demography and within-year periodicity in survival, temporary emigration and recapture probabilities. We found important variation over the annual cycle and up to 92% of the total monthly variation explained by cycles. Fine-scale demographic studies and assessments on the importance of cycles within parameters may be a powerful way to achieve a better understanding of population persistence over time.

Joint species distribution models with species correlations and imperfect detection.

Spatiotemporal patterns in biological communities are typically driven by environmental factors and species interactions. Spatial data from communities are naturally described by stacking models for all species in the community. Two important considerations in such multispecies or joint species distribution models (JSDMs) are measurement errors and correlations between species. Up to now, virtually all JSDMs have included either one or the other, but not both features simultaneously, even though both measurement errors and species correlations may be essential for achieving unbiased inferences about the distribution of communities and species co-occurrence patterns. We developed two presence-absence JSDMs for modeling pairwise species correlations while accommodating imperfect detection: one using a latent variable and the other using a multivariate probit approach. We conducted three simulation studies to assess the performance of our new models and to compare them to earlier latent variable JSDMs that did not consider imperfect detection. We illustrate our models with a large Atlas data set of 62 passerine bird species in Switzerland. Under a wide range of conditions, our new latent variable JSDM with imperfect detection and species correlations yielded estimates with little or no bias for occupancy, occupancy regression coefficients, and the species correlation matrix. In contrast, with the multivariate probit model we saw convergence issues with large data sets (many species and sites) resulting in very long run times and larger errors. A latent variable model that ignores imperfect detection produced correlation estimates that were consistently negatively biased, that is, underestimated. We found that the number of latent variables required to represent the species correlation matrix adequately may be much greater than previously suggested, namely around n/2, where n is community size. The analysis of the Swiss passerine data set exemplifies how not accounting for imperfect detection will lead to negative bias in occupancy estimates and to attenuation in the estimated covariate coefficients in a JSDM. Furthermore, spatial heterogeneity in detection may cause spurious patterns in the estimated species correlation matrix if not accounted for. Our new JSDMs represent an important extension of current approaches to community modeling to the common case where species presence-absence cannot be detected with certainty.

Effects of forest wildfire on inner-Alpine bird community dynamics.

As major disturbance agents, natural catastrophes impact habitats, thereby maintaining the dynamics of ecological communities. Such discrete events are expected to positively affect biodiversity because they generate high habitat heterogeneity and thus numerous ecological niche opportunities. Species typical of open and semi-open habitats, which are often of high conservation concern in modern anthropized landscapes, may benefit most from recurrent natural catastrophes that regularly reset ecosystems. We investigated bird community changes and species-specific responses to wildfire at two recently burnt temperate, montane-subalpine forest stands in an inner-Alpine Swiss valley, with a special focus on red-listed and conservation priority species. We compared bird community changes in burnt forests (spanning 13 years) with bird assemblages occurring in adjacent non-burned forest stands that served as quasi-experimental controls. Strong species-specific responses to wildfire were evidenced, resulting in a dramatic post-fire decrease in overall bird abundance and species richness. Yet, red-listed bird species and conservation priority species in Switzerland were substantially more common in burnt than in control forest stands. Many red-listed species showed a bell-shaped numeric response to wildfire over time, suggesting low habitat suitability just after fire, high habitat suitability at pioneer and early stages of vegetation succession, followed by a long-term decrease in suitability while vegetation becomes denser, especially at ground level. As established for Mediterranean regions where wildfires are especially frequent, this study shows that forest fires can also boost the populations of red-listed and priority bird species typical of open and semi-open habitats in temperate biomes. Prescribed forest fire might represent a management option for preserving threatened elements of biodiversity despite the intense public debate it will trigger.

Integrated population models: powerful methods to embed individual processes in population dynamics models.

