Dynamic occupancy models for analyzing species' range dynamics across large geographic scales

Large‐scale biodiversity data are needed to predict species' responses to global change and to address basic questions in macroecology. While such data are increasingly becoming available, their analysis is challenging because of the typically large heterogeneity in spatial sampling intensity and the need to account for observation processes. Two further challenges are accounting for spatial effects that are not explained by covariates, and drawing inference on dynamics at these large spatial scales. We developed dynamic occupancy models to analyze large‐scale atlas data. In addition to occupancy, these models estimate local colonization and persistence probabilities. We accounted for spatial autocorrelation using conditional autoregressive models and autologistic models. We fitted the models to detection/nondetection data collected on a quarter‐degree grid across southern Africa during two atlas projects, using the hadeda ibis (Bostrychia hagedash) as an example. The model accurately reproduced the range expansion between the first (SABAP1: 1987–1992) and second (SABAP2: 2007–2012) Southern African Bird Atlas Project into the drier parts of interior South Africa. Grid cells occupied during SABAP1 generally remained occupied, but colonization of unoccupied grid cells was strongly dependent on the number of occupied grid cells in the neighborhood. The detection probability strongly varied across space due to variation in effort, observer identity, seasonality, and unexplained spatial effects. We present a flexible hierarchical approach for analyzing grid‐based atlas data using dynamical occupancy models. Our model is similar to a species' distribution model obtained using generalized additive models but has a number of advantages. Our model accounts for the heterogeneous sampling process, spatial correlation, and perhaps most importantly, allows us to examine dynamic aspects of species ranges.     

Multivariate analysis of a fine-scale breeding bird atlas using a geographical information system and partial canonical correspondence analysis: environmental and spatial effects

Aim To assess the relative roles of environment and space in driving bird species distribution and to identify relevant drivers of bird assemblage composition, in the case of a fine‐scale bird atlas data set. Location The study was carried out in southern Belgium using grid cells of 1 × 1 km, based on the distribution maps of the Oiseaux nicheurs de Famenne: Atlas de Lesse et Lomme which contains abundance for 103 bird species. Methods Species found in < 10% or > 90% of the atlas cells were omitted from the bird data set for the analysis. Each cell was characterized by 59 landscape metrics, quantifying its composition and spatial patterns, using a Geographical Information System. Partial canonical correspondence analysis was used to partition the variance of bird species matrix into independent components: (a) ‘pure’ environmental variation, (b) spatially‐structured environmental variation, (c) ‘pure’ spatial variation and (d) unexplained, non‐spatial variation. Results The variance partitioning method shows that the selected landscape metrics explain 27.5% of the variation, whilst ‘pure’ spatial and spatially‐structured environmental variables explain only a weak percentage of the variation in the bird species matrix (2.5% and 4%, respectively). Avian community composition is primarily related to the degree of urbanization and the amount and composition of forested and open areas. These variables explain more than half of the variation for three species and over one‐third of the variation for 12 species. Main conclusions The results seem to indicate that the majority of explained variation in species assemblages is attributable to local environmental factors. At such a fine spatial resolution, however, the method does not seem to be appropriated for detecting and extracting the spatial variation of assemblages. Consequently, the large amount of unexplained variation is probably because of missing spatial structures and ‘noise’ in species abundance data. Furthermore, it is possible that other relevant environmental factors, that were not taken into account in this study and which may operate at different spatial scales, can drive bird assemblage structure. As a large proportion of ecological variation can be shared by environment and space, the applied partitioning method was found to be useful when analysing multispecific atlas data, but it needs improvement to factor out all‐scale spatial components of this variation (the source of ‘false correlation’) and to bring out the ‘pure’ environmental variation for ecological interpretation.     

Effects of residual smoothing on the posterior of the fixed effects in disease-mapping models

Disease-mapping models for areal data often have fixed effects to measure the effect of spatially varying covariates and random effects with a conditionally autoregressive (CAR) prior to account for spatial clustering. In such spatial regressions, the objective may be to estimate the fixed effects while accounting for the spatial correlation. But adding the CAR random effects can cause large changes in the posterior mean and variance of fixed effects compared to the nonspatial regression model. This article explores the impact of adding spatial random effects on fixed effect estimates and posterior variance. Diagnostics are proposed to measure posterior variance inflation from collinearity between the fixed effect covariates and the CAR random effects and to measure each region's influence on the change in the fixed effect's estimates by adding the CAR random effects. A new model that alleviates the collinearity between the fixed effect covariates and the CAR random effects is developed and extensions of these methods to point-referenced data models are discussed.     

