Development of a stochastic dynamic model for ecological indicators’ prediction in changed Mediterranean agroecosystems of north-eastern Portugal

A holistic stochastic dynamic model was developed by focusing on the interactions between conceptually isolated key-components, such as local passerine guilds and changes in habitat conditions, in Mediterranean agroecosystems of the “Terra Quente Transmontana region” (north-eastern Portugal). The ecological integrity of the typical patchwork of this region, with respect to land use, can be partly assessed by the observation of the occurrence of passerine guilds. These important indicators and state variables are the underlying database of our model. This model aimed the prediction of the ecological changes which can be expected when olive orchards are being intensified. The model proposed was preceded by a conventional multivariate statistical procedure (stepwise multiple regression analysis) performed to discriminate the significant relationships between guild richness and environmental variables. Since this statistical analysis is static, the dataset recorded from the field included true gradients of habitat changes. The model parameters were estimated from the results of the stochastic treatment and from regional data regarding tendencies within the use of land. A period of 50 years was considered. The final model provided some basis to analyse the responses of passerine guilds to the environmental scenarios that will characterize the new agroecosystems of the region. The model simulations were incorporated into a Geographical Information System (GIS) approach. The results of the simulation revealed a structural drift within the different guild richness in response to the expected gradient of habitat changes. The possible local extinction of several species within the less well-represented guilds, such as the steppe passerine species, may be associated with a predictable reduction in ecological integrity of the typical agroecosystems. Therefore, a new structure of the passerine communities indicates that future agroecosystems will diverge from the initial or actual ecological state.     

Designing ecological climate change impact assessments to reflect key climatic drivers

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive – such as means or extremes – can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the ‘model space’ approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling.     

Toward ecologically scaled landscape indices

Nature conservation is increasingly based on a landscape approach rather than a species approach. Landscape planning that includes nature conservation goals requires integrated ecological tools. However, species differ widely in their response to landscape change. We propose a framework of ecologically scaled landscape indices that takes into account this variation. Our approach is based on a combination of field studies of spatially structured populations (metapopulations) and model simulations in artificial landscapes. From these, we seek generalities in the relationship among species features, landscape indices, and metapopulation viability. The concept of ecological species profiles is used to group species according to characteristics that are important in metapopulations' response to landscape change: individual area requirements as the dominant characteristic of extinction risk in landscape patches and dispersal distance as the main determinant of the ability to colonize patches. The ecological profiles and landscape indices are then integrated into two ecologically scaled landscape indices (ESLI): average patch carrying capacity and average patch connectivity. The field data show that the fraction of occupied habitat patches is correlated with the two ESLI. To put the ESLI into a perspective of metapopulation persistence, we determine the viability for six ecological profiles at different degrees of habitat fragmentation using a metapopulation model and computer-generated landscapes. The model results show that the fraction of occupied patches is a good indicator for metapopulation viability. We discuss how ecological profiles, ESLI, and the viability threshold can be applied for landscape planning and design in nature conservation.     

Choice of baseline climate data impacts projected species' responses to climate change

Climate data created from historic climate observations are integral to most assessments of potential climate change impacts, and frequently comprise the baseline period used to infer species‐climate relationships. They are often also central to downscaling coarse resolution climate simulations from General Circulation Models (GCMs) to project future climate scenarios at ecologically relevant spatial scales. Uncertainty in these baseline data can be large, particularly where weather observations are sparse and climate dynamics are complex (e.g. over mountainous or coastal regions). Yet, importantly, this uncertainty is almost universally overlooked when assessing potential responses of species to climate change. Here, we assessed the importance of historic baseline climate uncertainty for projections of species' responses to future climate change. We built species distribution models (SDMs) for 895 African bird species of conservation concern, using six different climate baselines. We projected these models to two future periods (2040–2069, 2070–2099), using downscaled climate projections, and calculated species turnover and changes in species‐specific climate suitability. We found that the choice of baseline climate data constituted an important source of uncertainty in projections of both species turnover and species‐specific climate suitability, often comparable with, or more important than, uncertainty arising from the choice of GCM. Importantly, the relative contribution of these factors to projection uncertainty varied spatially. Moreover, when projecting SDMs to sites of biodiversity importance (Important Bird and Biodiversity Areas), these uncertainties altered site‐level impacts, which could affect conservation prioritization. Our results highlight that projections of species' responses to climate change are sensitive to uncertainty in the baseline climatology. We recommend that this should be considered routinely in such analyses.     

