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Potential pitfalls in rescaling digital terrain model-derived attributes for ecological studies
Abstract
Terrain attributes (e.g., slope, rugosity) derived in Geographic Information Systems (GIS) from digital terrain
models (DTMs) are widely used in both terrestrial and marine ecological studies due to their potential to act as
surrogates of species distribution. However, the spatial resolution of DTMs is often altered to match the scale at
which species observations were collected. Here, we highlight the significance of adequately reporting the
methods used to derive terrain attributes from DTMs and the consequences of their incorrect reporting in
ecological studies. To ensure full repeatability of studies, they should report (i) the source and the resolution of
the original DTM; (ii) the algorithm used to calculate terrain attributes; (iii) the method used for rescaling (e.g.,
aggregating or resampling, using the mean or maximum values); and (iv) the order in which these operations
were performed. We contrast the effects of two common scale alteration approaches for the derivation of terrain
attributes from DTMs. These two scale alteration methods differ in the step at which the change is performed: (i)
the resolution alteration is performed after computing terrain attributes from the original DTM at the native
resolution, or (ii) the resolution alteration is performed on the native DTM before computing terrain attributes.
While these approaches conceptually do the same thing (i.e., change the resolution of the terrain attributes), we
demonstrate that they produce two distinct sets of variables that are not interchangeable and describe different
properties of the terrain. In a species distribution modelling (SDM) context, the first approach calculates terrain
attribute values within the cell where a species is found, while the second approach calculates terrain attribute
values with respect to neighbouring cells. A mutual substitution of the two approaches results in a decrease of
models' discrimination ability and in misleading spatial predictions of species probability of occurrence.
Regardless of the DTM-derived attribute, we argue that the choice of the approach should be carefully guided by
both the ecological scale relevant to the question being asked and the performance of pre-analyses. We emphasize
that selected methods be clearly described to encourage reproducibility and proper interpretation of
results, thus enabling a better understanding of the role of scale in ecology.
... Additional complexity arises from scale dependence, wherein terrain attributes vary based on the scale at which they are calculated (Dolan, 2012;Misiuk et al., 2021;Moudrý et al., 2019;Wilson et al., 2007;Wood, 1996), and many software packages allow for calculation only at a fixed scale (Lecours et al., 2017;Misiuk et al., 2021). ...
... Smoothing filters (e.g., focal mean) are commonly applied to either the DTM or terrain attributes to achieve a broader analysis scale, which is possible with almost any spatial data software (Dolan, 2012;Misiuk et al., 2021;Moudrý et al., 2019). However, many terrain attributes can also be directly calculated by varying the size of the focal window (Figure 2), which may be preferable in many cases . ...
... These classes are planar flat, planar slope, pit, channel, pass, ridge, and peak. The morphometric features algorithm has been modified from Wood (1996) to use the curvature measures defined previously, and the "planar" class is additionally split into "planar flat" and "planar slope" as slope is considered its own class in several similar landform and habitat mapping schemes (Dove et al., 2020;Federal Geographic Data Committee, 2012;Jasiewicz & Stepinski, 2013;Masetti et al., 2018) and is an important factor shaping biotic communities (Moudrý et al., 2019;Sappington et al., 2007;Wilson et al., 2007). ...
Digital terrain models (DTMs) are datasets containing altitude values above or below a reference level, such as a reference ellipsoid or a tidal datum over geographic space, often in the form of a regularly gridded raster. They can be used to calculate terrain attributes that describe the shape and characteristics of topographic surfaces. Calculating these terrain attributes often requires multiple software packages that can be expensive and specialized. We have created a free, open‐source R package, MultiscaleDTM, that allows for the calculation of members from each of the five major thematic groups of terrain attributes: slope, aspect, curvature, relative position, and roughness, from a regularly gridded DTM. Furthermore, these attributes can be calculated at multiple spatial scales of analysis, a key feature that is missing from many other packages. Here, we demonstrate the functionality of the package and provide a simulation exploring the relationship between slope and roughness. When roughness measures do not account for slope, these attributes exhibit a strong positive correlation. To minimize this correlation, we propose a new roughness measure called adjusted standard deviation. In most scenarios tested, this measure produced the lowest rank correlation with slope out of all the roughness measures tested. Lastly, the simulation shows that some existing roughness measures from the literature that are supposed to be independent of slope can actually exhibit a strong inverse relationship with the slope in some cases.
... However, their use remains complex, which implies the need to follow good configuration practices [7] and to interpret the results by combining statistical, spatial, and expert-based indices [8]. Notably, the performance of OCCs is highly sensitive to classifier parametrization (e.g., fitting, thresholding, variable selection) [9][10][11], the quality of the predictive variables used [12], and the reference data [13]. Moreover, assessing the accuracy of OCCs remains challenging without absence data [14]. ...
... The quality of predictive variables and reference data influences OCC performance [5,12,24]. The literature review indicates that variables derived from RS data can be influenced by the specific characteristics of the sensor, environmental conditions, or data processing. ...
... The literature review indicates that variables derived from RS data can be influenced by the specific characteristics of the sensor, environmental conditions, or data processing. For examples, (i) Lopatin et al. [78] showed that shadows in very high spatial resolution images decrease classification accuracy; (ii) Moudrý et al. [12,113] found that variable topographic quality (e.g., spatial resolution, ability to penetrate vegetation cover, parameters for calculating topographic indices) influenced OCC accuracy greatly; (iii) Randin et al. [26] indicated that spectral values of the thermal bands used to generate bioclimatic variables are also influenced by the land-cover; (iv) Cord et al. [101] and Truong et al. [106] stated that the quality of the LULC variable (e.g., spatial resolution, thematic resolution, map reliability) often influences OCC accuracy and suggested replacing LULC categorical variable with continuous spectral variables. ...
Advances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in integrating RS data into OCCs to map vegetation classes. A systematic review was performed for the period 2013–2020. A total of 136 articles were analyzed based on 11 topics and 30 attributes that address the ecological issues, properties of RS data, and the tools and parameters used to classify natural vegetation. The results highlight several advances in the use of RS data in OCCs: (i) mapping of potential and actual vegetation areas, (ii) long-term monitoring of vegetation classes, (iii) generation of multiple ecological variables, (iv) availability of open-source data, (v) reduction in plotting effort, and (vi) quantification of over-detection. Recommendations related to interdisciplinary issues were also suggested: (i) increasing the visibility and use of available RS variables, (ii) following good classification practices, (iii) bridging the gap between spatial resolution and site extent, and (iv) classifying plant communities.
... The 2-m slope raster was derived from the 2-m DEM, and cell slopes calculated over a 3 by 3 moving window using Horn's algorithm (1981). The 10-m and 20-m slope rasters were produced by downsampling the 2-m slope rastercoarsening of slopes is known to result in maximum slope values that are closer to original slope calculations than slope values derived from coarsened DEMS are (Grohmann, 2015;Moudrý et al., 2019). Bilinear interpolation was used for the resampling, which resulted in an attenuation of the 2-m maximum slopes of 1.9 and 7.3 • for the 10-m and 20-m resolutions, respectively. ...
... In our study, similarity between routes modelled at a same spatial resolution exceeded that of routes modelled with a same cost function (+37 percent points); use of the finest resolution was also found to result in greater similarity between the modelled routes (+15 percent points). As the 10-m and 20-m slope rasters were obtained by downsampling the 2-m slope raster, one might wonder about what the alternative approach that consists of deriving cell slopes from downsampled DEMSknown to further attenuate the value of maximum slopes (Grohmann, 2015;Moudrý et al., 2019), would result in. Albeit changes in proportions, recalculations would indicate comparable sensitivity patterns: a greater similarity between mapped routes modelled at the 2-m resolution (+22 percent points), a similarity between routes modelled at a same spatial resolution higher than that of routes modelled with a same cost function (+19 percent points), and route footprints nearly identical (similarity >90%) to those obtained with the cost accumulation rasters based on slope downsampling. ...
This paper investigates how the spatial resolution of slope data affects the modelling of recreational trails in mountainous areas. It measures the impact of spatial resolution on a) the estimation of cost functions that link walking speeds and slope, and b) the modelling of moving times and mapping of routes using these functions. Cost functions that build on a mountain hike GPS record and slope data derived from a Digital Elevation Model (DEM) at varying resolutions are estimated and their ability to predict accurate moving times is evaluated. The cost functions are supplemented with recently published cost functions to map the quickest routes between the hike start and end points with least-cost-path analyses. The results indicate that spatial resolution has a critical influence in the modelling: the similarity between routes modelled at a same spatial resolution with distinct cost functions considerably surpasses that of routes simulated at varying resolutions with a same cost function. Furthermore, employing finer resolutions enhances the similarity between routes mapped with different cost functions, while also improving the accuracy of predicted moving times. These findings provide evidence that DEM resolution should receive prime attention in the modelling of trails with slope-dependent cost functions.
... At the continental level, the resolution of the topographic information had to be adjusted to fit with the other predictor variables and the species data. Aspect and slope were aggregated to 500×500 m after calculation with the original resolution (Moudrý et al., 2019). Only for the wetness index, the DEM was previously aggregated to 100×100 m to avoid computational issues with the calculation and values were aggregated to 500×500 m afterwards. ...