Some new approaches to conservation monitoring of British breeding birds

It is important to monitor bird populations both in their own right and as indicators of the general health of wildlife habitats. The objectives of the British Trust for Ornithology's Integrated Population Monitoring programme relate to breeding bird populations in Britain and Ireland and involve the estimation of demographic parameters as well as assessment of numbers. Current programmes for monitoring bird numbers cover the majority of British species; it would be feasible to monitor most of the rest. A new Breeding Bird Survey has been developed to provide effective coverage of all regions and all major habitats in Britain through random sampling, allowing for the marked geographical variation in volunteer observer density. The final choice of a random sample stratified by observer density (with some professional support in regions with few volunteer observers) was based on comparison with alternative stratifications, using data from a 2-year pilot study to assess the number of species adequately covered under various alternatives. A method of assessing whether or not targets are being achieved at any time has been developed: it involves looking back through the data at intervals of 1-year, 4-year, 16-year and longer spans. It will be possible to refine this by incorporating environmental and density-dependent effects into predictive models. The method is illustrated here using Common Birds Census data. We discuss associated problems of statistical inference and of taking decisions under uncertainty. The data provide evidence for large declines in some species, particularly in farmland; the value of birds as general indicators of habitat health is clear. The results of monitoring can be used to illuminate possible causes of problems and to guide both practical steps to ameliorate the problems and research aimed at better understanding the causes. Examples of such research are discussed.     

Can Observation Skills of Citizen Scientists Be Estimated Using Species Accumulation Curves?

Volunteers are increasingly being recruited into citizen science projects to collect observations for scientific studies. An additional goal of these projects is to engage and educate these volunteers. Thus, there are few barriers to participation resulting in volunteer observers with varying ability to complete the project’s tasks. To improve the quality of a citizen science project’s outcomes it would be useful to account for inter-observer variation, and to assess the rarely tested presumption that participating in a citizen science projects results in volunteers becoming better observers. Here we present a method for indexing observer variability based on the data routinely submitted by observers participating in the citizen science project eBird, a broad-scale monitoring project in which observers collect and submit lists of the bird species observed while birding. Our method for indexing observer variability uses species accumulation curves, lines that describe how the total number of species reported increase with increasing time spent in collecting observations. We find that differences in species accumulation curves among observers equates to higher rates of species accumulation, particularly for harder-to-identify species, and reveals increased species accumulation rates with continued participation. We suggest that these properties of our analysis provide a measure of observer skill, and that the potential to derive post-hoc data-derived measurements of participant ability should be more widely explored by analysts of data from citizen science projects. We see the potential for inferential results from analyses of citizen science data to be improved by accounting for observer skill.     

Modeling Spatial Trends in Estimated Species Richness using Breeding Bird Survey Data: A Valuable Tool in Biodiversity Assessment

We used data from the French breeding bird survey to estimate local bird species richness within sampled sites, using capture–recapture models. We investigated the possible effects of habitat structure and composition (landscape fragmentation, habitat cover and diversity) on estimated species richness at a local scale, and used the identified trends to help with modeling species richness at a large spatial scale. We performed geostatistical analyses based on spatial autocorrelation – cokriging models – to interpolate estimated species richness over the entire country, providing an opportunity to predict species-rich areas. We further compared species richness obtained with this method to species and rarity richness obtained using a national atlas of breeding birds. Estimated species richness was higher in species richness hotspots identified by the atlas. Combining informations on rare species from Atlas and species richness estimates from sound sampling based schemes should help with identifying species-rich areas for various taxa and locating biodiversity hotspots to be protected as high conservation value areas, especially in temperate zones where diversity hotspots are likely to match centers of high species richness because of very few centers of true endemicity.     