Framework for quantifying population responses to disturbance reveals that coastal birds are highly resilient to hurricanes

Changes in the frequency and severity of extreme weather may introduce new threats to species that are already under stress from gradual habitat loss and climate change. We provide a probabilistic framework that quantifies potential threats by applying concepts from ecological resilience to single populations. Our approach uses computation to compare disturbance–impacted projections to a population's normal range of variation, quantifying the full range of potential impacts. We illustrate this framework with projection models for coastal birds, which are commonly depicted as vulnerable to disturbances, especially hurricanes and oil spills. We found that populations of coastal specialists are resilient to extreme disturbances, with high resistance to the effects of short‐term reductions in vital rates and recovery within 20 years. Applying the general framework presented here across disturbance‐prone species and ecosystems would improve understanding of population resilience and generate specific projections of resilience that are needed for effective conservation planning.     

Inferring macro‐ecological patterns from local presence/absence data

Biodiversity provides support for life, vital provisions, regulating services and has positive cultural impacts. It is therefore important to have accurate methods to measure biodiversity, in order to safeguard it when we discover it to be threatened. For practical reasons, biodiversity is usually measured at fine scales whereas diversity issues (e.g. conservation) interest regional or global scales. Moreover, biodiversity may change across spatial scales. It is therefore a key challenge to be able to translate local information on biodiversity into global patterns. Many databases give no information about the abundances of a species within an area, but only its occurrence in each of the surveyed plots. In this paper, we introduce an analytical framework (implemented in a ready‐to‐use R code) to infer species richness and abundances at large spatial scales in biodiversity‐rich ecosystems when species presence/absence information is available on various scattered samples (i.e. upscaling). This framework is based on the scale‐invariance property of the negative binomial. Our approach allows to infer and link within a unique framework important and well‐known biodiversity patterns of ecological theory, such as the species accumulation curve (SAC) and the relative species abundance (RSA) as well as a new emergent pattern, which is the relative species occupancy (RSO). Our estimates are robust and accurate, as confirmed by tests performed on both in silico‐generated and real forests. We demonstrate the accuracy of our predictions using data from two well‐studied forest stands. Moreover, we compared our results with other popular methods proposed in the literature to infer species richness from presence to absence data and we showed that our framework gives better estimates. It has thus important applications to biodiversity research and conservation practice.     

Long‐term ecological data for conservation: Range change in the black‐billed capercaillie (Tetrao urogalloides) in northeast China (1970s–2070s)

Long‐term ecological data can be an effective tool to help ecologists integrate future projections with historical contexts and provide unique insights into the long‐term dynamics of endangered species. However, hampered by data limitations, including incomplete and spatially biased data, relatively few studies have used multidecadal datasets or have examined changes in biogeography from a historical perspective. The black‐billed capercaillie (Tetrao urogalloides) is a large capercaillie (classified as Least Concern [LC] on the IUCN red list) that has undergone a dramatic decline in population during the late 20th century and is considered endangered. Its conservation status is pessimistic, and the species requires immediate protection. Therefore, we supplemented a historical dataset to identify changes in this bird's range and population in northeast China over the long term. The study area spanned Heilongjiang Province, Jilin Province, and the northeast corner of Inner Mongolia in northeast China. We integrated an ecological niche model (BIOMOD2) with long‐term ecological data on this species to estimate the magnitude of change in distribution over time. Our results revealed a 35.25% reduction in the current distribution of this species compared to their potential distribution in the 1970s. This decline is expected to continue under climate change. For example, the future range loss was estimated to be 38.79 ± 0.22% (8.64–90.19%), and the actual state could be worse, because the baseline range of the model was greater than the real range in the 2000s, showing a 12.39% overestimation. To overcome this poor outlook, a conservation strategy should be established in sensitive areas, including the southwestern Greater Khingan Mountains and northern Lesser Khingan Mountains. Actions that should be considered include field investigations, establishing a monitor network, designing ecological corridors, and cooperating with local inhabitants, governments, and conservation biologists to improve the conservation of the black‐billed capercaillie.     

