Species distribution model transferability and model grain size – finer may not always be better

Species distribution models have been used to predict the distribution of invasive species for conservation planning. Understanding spatial transferability of niche predictions is critical to promote species-habitat conservation and forecasting areas vulnerable to invasion. Grain size of predictor variables is an important factor affecting the accuracy and transferability of species distribution models. Choice of grain size is often dependent on the type of predictor variables used and the selection of predictors sometimes rely on data availability. This study employed the MAXENT species distribution model to investigate the effect of the grain size on model transferability for an invasive plant species. We modelled the distribution of Rhododendron ponticum in Wales, U.K. and tested model performance and transferability by varying grain size (50 m, 300 m, and 1 km). MAXENT-based models are sensitive to grain size and selection of variables. We found that over-reliance on the commonly used bioclimatic variables may lead to less accurate models as it often compromises the finer grain size of biophysical variables which may be more important determinants of species distribution at small spatial scales. Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size

Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

The performance of species distribution models (SDMs) is known to be affected by analysis grain and positional error of species occurrences. Coarsening of the analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine-scale environmental data in SDMs, it is important to test this assumption. Models using fine-scale environmental data are more likely to be negatively affected by positional error as the inaccurate occurrences might easier end up in unsuitable environment. This can result in inappropriate conservation actions. Here, we examined the trade-offs between positional error and analysis grain and provide recommendations for best practice. We generated narrow niche virtual species using environmental variables derived from LiDAR point clouds at 5 x 5 m fine-scale. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three modelling techniques (MaxEnt, BRT and GLM) and evaluated their discrimination ability, niche overlap with virtual species and change in realized niche. We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance. We recommend fitting models with the finest possible analysis grain and as close to the response grain as possible even when available species occurrences suffer from positional errors. If there are significant positional errors in species occurrences, users are unlikely to benefit from making additional efforts to obtain higher resolution environmental data unless they also minimize the positional errors of species occurrences. Our findings are also applicable to coarse analysis grain, especially for fragmented habitats, and for species with narrow niche breadth

Understanding the Distributions of Benthic Foraminifera in the Adriatic Sea with Gradient Forest and Structural Equation Models

Abstract: In the last three decades, benthic foraminiferal ecology has been intensively investigated to improve the potential application of these marine organisms as proxies of the effects of climate change and other global change phenomena. It is still challenging to define the most important factors affecting foraminiferal communities and derived faunistic parameters. In this study, we examined the abiotic-biotic relationships of foraminiferal communities in the central-southern area of the Adriatic Sea using modern machine learning techniques. We combined gradient forest (Gf) and structural equation modeling (SEM) to test hypotheses about determinants of benthic foraminiferal assemblages. These approaches helped determine the relative effect of sizes of different environmental variables responsible for shaping living foraminiferal distributions. Four major faunal turnovers (at 13–28 m, 29–58 m, 59–215 m, and >215 m) were identified along a large bathymetric gradient (13–703 m water depth) that reflected the classical bathymetric distribution of benthic communities. Sand and organic matter (OM) contents were identified as the most relevant factors influencing the distribution of foraminifera either along the entire depth gradient or at selected bathymetric ranges. The SEM supported causal hypotheses that focused the factors that shaped assemblages at each bathymetric range, and the most notable causal relationships were direct effects of depth and indirect effects of the Gf-identified environmental parameters (i.e., sand, pollution load Index–PLI, organic matter–OM and total nitrogen–N) on foraminifera infauna and diversity. These results are relevant to understanding the basic ecology and conservation of foraminiferal communitie

Characterizing the Spatial Patterns and Spatially Explicit Probabilities of Post-Fire Vegetation residual patches in Boreal Wildfire Scars