Foraging height and landscape context predict the relative abundance of bird species in urban vegetation patches

In Australian urban environments, revegetation and vegetation restoration are increasingly utilized conservation actions. Simple methods that help assess the utility of urban vegetation for bird species will help direct this effort for bird conservation purposes. We therefore examine whether ecological principles can be used to predict, a priori, the relative abundance of different bird species in urban vegetation. Our model proposes that a bird species will be in greater abundance where vegetation structure better reflects its foraging height requirements, and this relationship will be moderated by the landscape context of the patch. To quantify and test this model, we created an index to rank existing and revegetated urban vegetation sites in order of greatest expected abundance for each of 30 bird species. We tested this model, alongside two simpler models which consider landscape context and foraging height preferences alone, using bird abundance data from 20 woodland remnants and 20 revegetated sites in Brisbane, Australia. From these bird abundance data, we calculated the relative abundance of each species between the top‐ranking sites and lowest‐ranking sites. The model which incorporated both foraging height requirements and landscape context made predictions that were positively correlated with the data for 77% of species in remnant vegetation and 67% in revegetation. The results varied across species groups; for example, we achieved lower predictive success for canopy foraging species in the less mature revegetation sites. Overall, this model provided a reasonable level of predictive accuracy despite the diversity of factors which can influence species occurrence in urban landscapes. The model is generic and, subject to further testing, can be used to examine the effect of manipulating vegetation structure and landscape context on the abundance of different bird species in urban vegetation. This could provide a cost‐effective tool for directing urban restoration and revegetation efforts.     

A comparison of wetland characteristics between Agricultural Conservation Easement Program and public lands wetlands in West Virginia,USA

In West Virginia, USA, there are 24 conservation easement program wetlands enrolled in the Agricultural Conservation Easement Program (ACEP). These wetlands are located on private agricultural land and are passively managed. Due to their location within fragmented agricultural areas, wetlands enrolled in ACEP in West Virginia have the potential to add wetland ecosystem services in areas that are lacking these features. We evaluated ACEP wetlands compared to reference wetlands on public land in West Virginia by using surrounding land cover, vegetative cover, and wetland features and stressors such as the presence or absence of erosion, upland inclusion, algal mats, and evidence of impacts from the surrounding landscape as surrogate measurements of wetland function on 13 ACEP wetlands and 10 reference wetlands. ACEP wetlands had higher percentages of tree coverage and a higher proportion of agricultural land in the areas immediately surrounding the wetland. Reference wetlands had higher percent coverage of emergent vegetation and had a higher proportion of forest in the immediate landscape. Our findings suggest that ACEP wetlands provide valuable early successional and forested wetland cover in a state that is largely forested. Because of this, it is important to maintain and even expand ACEP in West Virginia to continue providing a valuable source of early successional wetland habitat.     

Updating bird species distribution at large spatial scales: applications of habitat modelling to data from long-term monitoring programs

Mapping of species distributions at large spatial scales has been often based on the representation of gathered observations in a general grid atlas framework. More recently, subsampling and subsequent interpolation or habitat spatial modelling techniques have been incorporated in these projects to allow more detailed species mapping. Here, we explore the usefulness of data from long-term monitoring (LTM) projects, primarily aimed at estimating trends in species abundance and collected at shorter time intervals (usually yearly) than atlas data, to develop predictive habitat models. We modelled habitat occupancy for 99 species using a bird LTM program and evaluated the predictive accuracy of these models using independent data from a contemporary and comprehensive breeding bird atlas project from the same region. Habitat models from LTM data using generalized linear modelling were significant for all the species and generally showed a high predictive power, albeit lower than that from atlas models. Sample size and species range size and niche breadth were the most important factors behind variability in model predictive accuracy, whereas the spatial distribution of sampling units at a given sample size had minor effects. Although predictive accuracy of habitat modelling was strongly species dependent, increases in sample size and, secondarily, a better spatial distribution of sampling units should lead to more powerful predictive distribution models. We suggest that data from LTM programs, now established in a large number of countries, has the potential for being a major source of good quality data suitable for the estimation and regularly update of distributions at large spatial scales for a number of species.     

Species-specific features affect the ability of census-derived models to map winter avian distribution

We identify autoecological traits of bird species that influence the accuracy of predictive models of species distribution based on census data obtained from stratified sampling. These models would serve as a complementary approach to the development of regional bird atlases. We model the winter bird abundance of 64 terrestrial bird species in 77 census plots in Central Spain (Madrid province), using regression tree analyses. The predicted distribution of species density derived from statistical models (birds/10 ha) was compared with the published relative abundances depicted by a very accurate regional atlas of wintering birds (birds observed per 10 h). Statistical models explained an average of 41.7% of the original deviance observed in the local bird distribution (range 19.6–79.3%). Significant associations between observed relative abundances (atlas data) and predicted average densities in 1×1 km squares within 10×10 km UTMs were attained for 44 out of 64 species. Interspecific discrepancies between predicted and observed distribution maps decreased with between-year constancy in regional bird distribution and the degree of ecological specialization of species. Therefore, statistical modeling using census localities allowed us to depict geographical variations in bird abundance that were similar to those in the quantitative atlas maps. Nevertheless, bird distributions derived from statistical models are less reproducible in some species than in others, depending on their autoecological traits.     

