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Understanding the dynamics of socio‐ecological systems is crucial to the development of environmentally sustainable practices. Models of social or ecological sub‐systems have greatly enhanced such understanding, but at the risk of obscuring important feedbacks and emergent effects. Integrated modelling approaches have the potential to address this shortcoming by explicitly representing linked socio‐ecological dynamics. We developed a socio‐ecological system model by coupling an existing agent‐based model of land‐use dynamics and an individual‐based model of demography and dispersal. A hypothetical case‐study was established to simulate the interaction of crops and their pollinators in a changing agricultural landscape, initialised from a spatially random distribution of natural assets. The bi‐directional coupled model predicted larger changes in crop yield and pollinator populations than a unidirectional uncoupled version. The spatial properties of the system also differed, the coupled version revealing the emergence of spatial land‐use clusters that neither supported nor required pollinators. These findings suggest that important dynamics may be missed by uncoupled modelling approaches, but that these can be captured through the combination of currently‐available, compatible model frameworks. Such model integrations are required to further fundamental understanding of socio‐ecological dynamics and thus improve management of socio‐ecological systems. [ABSTRACT FROM AUTHOR]

Globally, coral reef monitoring programmes conducted by volunteer-based organizations or local communities have the potential to collect large quantities of marine data at low cost. However, many scientists remain sceptical about the ability of these programmes to detect changes in marine systems when compared with professional techniques., A limited number of studies have assessed the efficacy and validity of volunteer-based monitoring, and even fewer have assessed community-based methods., This study in Cambodia investigated the ability of surveyors of different levels of experience to conduct underwater surveys using a simple coral reef methodology. Surveyors were assigned to four experience categories and conducted a series of six 20 × 5 m belt transects using five benthic indicator species., Results show decreased variation in marine community assessments with increasing experience, indicating that experience, rather than cultural background, influences survey ability. This suggests that locally based programmes can fill gaps in knowledge with suitable ongoing training and assessment., Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Spatial patterns of community composition turnover (beta diversity) may be mapped through generalised dissimilarity modelling ( GDM). While remote sensing data are adequate to describe these patterns, the often high-dimensional nature of these data poses some analytical challenges, potentially resulting in loss of generality. This may hinder the use of such data for mapping and monitoring beta-diversity patterns., This study presents Sparse Generalised Dissimilarity Modelling ( SGDM), a methodological framework designed to improve the use of high-dimensional data to predict community turnover with GDM. SGDM consists of a two-stage approach, by first transforming the environmental data with a sparse canonical correlation analysis ( SCCA), aimed at dealing with high-dimensional data sets, and secondly fitting the transformed data with GDM. The SCCA penalisation parameters are chosen according to a grid search procedure in order to optimise the predictive performance of a GDM fit on the resulting components. The proposed method was illustrated on a case study with a clear environmental gradient of shrub encroachment following cropland abandonment, and subsequent turnover in the bird communities. Bird community data, collected on 115 plots located along the described gradient, were used to fit composition dissimilarity as a function of several remote sensing data sets, including a time series of Landsat data as well as simulated En MAP hyperspectral data., The proposed approach always outperformed GDM models when fit on high-dimensional data sets. Its usage on low-dimensional data was not consistently advantageous. Models using high-dimensional data, on the other hand, always outperformed those using low-dimensional data, such as single-date multispectral imagery., This approach improved the direct use of high-dimensional remote sensing data, such as time-series or hyperspectral imagery, for community dissimilarity modelling, resulting in better performing models. The good performance of models using high-dimensional data sets further highlights the relevance of dense time series and data coming from new and forthcoming satellite sensors for ecological applications such as mapping species beta diversity. [ABSTRACT FROM AUTHOR]

Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold-based pre-selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor-response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold-based pre-selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the 'folk lore'-thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre-analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Species distribution modelling is commonly used to guide future conservation policies in the light of potential climate change. However, arbitrary decisions during the model-building process can affect predictions and contribute to uncertainty about where suitable climate space will exist. For many species, the key climatic factors limiting distributions are unknown. This paper assesses the uncertainty generated by using different climate predictor variable sets for modelling the impacts of climate change. Location Europe, 10° W to 50° E and 30° N to 60° N. Methods Using 1453 presence pixels at 30 arcsec resolution for the great bustard ( Otis tarda), predictions of future distribution were made based on two emissions scenarios, three general climate models and 26 sets of predictor variables. Twenty-six current models were created, and 156 for both 2050 and 2080. Map comparison techniques were used to compare predictions in terms of the quantity and the location of presences (map comparison kappa, MCK) and using a range change index (RCI). Generalized linear models (GLMs) were used to partition explained deviance in MCK and RCI among sources of uncertainty. Results The 26 different variable sets achieved high values of AUC (area under the receiver operating characteristic curve) and yet introduced substantial variation into maps of current distribution. Differences between maps were even greater when distributions were projected into the future. Some 64-78% of the variation between future maps was attributable to choice of predictor variable set alone. Choice of general climate model and emissions scenario contributed a maximum of 15% variation and their order of importance differed for MCK and RCI. Main conclusions Generalized variable sets produce an unmanageable level of uncertainty in species distribution models which cannot be ignored. The use of sound ecological theory and statistical methods to check predictor variables can reduce this uncertainty, but our knowledge of species may be too limited to make more than arbitrary choices. When all sources of modelling uncertainty are considered together, it is doubtful whether ensemble methods offer an adequate solution. Future studies should explicitly acknowledge uncertainty due to arbitrary choices in the model-building process and develop ways to convey the results to decision-makers. [ABSTRACT FROM AUTHOR]

The baobab tree, with more than 300 uses and commercial value in EU and United States, has been identified as one of the most important trees to be conserved and domesticated in Africa. A decline in baobab populations because of changes in climate could have a negative effect on African livelihoods. Therefore, it is important to study the potential future distribution of this species and determine strategies for conservation. We used Maxent, 480 geo-referenced records, present and future climatic and soil layers. Different general circulation models and scenarios were selected. Models were simulated for (i) All records, (ii) East Africa and (iii) West Africa species records. For each combination, the proportion of the present habitat that might remain suitable in the future was determined. These habitat proportions were compared with the Protected Areas in Africa. Although potential future distributions were different depending on model, scenario and records used, in all cases only a percentage of the present distribution was predicted to remain suitable in the future. Some countries were found to have no suitable habitat in the future. Recommendations for different conservation strategies include in situ conservation in Protected Areas; ex situ conservation in seed banks; and conservation through 'sustainable utilization'. [ABSTRACT FROM AUTHOR]

Aim A key assumption in species distribution modelling is that both species and environmental data layers contain no positional errors, yet this will rarely be true. This study assesses the effect of introduced positional errors on the performance and interpretation of species distribution models. Location Baixo Alentejo region of Portugal. Methods Data on steppe bird occurrence were collected using a random stratified sampling design on a 1-km2 pixel grid. Environmental data were sourced from satellite imagery and digital maps. Error was deliberately introduced into the species data as shifts in a random direction of 0–1, 2–3, 4–5 and 0–5 pixels. Whole habitat layers were shifted by 1 pixel to cause mis-registration, and the cumulative effect of one to three shifted layers investigated. Distribution models were built for three species using three algorithms with three replicates. Test models were compared with controls without errors. Results Positional errors in the species data led to a drop in model performance (larger errors having larger effects – typically up to 10% drop in area under the curve on average), although not enough for models to be rejected. Model interpretation was more severely affected with inconsistencies in the contributing variables. Errors in the habitat layers had similar although lesser effects. Main conclusions Models with species positional errors are hard to detect, often statistically good, ecologically plausible and useful for prediction, but interpreting them is dangerous. Mis-registered habitat layers produce smaller effects probably because shifting entire layers does not break down the correlation structure to the same extent as random shifts in individual species observations. Spatial autocorrelation in the habitat layers may protect against species positional errors to some extent but the relationship is complex and requires further work. The key recommendation must be that positional errors should be minimised through careful field design and data processing. [ABSTRACT FROM AUTHOR]