Population dynamics models have long assumed that populations are composed of a restricted number of groups, where individuals in each group have identical demographic rates and where all groups are similarly affected by density-dependent and -independent effects. However, individuals usually vary tremendously in performance and in their sensitivity to environmental conditions or resource limitation, such that individual contributions to population growth will be highly variable. Recent efforts to integrate individual processes in population models open up new opportunities for the study of eco-evolutionary processes, such as the density-dependent influence of environmental conditions on the evolution of morphological, behavioral, and life-history traits. We review recent advances that demonstrate how including individual mechanisms in models of population dynamics contributes to a better understanding of the drivers of population dynamics within the framework of integrated population models (IPMs). IPMs allow for the integration in a single inferential framework of different data types as well as variable population structure including sex, social group, or territory, all of which can be formulated to include individual-level processes. Through a series of examples, we first show how IPMs can be beneficial for getting more accurate estimates of demographic traits than classic matrix population models by including basic population structure and their influence on population dynamics. Second, the integration of individual- and population-level data allows estimating density-dependent effects along with their inherent uncertainty by directly using the population structure and size to feedback on demography. Third, we show how IPMs can be used to study the influence of the dynamics of continuous individual traits and individual quality on population dynamics. We conclude by discussing the benefits and limitations of IPMs for integrating data at different spatial, temporal, and organismal levels to build more mechanistic models of population dynamics.

Inferring species richness using multispecies occupancy modeling: Estimation performance and interpretation.

Multispecies occupancy models can estimate species richness from spatially replicated multispecies detection/non-detection survey data, while accounting for imperfect detection. A model extension using data augmentation allows inferring the total number of species in the community, including those completely missed by sampling (i.e., not detected in any survey, at any site). Here we investigate the robustness of these estimates. We review key model assumptions and test performance via simulations, under a range of scenarios of species characteristics and sampling regimes, exploring sensitivity to the Bayesian priors used for model fitting. We run tests when assumptions are perfectly met and when violated. We apply the model to a real dataset and contrast estimates obtained with and without predictors, and for different subsets of data. We find that, even with model assumptions perfectly met, estimation of the total number of species can be poor in scenarios where many species are missed (>15%-20%) and that commonly used priors can accentuate overestimation. Our tests show that estimation can often be robust to violations of assumptions about the statistical distributions describing variation of occupancy and detectability among species, but lower-tail deviations can result in large biases. We obtain substantially different estimates from alternative analyses of our real dataset, with results suggesting that missing relevant predictors in the model can result in richness underestimation. In summary, estimates of total richness are sensitive to model structure and often uncertain. Appropriate selection of priors, testing of assumptions, and model refinement are all important to enhance estimator performance. Yet, these do not guarantee accurate estimation, particularly when many species remain undetected. While statistical models can provide useful insights, expectations about accuracy in this challenging prediction task should be realistic. Where knowledge about species numbers is considered truly critical for management or policy, survey effort should ideally be such that the chances of missing species altogether are low.

Use of classical bird census transects as spatial replicates for hierarchical modeling of an avian community.

New monitoring programs are often designed with some form of temporal replication to deal with imperfect detection by means of occupancy models. However, classical bird census data from earlier times often lack temporal replication, precluding detection-corrected inferences about occupancy. Historical data have a key role in many ecological studies intended to document range shifts, and so need to be made comparable with present-day data by accounting for detection probability. We analyze a classical bird census conducted in the region of Murcia (SE Spain) in 1991 and 1992 and propose a solution to estimating detection probability for such historical data when used in a community occupancy model: the spatial replication of subplots nested within larger plots allows estimation of detection probability. In our study, the basic sample units were 1-km transects, which were considered spatial replicates in two aggregation schemes. We fit two Bayesian multispecies occupancy models, one for each aggregation scheme, and evaluated the linear and quadratic effect of forest cover and temperature, and a linear effect of precipitation on species occupancy probabilities. Using spatial rather than temporal replicates allowed us to obtain individual species occupancy probabilities and species richness accounting for imperfect detection. Species-specific occupancy and community size decreased with increasing annual mean temperature. Both aggregation schemes yielded estimates of occupancy and detectability that were highly correlated for each species, so in the design of future surveys ecological reasons and cost-effective sampling designs should be considered to select the most suitable aggregation scheme. In conclusion, the use of spatial replication may often allow historical survey data to be applied formally hierarchical occupancy models and be compared with modern-day data of the species community to analyze global change process.