Optimising long‐term monitoring projects for species distribution modelling: how atlas data may help

Long‐term biodiversity monitoring data are mainly used to estimate changes in species occupancy or abundance over time, but they may also be incorporated into predictive models to document species distributions in space. Although changes in occupancy or abundance may be estimated from a relatively limited number of sampling units, small sample size may lead to inaccurate spatial models and maps of predicted species distributions. We provide a methodological approach to estimate the minimum sample size needed in monitoring projects to produce accurate species distribution models and maps. The method assumes that monitoring data are not yet available when sampling strategies are to be designed and is based on external distribution data from atlas projects. Atlas data are typically collected in a large number of sampling units during a restricted timeframe and are often similar in nature to the information gathered from long‐term monitoring projects. The large number of sampling units in atlas projects makes it possible to simulate a broad gradient of sample sizes in monitoring data and to examine how the number of sampling units influences the accuracy of the models. We apply the method to several bird species using data from a regional breeding bird atlas. We explore the effect of prevalence, range size and habitat specialization of the species on the sample size needed to generate accurate models. Model accuracy is sensitive to particularly small sample sizes and levels off beyond a sufficiently large number of sampling units that varies among species depending mainly on their prevalence. The integration of spatial modelling techniques into monitoring projects is a cost‐effective approach as it offers the possibility to estimate the dynamics of species distributions in space and over time. We believe our innovative method will help in the sampling design of future monitoring projects aiming to achieve such integration.     

Decline of birds in a human modified coastal dune forest landscape in South Africa

Previous studies demonstrate that old-growth forest remnants and vegetation regenerating after anthropogenic disturbance provide habitat for birds in a human modified coastal dune forest landscape in northern KwaZulu-Natal, South Africa. However, occurrence does not ensure persistence. Based on a 13-year monitoring database we calculated population trends for 37 bird species and general trends in overall bird density in different vegetation types. We evaluated species' characteristics as covariates of population trend and assessed changes in rainfall and proportional area and survey coverage per vegetation type. 76% of species assessed have declined, 57% significantly so at an average rate of 13.9% per year. Overall, bird density has fallen at 12.2% per year across old-growth forest and woody regenerating vegetation types. Changes in proportional area and coverage per vegetation type may partly explain trends for a few species but are unlikely to account for most. Below average rainfall may have contributed to bird declines. However, other possibilities warrant further investigation. Species with larger range extents tended to decline more sharply than did others, and these species may be responding to environmental changes on a broader geographical scale. Our results cast doubt on the future persistence of birds in this human modified landscape. More research is needed to elucidate the mechanisms driving population decline in the study area and to investigate whether the declines identified here are more widespread across the region and perhaps the continent.     

A hierarchical nest survival model integrating incomplete temporally varying covariates

Nest success is a critical determinant of the dynamics of avian populations, and nest survival modeling has played a key role in advancing avian ecology and management. Beginning with the development of daily nest survival models, and proceeding through subsequent extensions, the capacity for modeling the effects of hypothesized factors on nest survival has expanded greatly. We extend nest survival models further by introducing an approach to deal with incompletely observed, temporally varying covariates using a hierarchical model. Hierarchical modeling offers a way to separate process and observational components of demographic models to obtain estimates of the parameters of primary interest, and to evaluate structural effects of ecological and management interest. We built a hierarchical model for daily nest survival to analyze nest data from reintroduced whooping cranes (Grus americana) in the Eastern Migratory Population. This reintroduction effort has been beset by poor reproduction, apparently due primarily to nest abandonment by breeding birds. We used the model to assess support for the hypothesis that nest abandonment is caused by harassment from biting insects. We obtained indices of blood‐feeding insect populations based on the spatially interpolated counts of insects captured in carbon dioxide traps. However, insect trapping was not conducted daily, and so we had incomplete information on a temporally variable covariate of interest. We therefore supplemented our nest survival model with a parallel model for estimating the values of the missing insect covariates. We used Bayesian model selection to identify the best predictors of daily nest survival. Our results suggest that the black fly Simulium annulus may be negatively affecting nest survival of reintroduced whooping cranes, with decreasing nest survival as abundance of S. annulus increases. The modeling framework we have developed will be applied in the future to a larger data set to evaluate the biting‐insect hypothesis and other hypotheses for nesting failure in this reintroduced population; resulting inferences will support ongoing efforts to manage this population via an adaptive management approach. Wider application of our approach offers promise for modeling the effects of other temporally varying, but imperfectly observed covariates on nest survival, including the possibility of modeling temporally varying covariates collected from incubating adults.     