... In the case of the continental model, the spatial resolution of the topographic information slope and aspect was significantly higher than the one of the species data. This could lead to bias of the model for that variables as shown by Lechner et al. (2012) and Moudrý et al. (2019). Since the overall importance of this predictor group is rather small, we did not assume any significant influence on the model results. ...
Taking into account and combining different levels of scale is essential in species distribution models when it comes to the investigation of the environmental niche of a species. The study focuses on two levels of spatial extent, on the one hand the continental level of Europe, and on the other hand the regional level of the federal state of Tyrol in Austria. Mountain forests in the European Alps cover the inner Alpine distribution margins of several tree species and therefore are particularly well-suited to reveal the predictive power of climate, soil, and topographic variables on the shaping of these margins. The potential occurrence of the two investigated tree species, Abies alba and Fagus sylvatica, is an important criterion for the delineation of elevational vegetation zones in forest site classification systems and was modelled with Deep Neural Networks. In the process, we observed a strong imbalance of absence and presence records at the continental level and evaluated different methods to address this issue. The potential predictor variables for species distribution modelling at the different spatial extents were grouped into climate, soil, and topographic information. The combination of the different hierarchical levels of extent and associated spatial resolution was implemented by using the outputs of the continental model as a predictor variable in the regional model. The binary classification of the 30% test dataset showed a True Skill Statistic of 0.73 to 0.76 for the regional level and 0.5 to 0.74 for the continental level, with slightly higher values for F. sylvatica than for A. alba. For both species and extent levels, the climate predictor group showed the greatest contribution (81 to 96%) to the models' predictive power. At the regional level, climate was followed by soil and then topographic predictor groups. At the continental level however, topography showed stronger effects than soil information. In most cases, the consideration of soil information along climatic gradients led to an increase in the occurrence probability at the climatic distribution margins. There is evidence that soil conditions are more important in determining the inner Alpine distribution margins for F. sylvatica than for A. alba. To improve species distribution models at the regional level, e.g. in the Alpine area, a focus on soil information is proposed. In general, models which combine continental and regional data are preferable.
... Interpreting ecological characteristics within a lattice may bias results by failing to sufficiently represent the species-environment response (Moudrý et al., 2019;Cheng et al., 2021). A species' natural dispersal range provides an approximate scale of the species-environment responses (Jackson and Fahrig, 2015). ...
... However, SDMs must be calibrated with predictors that reflect the size of the lattice. Otherwise, the interpreted values misrepresent the environmental conditions (Moudrý et al., 2019). Lattices with equal-sized grids are more reliable for interpretation than unequal sized grids (i.e. ...
Mosquito borne diseases (MBD) are a major global health concern. To aid MBD management efforts, the distribution of mosquito species is frequently investigated through species distribution models (SDMs). However, the quality these SDMs for management use has not been examined. We evaluated 127 publications of mosquito SDMs published between 1998 and 2020 and assessed each against a set of recently-developed, best-practice standards pertaining to quality of the response variable, predictor variables, model building, and model evaluation aspects. Mosquito SDMs were predominantly trained with presence-background response variables (77% of studies), bioclimatic predictor variables (39-63%), maximum entropy algorithm (54%), and evaluated by area under the receiver operating curve (36%) or confusion matrix metrics (34%). Aedes were the best-studied genus (70 studies). Pan-African (20%) and global (16%) distribution studies dominated. All published studies had one or more unacceptable standards within considered aspects, but no aspect observed unacceptable standards in all publications. The highest proportion of unacceptable standards were observed within predictor variables (60%), followed by model building (53%), model evaluation (34%), and response variable (17%). Response variable and model building demonstrated 8% and 0.2% increases in quality over time, but predictor variables and model evaluation exhibited 6% and 2% decreases in quality, respectively. Quality of mosquito SDMs has not changed since introduction of best practice standards. Quality of mosquito SDMs can be improved by ensuring known species temperature and precipitation thresholds are represented within the response variable. Resolution of predictor variables must be justified from ecological knowledge or statistically approximated. SDMs of mosquitoes require improved evaluation against independent data or creation of geographically-structured data. We encourage future mosquito SDM applications to utilize the most recent SDM standards and recommendations to improve applicability.
... Please also note that we calculated the aspect descriptor from the 10 m aggregate of the DTM/Terrain data set rather than deriving it from the 0.4 m original-resolution rasters and then aggregating it. The latter approach could represent the aspect at the original resolution better (Grohmann, 2015;Moudrý et al., 2019), but would create inconsistencies within how the remaining DTM/Terrain descriptors are calculated in this data set. ...
... Please also note that we calculated the slope descriptor from the 10 m aggregate of the DTM/Terrain data set rather than deriving it from the 0.4 m original-resolution rasters and then aggregating it. The latter approach could represent the slope at the original resolution better (Grohmann, 2015;Moudrý et al., 2019), but would create inconsistencies within how the re-maining DTM/Terrain descriptors are calculated in this data set. ...
Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as light detection and ranging (lidar). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote sensing knowledge. We processed Denmark's publicly available national airborne laser scanning (ALS) data set acquired in 2014/15, together with the accompanying elevation model, to compute 70 rasterised descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than 40 000 km2 covering almost all of Denmark's terrestrial surface. The resulting data set is comparatively small (∼94 GB, compressed 16.8 GB), and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark's national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterised data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond. The full data set is available on Zenodo: 10.5281/zenodo.4756556 (Assmann et al., 2021); a 5 MB teaser subset is also available: 10.5281/zenodo.6035188 (Assmann et al., 2022a).
... The terrain characteristics were aggregated using the mean value within a 100 m buffer (Moudrý, Lecours, et al., 2019). We used LAStools (version 200112), ENVI (version 5.5), and ArcGIS (version 10.3) to calculate vegetation structure, senescence and terrain characteristics, respectively. ...
A global transition of energy production is underway. Coal‐based power production is gradually being replaced by renewable energies, leading to the decommissioning of coal‐mining sites. In Europe, this presents an opportunity for restoring degraded habitats, in alignment with the goals of the Nature Restoration Law. However, systematic approaches to monitoring restoration of mining sites are lacking as current practices are often labour‐intensive and (both spatially and temporally) constrained.
In this study, we evaluated the suitability of habitat heterogeneity metrics derived through airborne remote sensing for monitoring the restoration of coal‐mining sites. Specifically, we tested the response of guild‐specific bird species richness to various metrics of habitat heterogeneity, both at and around a restored coal‐mining site. We (i) examined differences in habitat heterogeneity, including terrain characteristics, vegetation structure and senescent vegetation, between restored and surrounding areas; (ii) documented key aspects of habitat heterogeneity that influence bird richness and different bird guilds; and (iii) tested for significant differences among bird responses between restored and surrounding areas.
Generalised additive models explained between 19% and 78% of the variability in guild richness. Canopy cover, understory cover, the standard deviation of vegetation height, and senescent vegetation positively affected overall bird richness, while terrain characteristics significantly influenced some guilds (e.g. ground‐nesting birds). The effects of evaluated variables on guild diversity were generally similar in both restored and surrounding areas, with the standard deviation of vegetation height being the only exception. Increasing standard deviation of vegetation height positively affected the richness of understory nesters and foragers in the restored area but had no (or slightly negative) effect in the surrounding area.
Synthesis and applications. This study underscores the need to design a mosaic of habitats with complex vertical structures, emphasising the critical role of senescent vegetation and unaltered terrain features in supporting biodiversity. Finally, it provides evidence that integrating habitat heterogeneity metrics derived from airborne remote sensing data into restoration success assessment can support biodiversity‐promoting management measures.
... Slope, aspect, the Terrain Ruggedness Index (TRI), and Roughness were calculated from the high-resolution Shuttle Radar Topography Mission DEM (SRTM, Farr et al., 2007) that was interpolated to a resolution of 10 m x 10 m and void filled with CGIAR-CSI SRTM ver.4.1 (Riley et al., 1999;Fuchs et al., 2017;Macek et al., 2019;Moudrý et al., 2019). We converted aspect values to "eastness" and "southness" to avoid having circular values and calculated folded aspect (A f = 180 -| aspect − 225 |) to estimate heat load per sample according to McCune and Keon, (2002), (Feilhauer and Schmidtlein, 2009). ...
Organizing species assemblages based on compositional characteristics enables the identification of ecologically meaningful patterns in biodiversity and supports forest diversity monitoring, conservation, and management. In this context, ecophylogenetics offers powerful opportunities by exploring how evolutionary relationships between species reflect community distributions within ecological space. Using national forest inventory data of Georgia (Sakartvelo), we classify woody species assemblages based on interspecies phylogenetic dissimilarity and evaluated whether cluster membership could be predicted from multivariate Earth observation data describing site-specific environmental conditions. Principal components of 30 explanatory variables were used to model class membership across three sample groups with increasing disturbance levels. Prediction accuracy reached 53.6 % (OOB error 46.4 %) for undisturbed samples, 67.5 % for disturbed (OOB 32.5 %), and 45.7 % for disturbed samples with neophytes (OOB 54.3 %), based on 12, 6, and 5 clusters, respectively. The decline in classification accuracy with increasing disturbance reflects compositional homogenization and a weakened alignment of the phylogenetic signal with environmental gradients. Our findings demonstrate that incorporating phylogenetic variability in the classification of woody species assemblages enables coherent clustering and effectively captures distributions along environmental gradients particularly under low-disturbance conditions. This approach offers a solid framework to improve forest community classification and to support sustainable forest and conservation management.