The impact of forest encroachment after agricultural land abandonment on passerine bird communities: The case of Greece

Agricultural land abandonment is one of the main drivers of land use change, leading to various responses of farmland ecological communities. In an effort to better understand the effect of agricultural land abandonment on passerine bird communities, we sampled 20 randomly selected sites [1 km × 1 km] in remote Greek mountains, reflecting an abandonment gradient, in terms of forest encroachment. We sampled 169 plots using the point count method of fixed distance (47 passerine species), and we investigated bird diversity and community structure turnover along the gradient. We found that grazing intensity has a beneficial effect hampering forest encroachment that follows progressively land abandonment. Habitat composition changes gradually with forests developing at the expense of open meadows and heterogeneous grasslands. Forest encroachment has a significant negative effect on bird diversity and species richness, affecting in particular typical farmland and Mediterranean shrubland species. Birds form five distinct ecological clusters after land abandonment: species mostly found in pinewoods and cavity-dwelling species; species that prefer open forests forest edges or ecotones; species that prefer shrubland or open habitats with scattered woody vegetation; Mediterranean farmland birds that prefer semi-open habitats with hedges and/or woodlots; and, generalist forest-dwelling or shrubland species. We extracted a set of 22 species to represent the above ecological communities, as a new monitoring tool for agricultural land use change and conservation. We suggest that the maintenance of rural mosaics should be included in the priorities of agricultural policy for farmland bird diversity conservation.     

The Combined Use of Correlative and Mechanistic Species Distribution Models Benefits Low Conservation Status Species

Species can respond to climate change by tracking appropriate environmental conditions in space, resulting in a range shift. Species Distribution Models (SDMs) can help forecast such range shift responses. For few species, both correlative and mechanistic SDMs were built, but allis shad (Alosa alosa), an endangered anadromous fish species, is one of them. The main purpose of this study was to provide a framework for joint analyses of correlative and mechanistic SDMs projections in order to strengthen conservation measures for species of conservation concern. Guidelines for joint representation and subsequent interpretation of models outputs were defined and applied. The present joint analysis was based on the novel mechanistic model GR3D (Global Repositioning Dynamics of Diadromous fish Distribution) which was parameterized on allis shad and then used to predict its future distribution along the European Atlantic coast under different climate change scenarios (RCP 4.5 and RCP 8.5). We then used a correlative SDM for this species to forecast its distribution across the same geographic area and under the same climate change scenarios. First, projections from correlative and mechanistic models provided congruent trends in probability of habitat suitability and population dynamics. This agreement was preferentially interpreted as referring to the species vulnerability to climate change. Climate change could not be accordingly listed as a major threat for allis shad. The congruence in predicted range limits between SDMs projections was the next point of interest. The difference, when noticed, required to deepen our understanding of the niche modelled by each approach. In this respect, the relative position of the northern range limit between the two methods strongly suggested here that a key biological process related to intraspecific variability was potentially lacking in the mechanistic SDM. Based on our knowledge, we hypothesized that local adaptations to cold temperatures deserved more attention in terms of modelling, but further in conservation planning as well.     