Local environmental effects and spatial effects in macroecological studies using mapped abundance classes: the case of the rook Corvus frugilegus in Scotland

1. The study of the spatial pattern of species abundance is complicated by statistical problems, such as spatial autocorrelation of the abundance data, which lead to the confusion of environmental effects and dispersal. 2. Atlas-derived data for the rook in Scotland are used as a case study to propose an approach for assessing the likely contribution of dispersal and local environmental effects, based on a Bayesian Conditional Autoregressive (CAR) approach. 3. The availability of moist grasslands is a key factor explaining the spatial pattern of abundance. This is influenced by a combination of climatic and soil-related factors. A direct link to soil properties is for the first time reported for the wide-scale distribution of a bird species. In addition, for this species, dispersal seems to contribute significantly to the spatial pattern and produces a smoother than expected decline in abundance at the north-western edge of its distribution range. Areas where dispersal is most likely to be important are highlighted. 4. The approach described can help ecologists make more efficient use of atlas data for the investigation of the structure of species abundance, and can highlight potential sink areas at the landscape and regional scale. 5. Bayesian spatial models can deal with data autocorrelation in atlas-type data, while clearly communicating uncertainty through the estimation of the full posterior probability distribution of all parameters.     

Improving the analysis of movement data from marked individuals through explicit estimation of observer heterogeneity

Ring re-encounter data, in particular ring recoveries, have made a large contribution to our understanding of bird movements. However, almost every study based on ring re-encounter data has struggled with the bias caused by unequal observer distribution. Re-encounter probabilities are strongly heterogeneous in space and over time. If this heterogeneity can be measured or at least controlled for, the enormous number of ring re-encounter data collected can be used effectively to answer many questions. Here, we review four different approaches to account for heterogeneity in observer distribution in spatial analyses of ring re-encounter data. The first approach is to measure re-encounter probability directly. We suggest that variation in ring re-encounter probability could be estimated by combining data whose re-encounter probabilities are close to one (radio or satellite telemetry) with data whose re-encounter probabilities are low (ring re-encounter data). The second approach is to measure the spatial variation in re-encounter probabilities using environmental covariates. It should be possible to identify powerful predictors for ring re-encounter probabilities. A third approach consists of the comparison of the actual observations with all possible observations using randomization techniques. We encourage combining such randomisations with ring re-encounter models that we discuss as a fourth approach. Ring re-encounter models are based on the comparison of groups with equal re-encounter probabilities. Together these four approaches could improve our understanding of bird movements considerably. We discuss their advantages and limitations and give directions for future research.     

Landscape composition models for breeding bird populations in lowland English farmland over a 20 year period

The aim of this paper was to assess which components of landscape structure in lowland English farmland could best predict patterns m bird community composition, total bird density and densities of 12 bird species As far as possible, predictors were sought that were consistent m their effects over a 20 yr period The data were derived from mapping censuses in 1966, 1970, 1974, 1978, 1982 and 1986 with yearly sample sizes ranging from 41 to 51 plots, of minimum size 50 ha Landscape composition was quantified in terms of 10 landscape variables representing broad structural (e g density of woodland) and crop attributes (e g proportion of area under cereals) plus altitude Gradients in bird community composition were defined as plot scores on axis 1 of a detrended correspondence analysis (AXIS1) This gradient consistently represented a gradient from plots rich in woodland bird species to ones rich in field-feeding and wetland species Multiple regression was used to build models containing 2-, 3- and 4-vanables Two approaches were used to combine data from the six years and models based on the single best subset in each year were found to be most consistent with respect to predictor selection Predictors were tested by building models based on each combination of five years and assessing how well the predictors explained variation in the bird variables for the sixth, independent year The predictors were relatively successful in explaining variations in densities of wren, robin, willow warbler, blue tit, great tit and chaffinch but especially AXIS1 which was explained in terms of woodland density, field size and altitude The predictors were unsuccessful in explaining total bird density, song thrush density and whitethroat density Structural attributes of landscape (especially density of hedgerow and woodland, but also rivers and ponds) were frequent predictors of species densities, but variables relating to farming system were not, with the exception of skylark and yellowhammer which were positively associated with extent of cereal crops Implications for landscape design and management, and the relative contribution of woodland and hedgerows to bird communities m farmland landscapes are discussed There was considerable variation between individual years which suggests that models of bird-habitat relationships based on single years may have limited general application     