... Therefore, we assessed the effect of terrain slope on DEMs and streams accuracy, also analyzing whether it is amplified or concealed by the effect of vertical bias caused by vegetation (i.e., land cover). The terrain slope was derived from a lidar DTM at a 2 m resolution and aggregated to the resolution of each individual global DEM using a mean value (Moudrý et al., 2019). ...
Satellite‐derived global digital elevation models (DEMs) are essential for providing the topographic information needed in a wide range of hydrological applications. However, their use is limited by spatial resolution and vertical bias due to sensor limitations in observing bare terrain. Significant efforts have been made to improve the resolution of global DEMs (e.g., TanDEM‐X) and create bare‐earth DEMs (e.g., FABDEM, MERIT, CEDTM). We evaluated the vertical accuracy of bare‐earth and global DEMs in Central European mountains and submontane regions, and assessed how DEM resolution, vegetation offset removal, land cover, and terrain slope affect stream network delineation. Using lidar‐derived DTM and national stream networks as references, we found that: (a) bare‐earth DEMs outperform global DEMs across all land cover types. RMSEs increased with increasing slope for all DEMs in non‐forest areas. In forests, however, the negative effect of the slope was outweighed by the vegetation offset even for bare‐earth DTMs; (b) the accuracy of derived stream networks was affected by terrain slope and land cover more than by the vertical accuracy of DEMs. Stream network delineation performed poorly in non‐forest areas and relatively well in forests. Increasing slope improved the streams delineation performance; (c) using DEMs with higher resolution (e.g., 12 m TanDEM‐X) improved stream network delineation, but increasing resolution also increased the need for effective vegetation bias removal. Our results indicate that vertical accuracy alone does not reflect how well DEMs perform in stream network delineation. This underscores the need to include stream network performance in DEM quality rankings.
... patibility and model complexity(Moudrý et al., 2019). ...
Aim
Miombo, a prominent dry forest formation, holds ecological importance for both humans and wildlife. Trees are a driving force behind miombo dynamics, thus, spatially explicit metrics of tree cover are essential for evaluating habitat characteristics, resource availability, and environmental change. We developed predictive models and maps of tree species diversity and biomass within a previously undescribed landscape.
Location
Mahale Mountains National Park (MMNP), Greater Mahale Ecosystem (GME), Tanzania.
Methods
We created models of tree density, basal area, tree species richness, and tree diversity according to the Shannon Diversity Index. We created a predictive model using an ensemble modeling approach using plot‐based data from MMNP and predictor variables derived from satellite data associated with climate, habitat structure, plant productivity, and topography. We assessed predictor importance across models and produced maps based on model predictions and compared them to land cover type and protective status.
Results
Results revealed strong positive correlations between tree metrics ( r ≥ 0.70) and substantial overlap in the selection and relative importance of predictors. Canopy height was the most important predictor across models, followed by climate and topography predictors associated with energy. Predictors derived from the soil‐adjusted vegetation index were also valuable. Model performances ranged from R ² values of 0.45 to 0.55, with tree density performing best. Maps show high tree species diversity and biomass in protected areas.
Conclusions
This study and the maps it produced provide a baseline for land management and future modeling efforts in the GME. Our results highlight the contribution of a wide variety of environmental predictors and the importance of a select few. We confirmed the importance of the current protected area network where conservation efforts align, and help sustain, an abundance and diversity of trees. Current and historical disturbance‐related predictors should be considered to address remaining unexplained variance.
... These are in particular related to the quality of the input data, which can significantly impact the fitted models (Araújo et al. 2019;Gábor et al. 2020;Bazzichetto et al. 2023;Smith et al. 2023;Wang and Jackson 2023). Such challenges include, among other issues, the selection of the appropriate scale/grain (Miguet et al. 2016;Wunderlich et al. 2022;Zarzo-Arias et al. 2022) and environmental variables (Williams et al. 2012;Moudrý et al. 2019;Smith and Santos 2020). ...
Species distribution models are widely used in ecology. The selection of environmental variables is a critical step in SDMs, nowadays compounded by the increasing availability of environmental data.
To evaluate the interaction between the grain size and the binary (presence or absence of water) or proportional (proportion of water within the cell) representation of the water cover variable when modeling water bird species distribution.
eBird occurrence data with an average number of records of 880,270 per species across the North American continent were used for analysis. Models (via Random Forest) were fitted for 57 water bird species, for two seasons (breeding vs. non-breeding), at four grains (1 km2 to 2500 km2) and using water cover as a proportional or binary variable.
The models’ performances were not affected by the type of the adopted water cover variable (proportional or binary) but a significant decrease was observed in the importance of the water cover variable when used in a binary form. This was especially pronounced at coarser grains and during the breeding season. Binary representation of water cover is useful at finer grain sizes (i.e., 1 km2).
At more detailed grains (i.e., 1 km2), the simple presence or absence of a certain land-cover type can be a realistic descriptor of species occurrence. This is particularly advantageous when collecting habitat data in the field as simply recording the presence of a habitat is significantly less time-consuming than recording its total area. For models using coarser grains, we recommend using proportional land-cover variables.
... Các bản đồ đa dạng sinh học thường được sử dụng để bảo vệ đa dạng sinh học và thực hiện các chương trình bảo tồn. Bản đồ đa dạng sinh học được ứng dụng rộng rãi là nhờ sự phát triển của mô hình hóa (ví dụ: Mô hình phân bố loài) và Hệ thống thông tin địa lý (GIS) (Burrough, 2001), cùng với sự sẵn có ngày càng tăng của dữ liệu không gian chất lượng cao từ các nguồn viễn thám khác nhau (Moudrý et al., 2019). Khả năng lập bản đồ đa dạng sinh học đạt đến những tiêu chuẩn mà trước đó dường như là điều không thể (Rocchini et al., 2016), biến viễn thám và GIS trở thành một trong những cuộc cách mạng công nghệ lớn có sự tham gia của lĩnh vực sinh thái (Chave, 2013). ...
Sinh học bảo tồn phải thiết lập các ưu tiên bảo tồn địa lý không chỉ dựa trên thành phần hoặc cấu trúc mà còn trên các khía cạnh chức năng của đa dạng sinh học. Tuy nhiên, đánh giá đa dạng chức năng là một thách thức ở quy mô khu vực. Nghiên cứu này đề xuất sử dụng các Nhóm Chức năng Sinh thái (Ecosystem Functional Types - EFT) trích xuất từ vệ tinh để mô tả tính không đồng nhất theo vùng của các động lực sản xuất sơ cấp trong Khu Dự trữ Sinh quyển Rừng ngập mặn Cần Giờ, Việt Nam. Các EFT được xác định dựa trên ba đặc điểm chức năng sinh thái rút ra từ động lực học theo mùa của Chỉ số thực vật nâng cao (Enhanced Vegetation Index): giá trị trung bình năm (đại diện cho sản lượng chính), hệ số biến thiên theo mùa (mô tả tính thời vụ) và ngày đạt cực đại (chỉ báo hiện tượng học). Đánh giá dựa trên EFT của nghiên cứu đã chứng minh mức độ không đồng nhất của khu vực được cảm nhận từ xa trong các chức năng sinh thái có thể củng cố và bổ sung cho các thiết lập ưu tiên bảo tồn truyền thống.
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Geographic conservation priorities must be determined by conservation biology based on both the functional and compositional aspects of biodiversity. However, at the regional level, evaluating functional diversity is difficult. To quantify the regional heterogeneity of primary production dynamics in the Can Gio Mangrove Biosphere Reserve, Vietnam, we propose using Ecosystem Functional Types (EFTs), which are land surface patches that share comparable primary production dynamics and are generated from satellite data. Three ecosystem functional attributes—the annual mean (a proxy for primary production), the seasonal coefficient of variation (a descriptor of seasonality), and the date of maximum (an indicator of phenology)—were used to identify EFTs. These attributes were generated from the seasonal dynamics of the Enhanced Vegetation Index. Our EFT-based analysis shows how remotely sensed regional variation in ecosystem functions may support and enhance established conservation priority choices.
... bioclimatic variables, terrain characteristics such as slope) and categorical (e.g. land cover) predictors are often aggregated or resampled to match the resolution of the response variable (Grohmann, 2015;Moudrý et al., 2019). While not commonly implemented, an alternative approach consists of retaining the discrepancy between the grain sizes of the response and predictor variables through hierarchical modelling. ...