Despite a growing interest in species distribution modelling, relatively little attention has been paid to spatial autocorrelation and non-stationarity. Both spatial autocorrelation (the tendency for adjacent locations to be more similar than distant ones) and non-stationarity (the variation in modelled relationships over space) are likely to be common properties of ecological systems. This paper focuses on non-stationarity and uses two local techniques, geographically weighted regression (GWR) and varying coefficient modelling (VCM), to assess its impact on model predictions. We extend two published studies, one on the presence–absence of calandra larks in Spain and the other on bird species richness in Britain, to compare GWR and VCM with the more usual global generalized linear modelling (GLM) and generalized additive modelling (GAM). For the calandra lark data, GWR and VCM produced better-fitting models than GLM or GAM. VCM in particular gave significantly reduced spatial autocorrelation in the model residuals. GWR showed that individual predictors became stationary at different spatial scales, indicating that distributions are influenced by ecological processes operating over multiple scales. VCM was able to predict occurrence accurately on independent data from the same geographical area as the training data but not beyond, whereas the GAM produced good results on all areas. Individual predictions from the local methods often differed substantially from the global models. For the species richness data, VCM and GWR produced far better predictions than ordinary regression. Our analyses suggest that modellers interpolating data to produce maps for practical actions (e.g. conservation) should consider local methods, whereas they should not be used for extrapolation to new areas. We argue that local methods are complementary to global methods, revealing details of habitat associations and data properties which global methods average out and miss. [ABSTRACT FROM AUTHOR]

1. Recent declines in woodland birds in Britain have been linked to increasing habitat fragmentation. To understand the effects of fragmentation, data on avian dispersal across woodland gaps are essential but often lacking. 2. We used song thrush Turdus philomelos mobbing calls to attract songbirds across gaps ranging from 5 to 120 m in width and along comparable woodland edges. Responses were modelled against distance using generalized linear models. Such models have clear applied value for connecting fragmented landscapes. 3. We also calculated response indices and compared these with bird morphology. The gap-crossing results were applied to a real landscape in central Scotland and landscape metrics were calculated to judge how perception of habitat connectivity varies interspecifically. 4. The chaffinch Fringilla coelebs and the robin Erithacus rubecula both responded more readily across gaps than through woodland. There was no difference between gap and edge response for the coal tit Parus ater, while the goldcrest Regulus regulus responded more readily along edges than across gaps. Maximum gap-crossing distances ranged from 46 m (goldcrest) to 150 m (chaffinch). 5. There was a positive linear trend between mass of bird and the difference in the maximum response for gap and control experiments. Likewise there was a positive curvilinear relationship between wing area and the difference in probability of response between gap and control experiments at 50 m. These results may be interpreted in terms of manoeuvrability and ability to escape avian predation. 6. For the central Scotland landscape, the perceived number of patches in the landscape decreased exponentially with increasing gap-crossing distance, while the median patch size and mean patch fractal dimension increased linearly with gap-crossing distance. 7. Synthesis and applications. Our results show that an experimental approach using playback can be used to obtain data on avian gap crossing and the results applied to real landscapes to visualize interspecific differences in habitat perception. This has practical management applications, especially for designing forest habitat networks to maximize avian biodiversity, and potentially could help reverse the recent declines in woodland birds. Journal of Applied Ecology (2005) 42, 678–687 doi: 10.1111/j.1365-2664.2005.01057.x [ABSTRACT FROM AUTHOR]

Global climate change generated by human activities is likely to affect agroecosystems in several ways: reinforcing intensification in northern and western Europe, and extensification in the Mediterranean countries. If we are to predict the consequences of global warming for wildlife, distribution models have to include climate data. The METEOSAT temporal series from EWBMS offers an attractive alternative to using climatic surfaces derived from ground stations. The aim of this paper is to test whether this climatic satellite data can improve the distribution models obtained previously by Suárez-Seoane et al. using habitat variables for three agro-steppe bird species: great bustard, little bustard and calandra lark in Spain. Rainfall, radiation balance, evapotranspiration and soil moisture images were incorporated together with the other variables used as predictors in the published stepwise GAM models. Changes in the predicted distributions from the habitat only and climate-habitats models were assessed by reference to the CORINE land cover categories. Inclusion of climatic variables from METEOSAT led to statistically superior models for all three species. There were large differences in the climatic variables selected and the original variables dropped among the species. Evapotranspiration variables were the most frequently selected. Maps of the differences between the habitat and climate-habitat models showed very different patterns for the three species. Inclusion of climate variables led to a wider range of land cover types being deemed suitable. Despite the statistical superiority of models, care is needed in deciding whether to use climatic variables because they may emphasize the fundamental rather than the realized niche. Used together, however, habitat and climate models can provide new insights into factors limiting species distributions and how they may respond to climate change. [ABSTRACT FROM AUTHOR]