Wildfire is one of the main natural disturbances that consume a substantial amount of forest cover, influencing and reshaping the landscape mosaic of boreal forests. Wildfires do not burn the entire landscape; they rather create a complex mosaic of post-fire landscape structure with different degrees of burn severity. The resulting spatial mosaic includes fully burned, partially burned, and unburned areas. Even though the most visible components of a fire disturbed landscape are the completely burned areas, a considerable number of residual patches of various size, shape, and composition are retained following a fire. The residual patches refer to remnants of the pre-fire forest ecosystem that left completely unaltered within the fire footprint. Improved understanding of the patterns and characteristics of wildfire residuals provides insights for investigating the effects of fire disturbances, emulating forest disturbances in harvesting operations, and improving forest management planning. Knowledge about the post-fire residuals relies on how well we measure the patterns and characteristics of post-fire residuals, determine the factors that explain their occurrence and patterns, and what consistent measurement framework we use to understand the patterns and predict their likely occurrence. In this study, the patterns and characteristics of post-fire residuals was initially examined based on eleven boreal wildfire events within northwestern Ontario; each ignited by lightning and never suppressed. The wildfire events were occurred in ecoregion 2W during the fire seasons of 2002 and 2003. In order to design a consistent and repeatable method for measuring the patterns of residuals, an integrate approach has been designed. This involves assessing the spatial patterns where the composition, configuration, and fragmentation of residual patches were assessed based on selected spatial metrics; examining the importance of predictor variables that explain residuals and their marginal effects on residual patch occurrence using Random Forest (RF) ensemble method; and developing a spatially explicit predictive model using the RF method where the combined effects of the variables were examined. Finally, the three approaches are applied and evaluated using a recent and independent data from the extensive RED084 wildfire event that occurred in 2011 within the adjacent ecoregion (3S). The effects of analytical scale (i.e., spatial resolution) on characterizing the spatial patterns, determining the relative variable importance, and predicted probabilities of residual patches are assessed. The results show that the composition and configuration of wildfire residuals vary as a function of measurement, spatial resolutions, and fire event sizes, suggesting the variation in fire intensity and severity across the fire events. The patterns of wildfire residuals are also sensitive to changing scale, but the responses of the spatial metrics to changing spatial resolutions are grouped into three categories: monotonic change and predictable response in which three shape related metrics (LSI, MSI, and FRAC) show a predictable responsible; monotonic change with no simple scaling rule; and non-monotonic change with erratic response. The results also reveal that the factors that are incorporated in this study interactively affect the occurrence and distribution of residual patches, but natural firebreak features (e.g., wetlands and surface water) were among the most important predictors to explain wildfire residuals. Furthermore, the model implemented to predict residual patches has a reasonable or high predictive performance (‘marginal’ to ‘strong’ model performance) when it was applied in wildfire events that occurred in the same ecoregion. However, the predictive power of the model is low for the independent fire event (RED084). The overall findings of this dissertation reveal that the 1) predictive model based on RF is robust enough to determine the relative importance of the predictors and their marginal effect; 2) the model was flexible enough to identify areas where wildfire residuals are likely to occur; and 3) there is a repeatable, robust measurement framework for characterizing residual patches and understanding their variability across different wildfire events

Abundance of small individuals influences the effectiveness of processing techniques for deep-sea nematodes

Nematodes are the most abundant metazoans of deep-sea benthic communities, but knowledge of their distribution is limited relative to larger organisms. Whilst some aspects of nematode processing techniques, such as extraction, have been extensively studied, other key elements have attracted little attention. We compared the effect of (1) mesh size (63, 45, and 32 μm) on estimates of nematode abundance, biomass, and body size, and (2) microscope magnification (50 and 100×) on estimates of nematode abundance at bathyal sites (250-3100 m water depth) on the Challenger Plateau and Chatham Rise, south-west Pacific Ocean. Variation in the effectiveness of these techniques was assessed in relation to nematode body size and environmental parameters (water depth, sediment organic matter content, %silt/clay, and chloroplastic pigments). The 63-μm mesh retained a relatively low proportion of total nematode abundance (mean ±SD = 55 ±9%), but most of nematode biomass (90 ± 4%). The proportion of nematode abundance retained on the 45-μm mesh in surface (0-1 cm) and subsurface (1-5 cm) sediment was significantly correlated (P < 0.01) with %silt/clay (R² = 0.39) and chloroplastic pigments (R² = 0.29), respectively. Variation in median nematode body weight showed similar trends, but relationships between mean nematode body weight and environmental parameters were either relatively weak (subsurface sediment) or not significant (surface sediment). Using a low magnification led to significantly lower (on average by 43%) nematode abundance estimates relative to high magnification (P < 0.001), and the magnitude of this difference was significantly correlated (P < 0.05) with total nematode abundance (R²p = 0.53) and the number of small (≤ 250 μm length) individuals (R²p = 0.05). Our results suggest that organic matter input and sediment characteristics influence the abundance of small nematodes in bathyal communities. The abundance of small individuals can, in turn, influence abundance estimates obtained using different mesh sizes and microscope magnifications