The development of appropriate ecological criteria and indicators for community forest conservation using participatory methods: A case study in northeastern Thailand

The paper reports the development and evaluation of relevant local ecological criteria and indicators for participatory resource management of community forest. The study site, the Nong Meg-Nong Hee community forest, Maha Sarakham Province, is in northeastern Thailand. Multi-criteria decision-making (MCDM) was adopted as a decision-making tool to evaluate criteria and indicators, using coarse and fine screening, based on local community participation. The criteria and indicators set consisted of 3 principles, 7 criteria, and 41 indicators covering the main attributes of forest ecosystem conservation. Relevant data were processed in a hierarchical framework and used as a template for further assessment, i.e., Principle 1 (forest ecosystem structure and composition), Principle 2 (forest ecosystem function), and Principle 3 (disturbance signs). The two selection phases comprised (1) the coarse screening based on scoring and ranking, and (2) fine screening, using a pair-wise comparison analysis to classify the order of relative weights of the indicators (importance value) and the consistency index (CI) of each criterion. As a result of coarse screening one criterion and 16 indicators were eliminated from the analysis, while the remaining 3 principles, 6 criteria, and 25 indicators were retained for fine screening. Most criteria showed an acceptance value of less than 10% (tolerance consistency index threshold level). The final set of criteria and indicators, based on locally understandable ecological concepts of forest conservation, was ranked in order of importance under each criterion and applied to the study area. We conclude that these techniques are appropriate for selecting criteria and indicators, as they are relatively transparent, understandable and offer an input to participatory decision-making.     

Unravelling the structure of species extinction risk for predictive conservation science

Extinction risk varies across species and space owing to the combined and interactive effects of ecology/life history and geography. For predictive conservation science to be effective, large datasets and integrative models that quantify the relative importance of potential factors and separate rapidly changing from relatively static threat drivers are urgently required. Here, we integrate and map in space the relative and joint effects of key correlates of The International Union for Conservation of Nature-assessed extinction risk for 8700 living birds. Extinction risk varies significantly with species' broad-scale environmental niche, geographical range size, and life-history and ecological traits such as body size, developmental mode, primary diet and foraging height. Even at this broad scale, simple quantifications of past human encroachment across species' ranges emerge as key in predicting extinction risk, supporting the use of land-cover change projections for estimating future threat in an integrative setting. A final joint model explains much of the interspecific variation in extinction risk and provides a remarkably strong prediction of its observed global geography. Our approach unravels the species-level structure underlying geographical gradients in extinction risk and offers a means of disentangling static from changing components of current and future threat. This reconciliation of intrinsic and extrinsic, and of past and future extinction risk factors may offer a critical step towards a more continuous, forward-looking assessment of species' threat status based on geographically explicit environmental change projections, potentially advancing global predictive conservation science.     

Modelling changes in species’ abundance in response to projected climate change

Aim Existing climate envelope models give an indication of broad scale shifts in distribution, but do not specifically provide information on likely future population changes useful for conservation prioritization and planning. We demonstrate how these techniques can be developed to model likely future changes in absolute density and population size as a result of climate change. Location Great Britain. Methods Generalized linear models were used to model breeding densities of two northerly‐ and two southerly‐distributed bird species as a function of climate and land use. Models were built using count data from extensive national bird monitoring data and incorporated detectability to estimate absolute abundance. Projections of likely future changes in the distribution and abundance of these species were made by applying these models to projections of future climate change under two emissions scenarios. Results Models described current spatial variation in abundance for three of the four species and produced modelled current estimates of national populations that were similar to previously published estimates for all species. Climate change was projected to result in national population declines in the two northerly‐distributed species, with declines for Eurasian curlew Numenius arquata projected to be particularly severe. Conversely, the abundances of the two southerly distributed species were projected to increase nationally. Projected maps of future abundance may be used to identify priority areas for the future conservation of each species. Main conclusions The analytical methods provide a framework to make projections of impacts of climate change on species abundance, rather than simply projected range changes. Outputs may be summarized at any spatial scale, providing information to inform future conservation planning at national, regional and local scales. Results suggest that as a consequence of climate change, northerly distributed bird species in Great Britain are likely to become an increasingly high conservation priority within the UK.     