1. Predictive models of species' distributions are used increasingly in ecological studies investigating features as varied as biodiversity, habitat selection and interspecific competition. In a pilot study, we based a successful model for the great bustard Otis tarda on advanced very high resolution radiometer (AVHRR) satellite data, which offer attractive predictor variables because of the global coverage, high temporal frequency of overpasses and low cost. We wished to assess whether the approach could be applied at very large spatial scales, and whether the coarse resolution of the imagery (1 km²) would limit application to those bird species with large home ranges or to simple recognition of broad habitat types. 2. We modelled the distributions of three agricultural steppe birds over the whole of Spain using a common set of predictor variables, including AVHRR imagery. The species, great bustard, little bustard Tetrax tetrax and calandra lark Melanocorhypha calandra, have similar habitat requirements but differently sized home ranges, and are all species of conservation concern. Good models would reveal differences in distribution between the species and have high predictive power despite the large geographical extent covered. 3. Generalized additive models (GAMs) were built with the presence-absence of the species as the response variable. Individual species' responses to the habitat variables were identified using partial fits and compared with each other. We found that this modelling framework could successfully distinguish the habitats selected by the three species, while the response curves indicated how the habitats differed. Model fits and cross-validations assessed using receiver operating characteristic (ROC) plots showed the models to be successful and robust. 4. We overlaid the predictive maps to identify key areas for agricultural steppe birds in Spain and compared these with the present network of protected sites in two sample regions. In... [ABSTRACT FROM AUTHOR]

The ecology of the Houbara Bustard Chlamydotis [undulata] macqueenii is poorly known and populations are declining due to hunting and habitat loss. As wintering populations in Abu Dhabi may be limited by habitat and food, we studied the diet using calibrated faecal analysis. Prey were categorized into 16 groups and fed to captive birds under controlled conditions. We calculated the recovery rates of prey following digestion and identified consistent fragments for each group. Wild Houbara Bustard faeces were collected and examined for key fragments, and initial prey intake was calibrated. Plant remains were identified and their contribution was estimated. Fewer than 28% faeces contained >50% plant material by volume and only 12% contained 95% or more. Numerically, the most important prey were: ants (64%), large nocturnal tenebrionids (14.5%), small climbing tenebrionids (12%) and diurnal tenebrionids. However, the Tenebrionidae contributed 97% of the animal biomass. The relative proportions of prey in the diet were similar to relative abundance as assessed by pitfall trapping. Estimates of the energetic value of the prey suggested that on average Houbara Bustards must consume around 670 desert invertebrates/day to meet energy needs. The effort required to catch these prey may vary at least ten-fold seasonally. On average plants could provide a further 6.4-14% energy but more work is needed on this. Whether Houbara Bustards wintering in Abu Dhabi are prey-limited depends on prey densities and renewal rates which remain unknown. [ABSTRACT FROM AUTHOR]

We captured desert arthropods from sand and gravel substrates at five coastal or inland locations in Abu Dhabi around the new moon once every 28 days for two years. Three replicated lines of 20 unbaited pitfall traps were used from two hours before dusk until two hours after dawn. We examined the data for spatial and temporal variation, especially in relation to climate. Around 53,400 arthropods were caught, 97 3% being insects and the rest arachnids. Ants were most common (75.4%), followed by the Thysanura (12%), Coleoptera (8.4%), Araneae (1.5%) and Scorpiones (1.1%). Catches differed significantly among sites, substrates and time of year. In general, the number of arthropods caught matched the annual temperature cycle. Peak catches also followed the annual rains. Summer catches were higher at coastal than inland sites. Fewer arthropods were caught at gravel sites than on sand, but the number of taxa did not differ consistently between substrates. Community composition was generally less similar among sites than between substrates although this was not always the case. Inland communities had fewer taxa and generally fewer individuals than those near the coast. Isoptera, Tettigonndae and Carabidae occurred less frequently (if at all) inland. We suggest that the higher humidity at sites near the coast allows more taxa to occur and to remain active during the extreme heat of summer. Further into the Empty Quarter we would expect a decline in the number of taxa and in the total catch. [ABSTRACT FROM AUTHOR]