Habitats as predictors in species distribution models: Shall we use continuous or binary data?

The representation of a land cover type (i.e. habitat) within an area is often used as an explanatory variable in species distribution models. However, it is possible that a simple binary presence/absence of the suitable habitat might be the most important determinant of the presence/absence of some species and, thus, be a better predictor of species occurrence than the continuous parameter (area). We hypothesize that the binary predictor is more suitable for relatively rare habitats (e.g. wetlands) while for common habitats (e.g. forests) the amount of the focal habitat is a better predictor. We used the Third Atlas of Breeding Birds in the Czech Republic as the source of species distribution data and CORINE Land Cover inventory as the source of the landcover information. To test our hypothesis, we fitted generalized linear models of 32 water and 32 forest bird species. Our results show that for water bird species, models using binary predictors (presence/absence of the habitat) performed better than models with continuous predictors (i.e. the amount of the habitat); for forest species, however, we observed the opposite. Thus, future studies using habitats as predictors of species occurrences should consider the prevalence of the habitat in the landscape, and the biological role of the habitat type in the particular species' life history. In addition, performing a preliminary comparison of the performance of the binary and continuous versions of habitat predictors (e.g. using information criteria) prior to modelling, during variable selection, can be beneficial. These are simple steps that will improve explanatory and predictive performance of models of species distributions in biogeography, community ecology, macroecology and ecological conservation

Extrapolating insect biodiversity across spatial scales

Extrapolating biodiversity patterns across spatial scales can address shortfalls in our knowledge of species distributions, inform conservation and further our understanding of spatial patterns in biodiversity. I compared fine grain predictions of occupancy for British Odonata species among ten downscaling models. I observed a sigmoidal occupancy-area relationship for the best performing model and found that predictive success for Odonata species varied systematically with species traits. Species with high dispersal abilities had greater predictive error. Poorer predictions for species with a climatic range limit in Britain suggest that environmental information is required to fully capture spatial patterns in biodiversity. I modelled the distribution of the Brindled Green moth at two spatial grains using a hierarchical Bayesian model to quantify associations with climate, landcover and elevation, whilst accounting for residual spatial autocorrelation and spatial patterns in recording effort. Model predictions improved at the finer spatial grain and identified unsurveyed grid cells with high suitability for future recording. The overlap between individual species distributions underpins spatial patterns in multi-species assemblages. I used simulated species assemblages to evaluate 29 abundance-based metrics of β-diversity against a set of desirable and ‘personality’ properties. Metrics accounting for unseen shared and unshared species were lacking. I identified a trade-off between robustness in the face of undersampling and sensitivity to turnover in rare species. The findings were borne out when a selection of metrics were applied to assemblages of British macro-moths: variation in β-diversity was best explained by climate, landcover and distance when using standardised data and abundance-based metrics, as opposed to opportunistic data and presence-absence metrics. This thesis has demonstrated the value of using biological records to explore biodiversity patterns at multiple spatial scales and has highlighted some of the methodological challenges that remain

Options and limitations of statistical modelling as a tool for understanding and predicting benthic functions in an area with high environmental variability

The objective was to investigate functional changes of macrobenthic communities along the salinity gradient in the south-western Baltic Sea. Two different approaches were chosen: (1) the appraisal of functional diversity and composition and (2) mapping species biomass as a basic tool for quantification of single key functions. A distinct shift of the functional composition was detected. In contrast, a potential shift in functional diversity was buffered by the dominance of ubiquitous species. Quantitative distribution maps were drawn for important bivalve species