Social learning in birds and its role in shaping a foraging niche

We briefly review the literature on social learning in birds, concluding that strong evidence exists mainly for predator recognition, song, mate choice and foraging. The mechanism of local enhancement may be more important than imitation for birds learning to forage, but the former mechanism may be sufficient for faithful transmission depending on the ecological circumstances. To date, most insights have been gained from birds in captivity. We present a study of social learning of foraging in two passerine birds in the wild, where we cross-fostered eggs between nests of blue tits, Cyanistes caeruleus and great tits, Parus major. Early learning causes a shift in the foraging sites used by the tits in the direction of the foster species. The shift in foraging niches was consistent across seasons, as showed by an analysis of prey items, and the effect lasted for life. The fact that young birds learn from their foster parents, and use this experience later when subsequently feeding their own offspring, suggests that foraging behaviour can be culturally transmitted over generations in the wild. It may therefore have both ecological and evolutionary consequences, some of which are discussed.     

Conservation efforts based on local ecological knowledge: The role of social variables in identifying environmental indicators

The incorporation of local ecological knowledge in monitoring processes has been one of the great challenges of conservation initiatives worldwide. Therefore, it is essential to use indicators as local evaluation tools of the conditions of a species in order to support conservation actions. Local populations observe the environment, climate change and the influence of these factors on the species they use. However, their observations and perceptions may vary depending on different social factors. We used as model two species of economic importance involved in sociobiodiversity product chains to evaluate the role of social variables in the identification of conservation indicators for this plants. The species studied were: Caryocar coriaceum Wittm. (locally known as pequi), and Himatanthus drasticus (Mart.) Plumel (locally known as janaguba). We also registered which indicators are perceived as the most important and what they are measuring. Our results show that the knowledge among collectors is homogeneous and that, generally, the social factors do not affect the knowledge on local indicators. Age and extraction time were factors that influenced the knowledge on climate indicators and population structure only for C. coriaceum. In the communities studied, collectors not only monitor the biological characteristics of the species, but also the environmental and climatic phenomena that are threatening the sustainability of the species. These results can help to improve our ability to manage information about natural resources, incorporating local ecological knowledge in the scientific process of evaluation and monitoring of biodiversity.     

The role of climate,water and biotic interactions in shaping biodiversity patterns in arid environments across spatial scales

Aim

Desert ecosystems, with their harsh environmental conditions, hold the key to understanding the responses of biodiversity to climate change. As desert community structure is influenced by processes acting at different spatial scales, studies combining multiple scales are essential for understanding the conservation requirements of desert biota. We investigated the role of environmental variables and biotic interactions in shaping broad and fine‐scale patterns of diversity and distribution of bats in arid environments to understand how the expansion of nondesert species can affect the long‐term conservation of desert biodiversity.

Location

Levant, Eastern Mediterranean.

Methods

We combine species distribution modelling and niche overlap statistics with a statistical model selection approach to integrate interspecific interactions into broadscale distribution models and fine‐scale analysis of ecological requirements. We focus on competition between desert bats and mesic species that recently expanded their distribution into arid environment following anthropogenic land‐use changes.

Results

We show that both climate and water availability limit bat distributions and diversity across spatial scales. The broadscale distribution of bats was determined by proximity to water and high temperatures, although the latter did not affect the distribution of mesic species. At the fine‐scale, high levels of bat activity and diversity were associated with increased water availability and warmer periods. Desert species were strongly associated with warmer and drier desert types. Range and niche overlap were high among potential competitors, but coexistence was facilitated through fine‐scale spatial partitioning of water resources.

Main conclusions

Adaptations to drier and warmer conditions allow desert‐obligate species to prevail in more arid environments. However, this competitive advantage may disappear as anthropogenic activities encroach further into desert habitats. We conclude that reduced water availability in arid environments under future climate change projections pose a major threat to desert wildlife because it can affect survival and reproductive success and may increase competition over remaining water resources.     