There is a lack of guidance on the choice of the spatial grain of predictor and response variables in species distribution models (SDM). This review summarizes the current state of the art with regard to the following points: (i) the effects of changing the resolution of predictor and response variables on model performance;
(ii) the effect of conducting multi-grain versus single-grain analysis on model performance; and (iii) the role of land cover type and spatial autocorrelation in selecting the appropriate grain size. In the reviewed literature, we found that coarsening the resolution of the response variable typically leads to declining model performance. Therefore, we recommend aiming for finer resolutions unless there is a reason to do otherwise (e.g. expert knowledge of the ecological scale). We also found that so far, the improvements in model performance reported for multi-grain models have been relatively low and that useful predictions can be generated even from single-scale models. In addition, the use of high-resolution predictors improves model performance; however, there is only limited evidence on whether this applies to models with coarser-resolution response variables (e.g. 100 km2 and coarser). Low-resolution predictors are usually sufficient for species associated with fairly common environmental conditions but not for species associated with less
common ones (e.g. common vs rare land cover category). This is because coarsening the resolution reduces variability within heterogeneous predictors and leads to underrepresentation of rare environments, which can lead to a decrease in model performance. Thus, assessing the spatial autocorrelation of the predictors at
multiple grains can provide insights into the impacts of coarsening their resolution on model performance. Overall, we observed a lack of studies examining the simultaneous manipulation of the resolution of predictor and response variables. We stress the need to explicitly report the resolution of all predictor and response
variables.
... The terrain variables (Lecours et al., 2017) were derived from a high-accuracy digital terrain model at a 25-m resolution (Moudrý et al., 2018). The terrain attributes were derived using Horn's algorithm at that resolution and subsequently aggregated to the analysis grain using the mean value from each cell (Moudrý et al., 2019). The human population density obtained from Gallego (2010) was also included in the analysis. ...
Species distribution models (SDMs) are powerful tools in ecology and conservation. Choosing the right environmental drivers and filtering species' occurrences taking their biases into account are key factors to consider before modeling. In this case study, we address five common problems arising during the selection of input data for presence-only SDMs on an example of a general-ist species: the endangered Cantabrian brown bear. First, we focus on the selection of environmental variables that may drive its distribution, testing if climatic variables should be considered at a 1-km analysis grain. Second, we investigate how filtering the species' data in view of (1) their collection procedures , (2) different time frames, (3) dispersal areas, and (4) subpopulations affects the performance and outputs of the models at three different spatial analysis grains (500 m, 1 km, and 5 km). Our results show that models with different input data yielded only minor differences in performance and behaved properly in terms of model validation, although coarsening the analysis grain deteriorated model performance. Still, the contribution of individual variables and the habitat suitability predictions differed among models. We show that a combination of limited data availability and poor selection of environmental variables can lead to inaccurate predictions. Specifically for the brown bear, we conclude that climatic variables should not be considered for exploring habitat suitability and that the best input data for modeling habitat suitability in the study area originate from (1) observations and traces from the (2) most recent period (2006-2019) in which the population is expanding, (3) not considering cells of dispersing bear occurrences and (4) modeling sub-populations independently (as they show distinct habitat preferences). In conclusion , SDMs can serve as a useful tool for generalist species including all available data; still, expert evaluation from the perspective of data suitability for the purpose of modeling and possible biases is recommended. This is especially important when the results are intended for management and conservation purposes at the local level, and for species that respond to the
environment at coarse analysis grains.
... Species distribution modelling is a rapidly evolving field in biogeography and spatial ecology, and the need for clear concepts and standards for modelling has long been acknowledged and advocated (Araújo et al., 2019;Austin & Van Niel, 2011;Jiménez-Valverde et al., 2008;Zurell, Franklin, et al., 2020). The selection of appropriate explanatory variables is crucial, as the variables chosen should adequately represent the main factors affecting species' distributions, e.g., climate, land cover, or topography Gardner et al., 2019;Moudrý et al., 2019;Santini et al., 2021) and ...
Ecosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up‐to‐date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products.
... Failure to explicitly consider the scale can limit SDMs ability to provide reliable conclusions, which are applicable and accurate to aid management actions, are directly affected (Moudrý et al., 2019). The applied scale must satisfy SDMs' assumptions of i) the occurrence records contain no error; and ii) the provided environmental predictors are representative of the physiological tolerances or resource requirements of the species' niche (Austin, 2002;Osborne and Leitão, 2009). ...
Describing and understanding species distributions and the factors driving them is fundamental to ecology and biogeography. Species distribution models (SDMs) allow one to investigate objectives of identifying ecologically important factors to the distribution, estimating species-environment responses, predicting the probability of species occurrence, and predicting species presence or absence. Mosquito occurrence records used in SDMs are often imprecise and represented as a centroid of a geopolitical/administrative boundary. Using a virtual species, we investigated the effect of centroids on SDMs and determined which methodology was best suited to provide accurate and applicable conclusions for each of the objectives. We compared 12 distinct algorithms, four levels of pseudo-absences, and three predictor sets to determine the optimal SDM methodology for each objective. The ability of methodology considerations to account for the effects of centroids varied for each objective. Ecologically important predictors were misidentified but could be best approximated by generalized additive models with 10,000 pseudo-absences. Response curves only captured the expected positive or negative trends. Centroids limited SDMs’ ability to differentiate expected probabilities, resulting in overprediction of high probability areas. Response curves and occurrence probabilities were best estimated by generalized boosting regression models. Species presence was largely over-estimated within southern regions, but underpredicted in northern regions, and was best estimated by weighted mean ensembles. Overall, generalized boosting regression methods and (weighted) mean ensembles provided the most reliable conclusions across all four objectives. Further, the most reliable conclusions were consistently observed with equal pseudo-absences when considered with the removal of low-contributing predictors, except for predictor identification.
... The goal of SDMs is to map species distributions Habitats as predictors in species distribution models: Shall we use continuous or binary data? 2 or to estimate species niches, and there is an ongoing effort to improve their reliability (Araújo et al. 2019, Zurell et al. 2020, Merow et al. 2022. Selecting appropriate environmental predictors is a major methodological challenge of species distribution modeling (Dormann et al. 2007, Austin and Van Niel 2011, Williams et al. 2012, Mod et al. 2016, Misiuk et al. 2018, Moudrý et al. 2019, Smith and Santos 2020, Zurell et al. 2020. These environmental predictors, such as landcover or habitat type, are most often included in SDMs as the area or percentage of a particular land cover type within the individual sites (e.g. ...
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.
... Regardless of the choice of conceptual model and associated metrics, key considerations for the selection of spatial pattern metrics should be their ecological relevance as driver or proxy (Kupfer 2012) of the phenomenon under consideration (Lecours et al. 2016a), the sensitivity of the data to interpretation rules and interdependencies, and any redundancy of indices (Turner 2005;). In metric extraction, researchers should consider neighborhood sizes and temporal, spatial, and thematic resolution of mapped data, scale-dependent effects on pattern metrics that may influence the results of ecological analyses, as well as error propagation (Kendall et al. 2011;Moudrý et al. 2019). To facilitate comparative studies and robust interpretations of the seabed and associated biological communities, researchers should explicitly address their methodology in choosing terrain metrics and extracting them, the ecological rationale behind scales chosen and analysis techniques (Lecours et al. 2016b). ...
Seascape ecology is an emerging pattern‐oriented and integrative science conceptually linked to landscape ecology. It aims to quantify multidimensional spatial structure in the sea and reveal its ecological consequences. The seascape ecology approach has made important advances in shallow coastal environments, and increasing exploration and mapping of the deep seabed provides opportunities for application in the deep ocean. We argue that seascape ecology, with its integrative and multiscale perspective, can generate new scientific insights at spatial and temporal scales relevant to ecosystem‐based management. Seascape ecology provides a conceptual and operational framework that integrates and builds on existing benthic ecology and habitat mapping research by providing additional pattern‐oriented concepts, tools and techniques to (1) quantify complex ecological patterns across multiple scales; (2) link spatial patterns to biodiversity and ecological processes; and (3) provide ecologically meaningful information that is operationally relevant to spatial management. This review introduces seascape ecology and provides a framework for its application to deep‐seabed environments. Research areas are highlighted where seascape ecology can advance the ecological understanding of deep benthic environments.
... We combined the auxiliary data (HEM, COM, and COV) and the terrain characteristics (slope, aspect, and altitude) to model the absolute vertical error (the difference between TDX90 and LiDAR DSM). The terrain slope and aspect were derived from a LiDAR digital terrain model at the 90 m resolution (i.e., we first aggregated the digital terrain model to the 90 m resolution using the mean values and then calculated the terrain characteristics) [47,48]. We used Horn's algorithm with a 3 × 3 cell neighborhood implemented in ArcGIS (version 10.8.1). ...
Several global digital elevation models (DEMs) have been developed in the last two decades.
The most recent addition to the family of global DEMs is the TanDEM-X DEM. The original
version of the TanDEM-X DEM is, however, a nonedited product (i.e., it contains local artefacts such
as voids, spikes, and holes). Therefore, subsequent identification of local artefacts and their editing
is necessary. In this study, we evaluated the accuracy of the original TanDEM-X DEM and its improved
edited version, the Copernicus DEM, in three major European mountain ranges (the Alps,
the Carpathians, and the Pyrenees) using a digital surface model derived from airborne laser scanning
data as a reference. In addition, to evaluate the applicability of data acquisition characteristics
(coverage map, consistency mask, and height error map) and terrain characteristics (slope, aspect,
altitude) to the localization of problematic sites, we modeled their associations with the TanDEM-X
DEM error. We revealed local occurrences of large errors in the TanDEM-X DEM that were typically
found on steep ridges or in canyons, which were largely corrected in the Copernicus DEM. The
editing procedure used for the Copernicus DEM construction was evidently successful as the RMSE
for the TanDEM-X and Copernicus DEMs at the 90 m resolution improved from 45 m to 12 m, from
16 m to 6 m, and from 24 m to 9 m for the Alps, the Pyrenees, and the Carpathians, respectively.