Use of ambiguous detections to improve estimates from species distribution models.

As large carnivores recover throughout Europe, their distribution needs to be studied to determine their conservation status and assess the potential for human-carnivore conflicts. However, efficient monitoring of many large carnivore species is challenging due to their rarity, elusive behavior, and large home ranges. Their monitoring can include opportunistic sightings from citizens in addition to designed surveys. Two types of detection errors may occur in such monitoring schemes: false negatives and false positives. False-negative detections can be accounted for in species distribution models (SDMs) that deal with imperfect detection. False-positive detections, due to species misidentification, have rarely been accounted for in SDMs. Generally, researchers use ad hoc data-filtering methods to discard ambiguous observations prior to analysis. These practices may discard valuable ecological information on the distribution of a species. We investigated the costs and benefits of including data types that may include false positives rather than discarding them for SDMs of large carnivores. We used a dynamic occupancy model that simultaneously accounts for false negatives and positives to jointly analyze data that included both unambiguous detections and ambiguous detections. We used simulations to compare the performances of our model with a model fitted on unambiguous data only. We tested the 2 models in 4 scenarios in which parameters that control false-positive detections and true detections varied. We applied our model to data from the monitoring of the Eurasian lynx (Lynx lynx) in the European Alps. The addition of ambiguous detections increased the precision of parameter estimates. For the Eurasian lynx, incorporating ambiguous detections produced more precise estimates of the ecological parameters and revealed additional occupied sites in areas where the species is likely expanding. Overall, we found that ambiguous data should be considered when studying the distribution of large carnivores through the use of dynamic occupancy models that account for misidentification.

Unraveling fine-scale habitat use for secretive species: When and where toads are found when not breeding.

A good understanding of species-habitat associations, or habitat use, is required to establish conservation strategies for any species. Many amphibian species are elusive and most information concerning amphibian habitat use comes from breeding sites where they are comparatively easy to find and study. Knowledge about retreat sites is extremely limited for most species and for the greater part of the year. For such species, it is especially important to factor in detection probability in habitat analyses, because otherwise distorted views about habitat preferences may result, e.g., when a species is more visible in habitat type B than in A, even though A may be preferred. The South American red-belly toad, Melanophryniscus pachyrhynus, is a range-restricted species from Southern Brazil and Uruguay that inhabits open areas with rocky outcrops and is usually seen only during explosive breeding events. Here we studied the fine-scale habitat use of the red-belly toad outside of the breeding season to identify retreat sites and test for the importance of accounting for species imperfect detection, using Bayesian occupancy models. We identified shrub density and the number of loose rocks as important predictors of occupancy, while detection probability was highest at intermediate temperatures. Considering the harsh (dry and hot) conditions of rocky outcrops, shrubs and loose rocks may both work as important refuges, besides providing food resources and protecting against predation. Rocky outcrops have been suffering changes in habitat configuration and we identify nonbreeding habitat preferences at a fine scale, which may help to promote population persistence, and highlight the importance of accounting for imperfect detection when studying secretive species.

Relationships between vital rates and ecological traits in an avian community.