Projecting species’ vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?

Species distribution models (SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross‐validation skill indicates extrapolative skill. We compared projected vulnerability for 29 wetland‐dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross‐validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross‐validation results were correlated with extrapolation results, the use of cross‐validation performance metrics to guide modeling choices where knowledge is limited was supported.     

Functional homogenization of bird communities along habitat gradients: accounting for niche multidimensionality

Aim To test whether functional homogenization of bird communities is promoted by anthropogenic landscape transformation, using specialization and habitat preference indices that account for the multidimensionality of niches. Location Catalonia, north‐east Iberian Peninsula. Methods We used data on bird species occurrences and landscape features in 2834 1‐km² squares. Three orthogonal landscape gradients, which were taken as niche dimensions, were defined by means of principal components analysis (PCA). Specialization and habitat preference indices were created for 103 terrestrial bird species on the basis of their frequency of occurrence variation along the landscape gradients. These indices, together with species rarity, were then averaged for bird communities. We then analysed the patterns of variation of communities’ mean specialization, mean rarity and mean habitat preference values along a gradient of agricultural–forest habitat mosaics. Results Wherever we found a significant variation in the degree of specialization along the agricultural–forest gradient, agricultural habitats held more specialized bird communities than did forest ones and bore, on average, rarer species. Thus, results contradicted our initial hypothesis that humanized areas would bear more functionally homogenized bird communities. Higher α‐diversity values tended to be associated with generalist communities and with those having rarer species. Main conclusions Estimations of bird community specialization for different niche dimensions can behave differently along certain landscape gradients, and some of these differences can be explained by the variation of mean habitat preferences. Thus, we argue that a multidimensional approach to assess average niche breadth of communities can be more informative than a unidimensional measure. Our results suggest that widespread land abandonment and current secondary forest expansion throughout the Mediterranean area are promoting functional homogenization of bird communities. It would be desirable to construct larger‐scale indicators of functional homogenization in order to monitor communities’ responses to widespread landscape changes.     

An empirical comparison of models for the phenology of bird migration

Bird migration phenology shows strong responses to climate change. Studies of trends and patterns in phenology are typically based on annual summarizing metrics, such as means and quantiles calculated from raw daily count data. However, with irregularly sampled data and large day‐to‐day variation, such metrics can be biased and noisy, and may be analysed using phenological functions fitted to the data. Here we use count data of migration passage from a Finnish bird observatory to compare different models for the phenological distributions of spring migration (27 species) and autumn migration (57 species). We assess parsimony and goodness‐of‐fit in a set of models, with phenological functions of different complexity, optionally with covariates accounting for day‐to‐day variability. The covariates describe migration intensities of related species or relative migration intensities the previous day (autocovariates). We found that parametric models are often preferred over the more flexible generalized additive models with constrained degrees of freedom. Models corresponding to a mixture of two distinct passing populations were frequently preferred over simpler ones, but usually no more complex models are needed. Slightly more complex models were favoured in spring compared to autumn. Related species’ migration activity effectively improves the model by accounting for the large day‐to‐day variation. Autocovariates were usually not that relevant, implying that autocorrelation is generally not a major concern if phenology is modelled properly. We suggest that parametric models are relatively good for studying single‐population migration phenology, or a mix of two groups with distinct phenologies, especially if daily variation in migration intensity can be controlled for. Generalized additive models may be useful when the migrating population composition is unknown. Despite these guidelines, choosing an appropriate model involves case‐by‐case assessment or the biological relevance and rationale for modelling phenology.