The Copernicus DEM at the 30 m resolution performed similarly well. The boosted regression trees
showed that acquisition characteristics provided as auxiliary data are useful for locating problematic
sites and explained 28–50% of deviance of the absolute vertical error. The absolute vertical error
was strongly related to the height error map. Finally, up to 26% of cells in the Copernicus DEM
were filled using DEMs from different time periods and, hence, users performing multitemporal
analysis or requiring data from a specific time period in the mountain environment should be wary
when using TanDEM-X and its derivations. We suggest that when filling problematic sites using
alternative DEMs, more attention should be paid to the period of their collection to minimize the
temporal displacement in the final products.
... We assessed the effect of the terrain character using the slope and aspect. The terrain slope and aspect were derived from a LiDAR DTM at a 90 m resolution (i.e., we first aggregated the LiDAR DTM to a 90 m resolution using mean values and then calculated the terrain characteristics) [63,64]. We used the Horn's algorithm with a 3 × 3 cell neighbourhood implemented in ArcGIS (version 10.8.1) [65]. ...
The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a high-resolution LiDAR-derived digital surface model (DSM) to evaluate the accuracy of canopy height estimates of the aforementioned global DEMs. In addition, we subtracted SRTM and TanDEM-X data at 90 and 30 m resolutions, respectively, to detect deforestation caused by bark beetle disturbance and evaluated the associations of their difference with terrain characteristics. The study areas covered three Central European mountain ranges and their surrounding areas: Bohemian Forest, Erzgebirge, and Giant Mountains. We found that vertical bias of SRTM and TanDEM-X, relative to the canopy height, is similar with negative values of up to −2.5 m and LE90s below 7.8 m in non-forest areas. In forests, the vertical bias of SRTM and TanDEM-X ranged from −0.5 to 4.1 m and LE90s from 7.2 to 11.0 m, respectively. The height differences between SRTM and TanDEM-X show moderate dependence on the slope and its orientation. LE90s for TDX-SRTM differences tended to be smaller for east-facing than for west-facing slopes, and varied, with aspect, by up to 1.5 m in non-forest areas and 3 m in forests, respectively. Finally, subtracting SRTM and NASA DEMs from TanDEM-X and Copernicus DEMs, respectively, successfully identified large areas of deforestation caused by hurricane Kyril in 2007 and a subsequent bark beetle disturbance in the Bohemian Forest. However, local errors in TanDEM-X, associated mainly with forest-covered west-facing slopes, resulted in erroneous identification of deforestation. Therefore, caution is needed when combining SRTM and TanDEM-X data in multitemporal studies in a mountain environment. Still, we can conclude that SRTM and TanDEM-X data represent suitable near global sources for the identification of deforestation in the period between the time points of their acquisition.
... Several studies have investigated the use and effects of multi-scale calculation methods on specific terrain attributes (e.g., Dolan and Lucieer 2014;Lecours et al. 2017b;Moudr y et al. 2019), but there remains a lack of general guidelines on the appropriateness of multi-scale methods for common benthic habitat mapping applications. The goal of this paper is therefore to investigate the appropriateness of multi-scale raster calculation methods for specific scale manipulation applications, and to provide recommendations on their use. ...
The scale dependence of benthic terrain attributes is well-accepted, and multi-scale methods are increasingly applied for benthic habitat mapping. There are, however, multiple ways to calculate terrain attributes at multiple scales, and the suitability of these approaches depends on the purpose of the analysis and data characteristics. There are currently few guidelines establishing the appropriateness of multi-scale raster calculation approaches for specific benthic habitat mapping applications. First, we identify three common purposes for calculating terrain attributes at multiple scales for benthic habitat mapping: i) characterizing scale-specific terrain features, ii) reducing data artefacts and errors, and iii) reducing the mischaracterization of ground-truth data due to inaccurate sample positioning. We then define criteria that calculation approaches should fulfill to address these purposes. At two study sites, five raster terrain attributes, including measures of orientation, relative position, terrain variability, slope, and rugosity were calculated at multiple scales using four approaches to compare the suitability of the approaches for these three purposes. Results suggested that specific calculation approaches were better suited to certain tasks. A transferable parameter, termed the ‘analysis distance’, was necessary to compare attributes calculated using different approaches, and we emphasize the utility of such a parameter for facilitating the generalized comparison of terrain attributes across methods, sites, and scales.
... The scale at which bathymetric features are measured can affect the ecological relevance of terrain metrics (Moudrý et al., 2019;Walbridge et al., 2018). Research articles included in this review have assessed the ecological effects of terrain on fish assemblages using metrics that were quantified across a variety of spatial scales (i.e. ...
The structure of seafloor terrain affects the distribution and diversity of animals in all seascapes. Effects of terrain on fish assemblages have been reported from most ecosystems, but it is unclear whether bathymetric effects vary among seascapes or change in response to seafloor modification by humans. We reviewed the global literature linking seafloor terrain to fish species and assemblages (96 studies) and determined that relief (e.g. depth), complexity (e.g. roughness), feature classes (e.g. substrate types) and morphology (e.g. curvature), have widespread effects on fish assemblages. Research on the ecological consequences of terrain have focused on coral reefs, rocky reefs, continental shelves and the deep sea (n ≥ 20 studies), but are rarely tested in estuaries (n = 7). Fish associate with a variety of terrain attributes, and assemblages change with variation in the depth and aspect of bathymetric features in reef and shelf seascapes, and in the deep sea. Fish from different seascapes also respond to distinct metrics, with fluctuations in slope of slope (coral reefs), rugosity (rocky reefs) and slope (continental shelves, deep sea) each linked to changes in assemblage composition. Terrain simplification from coastal urbanization (e.g. dredging) and resource extraction (e.g. trawling) can reduce fish diversity and abundance, but assemblages can also recover inside effective marine reserves. The consequences of these terrain changes for fish and fisheries are, however, rarely measured in most seascapes. The key challenge now is to examine how terrain modification and conservation combine to alter fish distributions and fisheries productivity across diverse coastal seascapes.
... Many submitted and published manuscripts do not account for the error propagation arising from the predictor variables. In any case, the way in which aggregation is performed should be reported (Moudrý et al. 2019). ...
Ecological niche models (ENMs) are widely used statistical methods to estimate various types of species niches. After lecturing several editions of introductory courses on ENMs and reviewing numerous manuscripts on this subject, we frequently faced some recurrent mistakes: 1) presence-background modelling methods, such as Maxent or ENFA, are used as if they were pseudo-absence methods; 2) spatial autocorrelation is confused with clustering of species records; 3) environmental variables are used with a higher spatial resolution than species records; 4) correlations between variables are not taken into account; 5) machine-learning models are not replicated; 6) topographical variables are calculated from unprojected coordinate systems, and; 7) environmental variables are downscaled by resampling. Some of these mistakes correspond to student misunderstandings and are corrected before publication. However, other errors can be found in published papers. We explain here why these approaches are erroneous and we propose ways to improve them.
... With a proper fitness-for-use assessment that includes data quality and scale, the resolution of environmental variables can be coarsened before they are integrated into a modelling exercise to minimize the adverse effects of the positional error of species occurrences. However, we are aware that this may involve altering the spatial resolution of data to a level that is no longer eligible for potentially optimal resolution(s), i.e. the scale at which species respond to the environment (Lecours et al. 2015, Moudrý et al. 2019. As demonstrated in , there is a tradeoff between spatial scale and data quality that needs to be evaluated as a part of the fitness-for-use assessment. ...
Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine‐resolution environmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for modelling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine‐scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true distribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models’ performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine‐scale SDMs are considerably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.
... DSMs represent the Earth surface including vegetation, buildings and other natural or man-made objects and can be used, for example, for viewshed analyses [2,3], solar potential estimates [4,5], or improvement of vegetation classification [6,7]. In contrast, DTMs provide a bare earth representation of terrain topography and are frequently used for hydrological modelling [8], species distribution modelling [9][10][11], digital soil mapping [12,13], or yield prediction [14]. ...
Ground filtering is an inevitable step of processing the Light detection and ranging-acquired point clouds.
Our objective was to evaluate the performance of six filtering algorithms. The point clouds filtering and
vertical accuracy were evaluated qualitatively, quantitatively and by comparison with a GNSS survey. All
tested algorithms achieved good results but their performance was affected by the terrain slope and vegetation
cover. Algorithms performed better in forests than in steppes with a high density of low vegetation.
The performance of all algorithms decreased with slopes over 15°. Our results show that some
algorithms tended to cause Type I error while others tended more to the Type II error. Furthermore,
for some algorithms this tendency depended on the vegetation and terrain character. The Progressive
Triangulated Irregular Network algorithm provided overall well-balanced results in all environments.