Comparative studies about the relationships between vital rates and ecological traits at the community level are conspicuously lacking for most taxa because estimating vital rates requires detailed demographic data. Identifying relationships between vital rates and ecological traits could help to better understand ecological and evolutionary demographic mechanisms that lead to interspecific differences in vital rates. We use novel dynamic N-mixture models for counts to achieve this for a whole avian community comprising 53 passerine species, while simultaneously accounting for density dependence and environmental stochasticity in recruitment and survival and, importantly, correcting our inferences for imperfect detection. Demographic stochasticity is taken into account in the form of the binomial and Poisson distributions describing survival events and number of recruits. We then explore relationships between estimated demographic parameters (i.e., vital rates) and ecological traits related to migration patterns, diet, habitat and nesting location of each species. The relative importance of recruitment and adult survival as contributors to population growth varied greatly among species, and interspecific differences in vital rates partly reflected differences in ecological traits. Migratory mode was associated with interspecific differences in population growth and density dependence. Resident species had higher population growth rates than long- and short-distance migrants. We found no relationships between diet and population growth rate. Habitat differences were associated with different growth rates: alpine, wetland and farmland species had lower population growth rates than forest species. Differences in population growth rates among nesting locations showed that breeding habitat is essential for population dynamics. Our study reveals relationships between ecological traits and contributions of vital rates to population growth and suggests ways in which patterns of population growth fluctuations in a community might be determined by life history.

Identifiability in N-mixture models: a large-scale screening test with bird data.

Binomial N-mixture models have proven very useful in ecology, conservation, and monitoring: they allow estimation and modeling of abundance separately from detection probability using simple counts. Recently, doubts about parameter identifiability have been voiced. I conducted a large-scale screening test with 137 bird data sets from 2,037 sites. I found virtually no identifiability problems for Poisson and zero-inflated Poisson (ZIP) binomial N-mixture models, but negative-binomial (NB) models had problems in 25% of all data sets. The corresponding multinomial N-mixture models had no problems. Parameter estimates under Poisson and ZIP binomial and multinomial N-mixture models were extremely similar. Identifiability problems became a little more frequent with smaller sample sizes (267 and 50 sites), but were unaffected by whether the models did or did not include covariates. Hence, binomial N-mixture model parameters with Poisson and ZIP mixtures typically appeared identifiable. In contrast, NB mixtures were often unidentifiable, which is worrying since these were often selected by Akaike's information criterion. Identifiability of binomial N-mixture models should always be checked. If problems are found, simpler models, integrated models that combine different observation models or the use of external information via informative priors or penalized likelihoods, may help.

Decomposing functional trait associations in a Chinese subtropical forest.

Functional traits, properties of organisms correlated with ecological performance, play a central role in plant community assembly and functioning. To some extents, functional traits vary in concert, reflecting fundamental ecological strategies. While "trait syndromes" characteristic of e.g. fast-growing, early-successional vs. competitive, late-successional species are recognized in principle, less is known about the environmental and genetic factors at the source of trait variation and covariation within plant communities. We studied the three leaf traits leaf half-life (LHL), leaf mass per area (LMA) and nitrogen concentration in green leaves (Ngreen) and the wood trait wood density (WD) in 294 individuals belonging to 45 tree or shrub species in a Chinese subtropical forest from September 2006 to January 2009. Using multilevel ANOVA and decomposition of sums of products, we estimated the amount of trait variation and covariation among species (mainly genetic causes), i.e. plant functional type (deciduous vs. evergreen species), growth form (tree vs. shrub species), family/genus/species differences, and within species (mainly environmental causes), i.e. individual and season. For single traits, the variation between functional types and among species within functional types was large, but only LMA and Ngreen varied significantly among families and thus showed phylogenetic signal. Trait variation among individuals within species was small, but large temporal variation due to seasonal effects was found within individuals. We did not find any trait variation related to soil conditions underneath the measured individuals. For pairs of traits, variation between functional types and among species within functional types was large, reflecting a strong evolutionary coordination of the traits, with LMA, LHL and WD being positively correlated among each other and negatively with Ngreen. This integration of traits was consistent with a putative stem-leaf economics spectrum ranging from deciduous species with thin, high-nitrogen leaves and low-density wood to evergreen species with thick, low-nitrogen leaves and dense wood and was not influenced by phylogenetic history. Trait coordination within species was weak, allowing individual trees to deviate from the interspecific trait coordination and thus respond flexibly to environmental heterogeneity. Our findings suggest that within a single woody plant community variation and covariation in functional traits allows a large number of species to co-exist and cover a broad spectrum of multivariate niche space, which in turn may increase total resource extraction by the community and community functioning.