Predicting when climate‐driven phenotypic change affects population dynamics

Species' responses to climate change are variable and diverse, yet our understanding of how different responses (e.g. physiological, behavioural, demographic) relate and how they affect the parameters most relevant for conservation (e.g. population persistence) is lacking. Despite this, studies that observe changes in one type of response typically assume that effects on population dynamics will occur, perhaps fallaciously. We use a hierarchical framework to explain and test when impacts of climate on traits (e.g. phenology) affect demographic rates (e.g. reproduction) and in turn population dynamics. Using this conceptual framework, we distinguish four mechanisms that can prevent lower‐level responses from impacting population dynamics. Testable hypotheses were identified from the literature that suggest life‐history and ecological characteristics which could predict when these mechanisms are likely to be important. A quantitative example on birds illustrates how, even with limited data and without fully‐parameterized population models, new insights can be gained; differences among species in the impacts of climate‐driven phenological changes on population growth were not explained by the number of broods or density dependence. Our approach helps to predict the types of species in which climate sensitivities of phenotypic traits have strong demographic and population consequences, which is crucial for conservation prioritization of data‐deficient species.     

Effects of Nutrient Heterogeneity and Competition on Root Architecture of Spruce Seedlings: Implications for an Essential Feature of Root Foraging

Background

We have limited understanding of root foraging responses when plants were simultaneously exposed to nutrient heterogeneity and competition, and our goal was to determine whether and how plants integrate information about nutrients and neighbors in root foraging processes.

Methodology/Principal Findings

The experiment was conducted in split-containers, wherein half of the roots of spruce (Picea asperata) seedlings were subjected to intraspecific root competition (the vegetated half), while the other half experienced no competition (the non-vegetated half). Experimental treatments included fertilization in the vegetated half (FV), the non-vegetated half (FNV), and both compartments (F), as well as no fertilization (NF). The root architecture indicators consisted of the number of root tips over the root surface (RTRS), the length percentage of diameter-based fine root subclasses to total fine root (SRLP), and the length percentage of each root order to total fine root (ROLP). The target plants used novel root foraging behaviors under different combinations of neighboring plant and localized fertilization. In addition, the significant increase in the RTRS of 0–0.2 mm fine roots after fertilization of the vegetated half alone and its significant decrease in fertilizer was applied throughout the plant clearly showed that plant root foraging behavior was regulated by local responses coupled with systemic control mechanisms.

Conclusions/Significance

We measured the root foraging ability for woody plants by means of root architecture indicators constructed by the roots possessing essential nutrient uptake ability (i.e., the first three root orders), and provided new evidence that plants integrate multiple forms of environmental information, such as nutrient status and neighboring competitors, in a non-additive manner during the root foraging process. The interplay between the responses of individual root modules (repetitive root units) to localized environmental signals and the systemic control of these responses may well account for the non-additive features of the root foraging process.     

A framework for assessing the vulnerability of species to climate change: a case study of the Australian elapid snakes

Frameworks that provide a system for assessing species according to their vulnerability to climate change can offer considerable guidance to conservation managers who need to allocate limited resources among a large number of taxa. To date, climate change vulnerability assessments have largely been based on projected changes in range size derived from the output of species distribution models (SDMs). A criticism of risk assessments based solely on these models is that information on species ecological and life history traits is lacking. Accordingly, we developed a points-based framework for assessing species vulnerability to climate change that considered species traits together with the projections of SDMs. Applying this method to the Australian elapid snakes (family Elapidae), we determined which species may be particularly susceptible in the future and assessed broad-scale biogeographic patterns in species vulnerability. By offering a more comprehensive and rigorous method for assessing vulnerability than those based solely on SDMs, this framework provides greater justification for resource allocation, and can help guide decisions regarding the most appropriate adaptation strategies.     

Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics

Aim The study and prediction of species–environment relationships is currently mainly based on species distribution models. These purely correlative models neglect spatial population dynamics and assume that species distributions are in equilibrium with their environment. This causes biased estimates of species niches and handicaps forecasts of range dynamics under environmental change. Here we aim to develop an approach that statistically estimates process‐based models of range dynamics from data on species distributions and permits a more comprehensive quantification of forecast uncertainties. Innovation We present an approach for the statistical estimation of process‐based dynamic range models (DRMs) that integrate Hutchinson's niche concept with spatial population dynamics. In a hierarchical Bayesian framework the environmental response of demographic rates, local population dynamics and dispersal are estimated conditional upon each other while accounting for various sources of uncertainty. The method thus: (1) jointly infers species niches and spatiotemporal population dynamics from occurrence and abundance data, and (2) provides fully probabilistic forecasts of future range dynamics under environmental change. In a simulation study, we investigate the performance of DRMs for a variety of scenarios that differ in both ecological dynamics and the data used for model estimation. Main conclusions Our results demonstrate the importance of considering dynamic aspects in the collection and analysis of biodiversity data. In combination with informative data, the presented framework has the potential to markedly improve the quantification of ecological niches, the process‐based understanding of range dynamics and the forecasting of species responses to environmental change. It thereby strengthens links between biogeography, population biology and theoretical and applied ecology.     

Downscaling European species atlas distributions to a finer resolution: implications for conservation planning

Aim One of the limitations to using species’ distribution atlases in conservation planning is their coarse resolution relative to the needs of local planners. In this study, a simple approach to downscale original species atlas distributions to a finer resolution is outlined. If such a procedure yielded accurate downscaled predictions, then it could be an aid to using available distribution atlases in real‐world local conservation decisions. Location Europe. Methods An iterative procedure based on generalized additive modelling is used to downscale original European 50 × 50 km distributions of 2189 plant and terrestrial vertebrate species to c. 10 × 10 km grid resolution. Models are trained on 70% of the original data and evaluated on the remaining 30%, using the receiver operating characteristic (ROC) procedure. Fitted models are then interpolated to a finer resolution. A British dataset comprising distributions of 81 passerine‐bird species in a 10 × 10 km grid is used as a test bed to assess the accuracy of the downscaled predictions. European‐wide, downscaled predictions are further evaluated in terms of their ability to reproduce: (1) spatial patterns of coincidence in species richness scores among different groups; and (2) spatial patterns of coincidence in richness, rarity and complementarity hotspots. Results There was a generally good agreement between downscaled and observed fine‐resolution distributions for passerine species in Britain (median Jaccard similarity = 70%; lower quartile = 36%; upper quartile = 88%). In contrast, the correlation between downscaled and observed passerine species richness was relatively low (rho = 0.31) indicating a pattern of error propagation through the process of overlaying downscaled distributions for many species. It was also found that measures of model accuracy in fitting original data (ROC) were a poor predictor of models’ ability to interpolate distributions at fine resolutions (rho = ?0.10). Although European hotspots were not fully coincident between observed and modelled coarse‐resolution data, or between modelled coarse resolution and modelled downscaled data, there was evidence that downscaled distributions were able to maintain original cross‐taxon coincidence of species‐richness scores, at least for terrestrial vertebrate groups. Downscaled distributions were also able to uncover important environmental gradients otherwise blurred by coarse‐resolution data. Main conclusions Despite uncertainties, downscaling procedures may prove useful to identify reserves that are more meaningfully related to local patterns of environmental variation. Potential errors arising from the presence of false positives may be reduced if downscaled‐distribution records projected to occur outside the range of original coarse‐resolution data are excluded. However, the usefulness of this procedure may be limited to data‐rich regions. If downscaling procedures are applied to data‐poor regions, then there is a need to undertake further research to understand the structure of error in models. In particular, it would be important to investigate which species are poorly modelled, where and why. Without such an assessment it is difficult to support unsupervised use of downscaled data in most real‐world situations.