We propose that software developers should provide users with recommendations of optimal parameters
for individual environments.
Species distribution models (SDMs) have become a common tool in studies of species–environment relationships but can be negatively affected by positional uncertainty of underlying species occurrence data. Previous work has documented the effect of positional uncertainty on model predictive performance, but its consequences for inference about species–environment relationships remain largely unknown. Here we use over 12 000 combinations of virtual and real environmental variables and virtual species, as well as a real case study, to investigate how accurately SDMs can recover species–environment relationships after applying known positional errors to species occurrence data. We explored a range of environmental predictors with various spatial heterogeneity, species' niche widths, sample sizes and magnitudes of positional error. Positional uncertainty decreased predictive model performance for all modeled scenarios. The absolute and relative importance of environmental predictors and the shape of species–environmental relationships co‐varied with a level of positional uncertainty. These differences were much weaker than those observed for overall model performance, especially for homogenous predictor variables. This suggests that, at least for the example species and conditions analyzed, the negative consequences of positional uncertainty on model performance did not extend as strongly to the ecological interpretability of the models. Although the findings are encouraging for practitioners using SDMs to reveal generative mechanisms based on spatially uncertain data, they suggest greater consequences for applications utilizing distributions predicted from SDMs using positionally uncertain data, such as conservation prioritization and biodiversity monitoring.
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 × 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.
Question
Does spectral diversity captured by unmanned aerial systems (UAS) provide reliable information for monitoring the eco‐geomorphological integrity of Mediterranean coastal dune ecosystems? Can this information discriminate between two coastal areas with low (LP) and high (HP) human pressure?
Location
Tyrrhenian coast, Central Italy.
Methods
By processing UAS images, we derived the normalized difference vegetation index (NDVI) and topographic variables at high spatial resolution (0.5 m) for 150 m wide strips starting from the coastline inland on two representative coastal tracts under low and high human pressure. We mapped the sea–inland heterogeneity applying Rao's Q index to the plant biomass (NDVI) and geomorphology variables (elevation and slope). Since Rao's Q index can be calculated in a multidimensional space, we summarized the variability of these three variables into a single eco‐geomorphological layer. We then inspected and compared how the Rao's Q index values for plant biomass, geomorphology and eco‐geomorphology change as a function of the distance from the sea between the two coastal sites.
Results
Rao's Q heterogeneity values vary along the sea–inland gradient of well‐preserved sites (LP). The maximum eco‐geomorphological heterogeneity was found at intermediate distances from the sea and decreased toward the inner sector where the dune geomorphology was more stable and vegetation more homogeneously distributed. Instead, Rao's Q heterogeneity values featured constant low values along the gradient on the HP site, highlighting a simplified eco‐geomorphological gradient related to the high human pressure.
Conclusions
Using UAS, the eco‐geomorphological gradient of coastal dunes can be quantified at a very fine spatial resolution over management‐relevant extents. Rao's Q index applied to sensing imagery successfully captured the differences in the eco‐geomorphological heterogeneity along the sea–inland dune gradient and among sites with different levels of anthropic pressure. This approach supports frequent surveys and is particularly suitable for spatial monitoring of key coastal functions and services.
In the face of rapid biodiversity loss, attention has been increasingly focused on the application of maps towards the challenges of protecting biodiversity. However, biodiversity maps can lead, or have led us into, errors since they are too often not questioned by ecologists, who perceive them as an objective and legitimate representation of the natural world. The aim of this paper is to acknowledge and question our assumptions of (biodiversity) mapping for conservation through an epistemic approach. Discussing two dominant metaphors explaining those cognitive processes involved in mapping and the conventional nature of maps supported by the wide carto-graphic diversity adopted by human societies, I will stress the need to leave behind the belief of an objective approach for biodiversity mapping and conservation goals as opposed to an alternative mapping approach-providing viable alternatives to mitigate or face rapid biodiversity loss in a more "systemic" way. This paper illustrates how biodiversity maps (even though based on up-to-date scientific assumptions), far from being objective and a neutral transcription of nature, are inevitably affected by personal constructions, dominant culture, and sometimes ignorance, or scientific blindness. As a result, it is important to strive and rate maps-not only in terms of scientific accuracy, but also on their "viability"-which is their range of application and how successful they are in achieving the aims for which they are drawn.
Species distribution models (SDMs) have become one of the major predictive tools in ecology. However, multiple methodological choices are required during the modelling process, some of which may have a large impact on forecasting results. In this context, virtual species, i.e., the use of simulations involving a fictitious species for which we have perfect knowledge of its occurrence‐environment relationships and other relevant characteristics, have become increasingly popular to test SDMs. This approach provides for a simple virtual ecologist framework under which to test model properties, as well as the effects of the different methodological choices, and allows teasing out the effects of targeted factors with great certainty. This simplification is therefore very useful in setting up modelling standards and best practice principles. As a result, numerous virtual species studies have been published over the last decade. The topics covered include differences in performance between statistical models, effects of sample size, choice of threshold values, methods to generate pseudo‐absences for presence‐only data, among many others. These simulations have therefore already made a great contribution to setting best modelling practices in SDMs. Recent software developments have greatly facilitated the simulation of virtual species, with at least 3 different packages published to that effect. However, the simulation procedure has not been homogeneous, which introduces some subtleties in the interpretation of results, as well as differences across simulation packages. Here we (1) review the main contributions of the virtual species approach in the SDM literature; (2) compare the major virtual species simulation approaches and software packages; and (3) propose a set of recommendations for best simulation practices in future virtual species studies in the context of SDMs. This article is protected by copyright. All rights reserved.
Demand for models in biodiversity assessments is rising, but which models are adequate for the task? We propose a set of best-practice standards and detailed guidelines enabling scoring of studies based on species distribution models for use in biodiversity assessments. We reviewed and scored 400 modeling studies over the past 20 years using the proposed standards and guidelines. We detected low model adequacy overall, but with a marked tendency of improvement over time in model building and, to a lesser degree, in biological data and model evaluation. We argue that implementation of agreed-upon standards for models in biodiversity assessments would promote transparency and repeatability, eventually leading to higher quality of the models and the inferences used in assessments. We encourage broad community participation toward the expansion and ongoing development of the proposed standards and guidelines.
The use of spatial analytical techniques for describing and classifying seafloor terrain has become increasingly widespread in recent years, facilitated by a combination of improved mapping technologies and computer power and the common use of Geographic Information Systems. Considering that the seafloor represents 71% of the surface of our planet, this is an important step towards understanding the Earth in its entirety. Bathymetric mapping systems, spanning a variety of sensors, have now developed to a point where the data they provide are able to capture seabed morphology at multiple scales, opening up the possibility of linking these data to oceanic, geological, and ecological processes. Applications of marine geomorphometry have now moved beyond the simple adoption of techniques developed for terrestrial studies. Whilst some former challenges have been largely resolved, we find new challenges constantly emerging from novel technology and applications. As increasing volumes of bathymetric data are acquired across the entire ocean floor at scales relevant to marine geosciences, resource assessment, and biodiversity evaluation, the scientific community needs to balance the influx of high-resolution data with robust quantitative processing and analysis techniques. This will allow marine geomorphometry to become more widely recognized as a sub-discipline of geomorphometry as well as to begin to tread its own path to meet the specific challenges that are associated with seabed mapping. This special issue brings together a collection of research articles that reflect the types of studies that are helping to chart the course for the future of marine geomorphometry.
While modelling habitat suitability and species distribution, ecologists must deal with issues related to the spatial resolution of species occurrence and environmental data. Indeed, given that the spatial resolution of species and environmental datasets range from centimeters to hundreds of kilometers, it underlines the importance of choosing the optimal combination of resolutions to achieve the highest possible modelling prediction accuracy. We evaluated how the spatial resolution of land cover/waterbody datasets (meters to 1 km) affect waterbird habitat suitability models based on atlas data (grid cell of 12×11 km). We hypothesized that the area, perimeter and number of waterbodies computed from high resolution datasets would explain distributions of waterbirds better because coarse resolution datasets omit small waterbodies affecting species occurrence. Specifically, we investigated which spatial resolution of waterbodies better explain the distribution of seven waterbirds nesting on ponds/lakes with areas ranging from 0.1 ha to hundreds of hectares. Our results show that the area and perimeter of waterbodies derived from high resolution datasets (raster data with 30 m resolution, vector data corresponding with map scale 1:10,000) explain the distribution of the waterbirds better than those calculated using less accurate datasets despite the coarse grain of the species data. Taking into account the spatial extent (global vs regional) of the datasets, we found the Global Inland Waterbody Dataset to be the most suitable for modelling distribution of waterbirds. In general, we recommend using land cover data of a resolution sufficient to capture the smallest patches of the habitat suitable for a given species’ presence for both fine and coarse grain habitat suitability and distribution modelling.
This article is protected by copyright. All rights reserved.
High resolution remotely sensed bathymetric data is rapidly increasing in volume, but analyzing this data requires a mastery of a complex toolchain of disparate software, including computing derived measurements of the environment. Bathymetric gradients play a fundamental role in energy transport through the seascape. Benthic Terrain Modeler (BTM) uses bathymetric data to enable simple characterization of benthic biotic communities and geologic types, and produces a collection of key geomorphological variables known to affect marine ecosystems and processes. BTM has received continual improvements since its 2008 release; here we describe the tools and morphometrics BTM can produce, the research context which this enables, and we conclude with an example application using data from a protected reef in St. Croix, US Virgin Islands.
Data Set: https://doi.org/10.6084/m9.figshare.5946463
Data Set License: CC-BY
Benthic habitat maps, including maps of seabed sediments, have become critical spatial-decision support tools for marine ecological management and conservation. Despite the increasing recognition that environmental variables should be considered at multiple spatial scales, variables used in habitat mapping are often implemented at a single scale. The objective of this study was to evaluate the potential for using environmental variables at multiple scales for modelling and mapping seabed sediments. Sixteen environmental variables were derived from multibeam echosounder data collected near Qikiqtarjuaq, Nunavut, Canada at eight spatial scales ranging from 5 to 275 m, and were tested as predictor variables for modelling seabed sediment distributions. Using grain size data obtained from grab samples, we tested which scales of each predictor variable contributed most to sediment models. Results showed that the default scale was often not the best. Out of 129 potential scale-dependent variables, 11 were selected to model the additive log-ratio of mud and sand at five different scales, and 15 were selected to model the additive log-ratio of gravel and sand, also at five different scales. Boosted Regression Tree models that explained between 46.4 and 56.3% of statistical deviance produced multiscale predictions of mud, sand, and gravel that were correlated with cross-validated test data (Spearman’s ρmud = 0.77, ρsand = 0.71, ρgravel = 0.58). Predictions of individual size fractions were classified to produce a map of seabed sediments that is useful for marine spatial planning. Based on the scale-dependence of variables in this study, we concluded that spatial scale consideration is at least as important as variable selection in seabed mapping.
Aim
(1) To evaluate whether geographic variation in population abundance of a widespread mammal ( Pecari tajacu ) is related to its location with respect to the centroid of its ecological niche or to the centroid of its geographic range. (2) To assess whether the abundance–niche centrality relationship defines the maximum expected abundance at any location, rather than the realized abundance. (3) To test whether including human impacts improves the abundance–niche centrality relationship, and therefore the prediction of geographic variation in population abundance.
Location
The Americas.
Methods
We modelled the ecological niche of the species using occurrence and environmental data and created spatial models of distance to the niche centroid ( DNC ) and to the geographic centroid ( DGC ). We tested the relationships between population abundance and DNC and between abundance and DGC . We evaluated whether the rate of change in the abundance– DNC relationship was steeper near the upper boundary of quantile regressions. We tested whether the human influence index ( HII ) contributed to improve niche‐based predictions of population abundance. Finally, we generated broad‐scale predictions of collared peccary population abundances.
Results
We found a negative relationship between abundance and DNC and a non‐significant relationship between abundance and DGC . The abundance– DNC relationship was wedge‐shaped, steeper in the upper quantile boundary than in the median. HII also had a negative effect on abundance. The model including DNC and HII was best supported for predicting the median abundance, while DNC alone was the best to predict the upper boundary of population abundances.
Main conclusions
Population abundances are associated with the structure of the ecological niche, especially the maximum abundance expected in an area. Thus, the DNC approach can be useful in obtaining a spatial approximation of potential abundance patterns at biogeographic extents. To achieve a better prediction of realized abundances, it is critical to consider the human influence.
Conservation planning and management typically require accurate and spatially explicit data at scales that are relevant for conservation objectives. In marine conservation, these data are often combined with spatial analytical techniques to produce marine habitat maps. While marine habitat mapping is increasingly used to inform conservation efforts, this field is still relatively young and its methods are rapidly evolving. Because conservation efforts do not always specify standards or guidelines for the production of habitat maps, results can vary dramatically. As representations of real environmental characteristics, habitat maps are highly sensitive to how they are produced. In this review paper, I present four concepts that are known to cause variation in spatial representation and prediction of habitats: the methodology used, the quality and scale of the data, and the choice of variables in regards to fitness for use. I then discuss the potential antinomy associated with the use of habitat maps in conservation: while habitat maps have become an invaluable tool to inform and assist decision-making, maps of the same area built using different methods and data may provide dissimilar representations, thus providing different information and possibly leading to different decisions. Exploring the theories and methods that have proved effective in terrestrial conservation and the spatial sciences, and how they can be integrated in marine habitat mapping practices, could help improve maps used to support marine conservation efforts and result in more reliable products to inform conservation decisions. Having a strong, consistent, transparent, repeatable, and science-based protocol for data collection and mapping is essential for effectively supporting decision-makers in developing conservation and management plans. The development of user-friendly tools to assist in the application of such protocol is crucial to a widespread improvement in practices. I discuss the potential to use interactive and collaborative Geographic Information Systems (GIS) to encourage the conservation and management community, from data collectors and mapmakers to decision-makers, to move toward a digital resilience and to develop such science-based protocol. Until standards and protocols are developed, habitat maps should always be interpreted with care, and the methods and metadata associated with their production should always be explicitly stated.
Remote sensing techniques are currently the main methods providing elevationdata used to produce Digital Terrain Models (DTM). Terrain attributes (e.g. slope,orientation, rugosity) derived from DTMs are commonly used as surrogates of spe-cies or habitat distribution in ecological studies. While DTMs’ errors are known topropagate to terrain attributes, their impact on ecological analyses is howeverrarely documented. This study assessed the impact of data acquisition artefacts onhabitat maps and species distribution models. DTMs of German Bank (off NovaScotia, Canada) at five different spatial scales were altered to artificially introducedifferent levels of common data acquisition artefacts. These data were used in 615unsupervised classifications to map potential habitat types based on biophysicalcharacteristics of the area, and in 615 supervised classifications (MaxEnt) to predictsea scallop distribution across the area. Differences between maps and models builtfrom altered data and reference maps and models were assessed. Roll artefactsdecreased map accuracy (up to 14% lower) and artificially increased models’ per-formances. Impacts from other types of artefacts were not consistent, eitherdecreasing or increasing accuracy and performance measures. The spatial distribu-tion of habitats and spatial predictions of sea scallop distributions were alwaysaffected by data quality (i.e. artefacts), spatial scale of the data, and the selection ofvariables used in the classifications. This research demonstrates the importance ofthese three factors in building a study design, and highlights the need for errorquantification protocols that can assist when maps and models are used in deci-sion-making, for instance in conservation and management.
Data acquisition artefacts are commonly found in multibeam bathymetric data, but their effects on mapping methodologies using geographic information system techniques have not been widely explored. Artefacts have been extensively studied in terrestrial settings, but their study in a marine context has currently been limited to engineering and surveying technology development in order to reduce their amplitude during data collection and postprocessing. Knowledge on how they propagate to further analyses like environmental characterization or terrain analysis is scant. The goal of this paper is to describe the contribution of different types of artefacts to marine terrain attributes at multiple scales. Using multibeam bathymetric data from German Bank, off Nova Scotia (Canada), digital bathymetric models (DBMs) were computed at five different spatial resolutions. Ten different amplitudes of heave, pitch, roll, and time artefacts were artificially introduced to generate altered DBMs. Then, six terrain attributes were derived from each of the reference and altered DBMs. Relationships between the amplitude of artefacts and the statistical and spatial distributions of: 1) altered bathymetry and terrain attributes surfaces and 2) errors caused by the artefacts were modeled. Spatial similarity between altered and reference surfaces was also assessed. Results indicate that most artefacts impact spatial similarity and that pitch and roll significantly impact the statistical distribution of DBMs and terrain attributes while time and heave artefacts have a more subtle impact. Results also confirm the relationship between spatial data quality and spatial scale, as finer-scale data were impacted by artefacts to a greater degree than broader-scale data.
Geomorphometry, the science of quantitative terrain characterization, has traditionally focused on the investigation of terrestrial landscapes. However, the dramatic increase in the availability of digital bathymetric data and the increasing ease by which geomorphometry can be investigated using geographic information systems (GISs) and spatial analysis software has prompted interest in employing geomorphometric techniques to investigate the marine environment. Over the last decade or so, a multitude of geomorphometric techniques (e.g. terrain attributes, feature extraction, automated classification) have been applied to characterize seabed terrain from the coastal zone to the deep sea. Geomorphometric techniques are, however, not as varied, nor as extensively applied, in marine as they are in terrestrial environments. This is at least partly due to difficulties associated with capturing, classifying, and validating terrain characteristics underwater. There is, nevertheless, much common ground between terrestrial and marine geomorphometry applications and it is important that, in developing marine geomorphometry, we learn from experiences in terrestrial studies. However, not all terrestrial solutions can be adopted by marine geomorphometric studies since the dynamic, four-dimensional (4-D) nature of the marine environment causes its own issues throughout the geomorphometry workflow. For instance, issues with underwater positioning, variations in sound velocity in the water column affecting acoustic-based mapping, and our inability to directly observe and measure depth and morphological features on the seafloor are all issues specific to the application of geomorphometry in the marine environment. Such issues fuel the need for a dedicated scientific effort in marine geomorphometry. This review aims to highlight the relatively recent growth of marine geomorphometry as a distinct discipline, and offers the first comprehensive overview of marine geomorphometry to date. We address all the five main steps of geomorphometry, from data collection to the application of terrain attributes and features. We focus on how these steps are relevant to marine geomorphometry and also highlight differences and similarities from terrestrial geomorphometry. We conclude with recommendations and reflections on the future of marine geomorphometry. To ensure that geomorphometry is used and developed to its full potential, there is a need to increase awareness of (1) marine geomorphometry amongst scientists already engaged in terrestrial geomorphometry, and of (2) geomorphometry as a science amongst marine scientists with a wide range of backgrounds and experiences.
Understanding the effects of scale is essential to the understanding of natural eco -
systems, particularly in marine environments where sampling is more limited and sporadic than in
terrestrial environments. Despite its recognized importance, scale is rarely considered in benthic
habitat mapping studies. Lack of explicit statement of scale in the literature is an impediment to
better characterization of seafloor pattern and process. This review paper highlights the importance
of incorporating ecological scaling and geographical theories in benthic habitat mapping. It
reviews notions of ecological scale and benthic habitat mapping, in addition to the way spatial
scale influences patterns and processes in benthic habitats. We address how scale is represented
in geographic data, how it influences their analysis, and consequently how it influences our understanding
of seafloor ecosystems. We conclude that quantification of ecological processes at multiple
scales using spatial statistics is needed to gain a better characterization of species−habitat
relationships. We offer recommendations on more effective practices in benthic habitat mapping,
including sampling that covers multiple spatial scales and that includes as many environmental
variables as possible, adopting continuum-based habitat characterization approaches, using
statistical analyses that consider the spatial nature of data, and explicit statement of the scale at
which the research was conducted. We recommend a set of improved standards for defining
benthic habitat. With these standards benthic habitats can be defined as ‘areas of seabed that are
(geo)statistically significantly different from their surroundings in terms of physical, chemical and
biological characteristics, when observed at particular spatial and temporal scales’.
1.Climate and topography are among the most fundamental drivers of plant diversity. Here, we assess the importance of climate and topography in explaining diversity patterns of species richness, endemic richness and endemicity on the landscape scale of an oceanic island, and evaluate the independent contribution of climatic and topographic variables to spatial diversity patterns.
2.We constructed a presence/absence matrix of perennial endemic and native vascular plant species (including subspecies) in 890 plots on the environmentally very heterogeneous island of La Palma, Canary Islands. Species richness, endemic richness and endemicity were recorded, interpolated and related to climate (i.e. variables describing temperature, precipitation, variability and climatic rarity) and topography (i.e. topographic complexity, solar radiation, geologic age, slope and aspect). We used multi-model inference, spatial autoregressive models, variance partitioning and linear regression kriging as statistical methods.
3.Species richness is best explained by both climatic and topographic variables. Topographic variables (esp. topographic complexity and solar radiation) explain endemic richness and climatic variables (esp. elevation/temperature and rainfall seasonality) explain endemicity. Spatial patterns of species richness, endemic richness and endemicity were in parts geographically decoupled from each other.
4.Synthesis. We identify several topography-dependent processes ranging from evolutionary processes (micro-refugia, in situ speciation, pre-adaptation to rupicolous conditions, dispersal limitations) to human-induced influences (introduced herbivores, fire, land use) that possibly shape the endemic richness pattern of La Palma. In contrast, climate mainly drives endemicity, which is connected to ecological speciation and specialisation to local conditions. We highlight the importance of incorporating climatic variability into future studies of plant species diversity and endemism. The spatial incongruence in hotspots of species richness, endemic richness and endemicity emphasize the need for an integrated conservation approach acknowledging different diversity measures to protect the complete spectrum of diversity. High-elevation islands such as La Palma are highly suitable to study drivers of diversity and endemism, as they offer environmental gradients of continental magnitude on the landscape scale of a single climatic mini-continent and a large array of in situ-speciated endemics.
virtualspecies is a freely available package for R designed to generate virtual species distributions, a procedure increasingly used in ecology to improve species distribution models. This package combines the existing methodological approaches with the objective of generating virtual species distributions with increased ecological realism. The package includes (1) generating the probability of occurrence of a virtual species from a spatial set of environmental conditions (i.e., environmental suitability), with two different approaches; (2) converting the environmental suitability into presence-absence with a probabilistic approach; (3) introducing dispersal limitations in the realised virtual species distributions and (4) sampling occurrences with different biases in the sampling procedure. The package was designed to be extremely flexible, to allow users to simulate their own defined species-environment relationships, as well as to provide a fine control over every simulation parameter. The package also includes a function to generate random virtual species distributions. We provide a simple example in this paper showing how increasing ecological realism of the virtual species impacts the predictive performance of species distribution models. We expect that this new package will be valuable to researchers willing to test techniques and protocols of species distribution models as well as various biogeographical hypotheses.This article is protected by copyright. All rights reserved.
Urban environment is only rarely considered an exclusive refuge for rapidly declining bird species. Crested Lark (Galerida cristata) is a species in Central Europe whose synanthropization began in the second half of the 20th century due to dramatic changes in agriculture and landscape structure. We analyze how changes in these distribution patterns mirror landscape structures and demonstrate colonization potential from adjacent refuges. We used Czech Breeding Bird Atlas data from two periods, 1985–1989 and 2001–2003, to model distribution change patterns for Crested Larks in two parts of the country, Bohemia and Moravia. Mapping quadrats comprised the sampling units, and binomial species presence or absence was used to model the change patterns using generalized linear models while considering landscape structure, demographic data and dominant habitat attributes as predictors. Despite similar attributes of landscape structures in Bohemia and Moravia, Crested Larks clearly differed in their extinction patterns. Within isolated Bohemia, the remnant subpopulations were restricted to early successional stands around commercial areas in urban zones. In Moravia, only altitude appeared related to the species’ disappearance. Moravia is connected at the south to the Pannonian Plain with stable or increasing Crested Lark populations, which we consider responsible for the significantly higher number of newly occupied quadrats in Moravia. Our results indicate the more isolated Bohemian population is more prone to extinction than is the Moravian population with its greater colonization potential. The study notes a rare example of urban zones serving as refuges for a bird species demanding early successional stands.
Despite being identified as a driver of mobile predator aggregations (hotspots) in both marine and terrestrial environments, topographic complexity has long remained a challenging concept for scientists to visualise and a difficult parameter to estimate. It is only with the advent of high-speed computers and the recent popularisation of geographical information systems (GIS) that terrain attributes have begun to be quantitatively measured in three-dimensional space and related to wildlife dynamics, making the well-established field of geomorphometry (or ‘digital terrain modelling’) a discipline of growing appeal to biologists. Although a diverse array of numerical metrics is now available to describe the shape, geometry and physical properties of natural habitats, few of these are known to, or adequately used by, ecologists. In this review, we examine the nature and usage of 56 geomorphometrics extracted from the ecological modelling literature over a period of 32 years (1979–2011). We show that, in studies of mobile predators, numerous topographic variables have largely been overlooked in favour of single basic metrics that do not, on their own, fully capture the complexity of continuous landscapes. Based on a simulation approach, we assess the redundancy and correlation structure of these metrics and demonstrate that a majority are highly collinear. We highlight a suite of 7–8 complementary metrics which best explain topographic patterns across a bathymetric grid of the west Australian seafloor, and contend that field and analytical protocols should prioritise variables of these types, particularly when the responses of predator populations to physical habitat features are of interest. We suggest that prominent structures such as canyons, seamounts or mountain chains can serve as useful proxies for predator hotspots, especially in remote locations where access to high-resolution biological data is often limited.
Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species' micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions - and therefore local management - compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.
Animals select habitat resources at multiple spatial scales. Thus, explicit attention to scale dependency in species–habitat relationships is critical to understand the habitat suitability patterns as perceived by organisms in complex landscapes. Identification of the scales at which particular environmental variables influence habitat selection may be as important as the selection of variables themselves. In this study, we combined bivariate scaling and Maximum entropy (Maxent) modeling to investigate multiscale habitat selection of endangered brown bear (Ursus arctos) populations in northwest Spain. Bivariate scaling showed that the strength of apparent habitat relationships was highly sensitive to the scale at which predictor variables are evaluated. Maxent models on the optimal scale for each variable suggested that landscape composition together with human disturbances was dominant drivers of bear habitat selection, while habi-tat configuration and edge effects were substantially less influential. We found that explicitly optimizing the scale of habitat suitability models considerably improved sin-gle-scale modeling in terms of model performance and spatial prediction. We found that patterns of brown bear habitat suitability represent the cumulative influence of habitat selection across a broad range of scales, from local resources within habitat patches to the landscape composition at broader spatial scales.
Accurate spatio-temporal predictions of land-cover are fundamentally important for assessing geomorphological and ecological patterns and processes. This study quantifies the epistemic uncertainty in the species distribution modeling, which is generated by spatio-temporal gaps between the biogeographical data, model selection and model complexity. Epistemic uncertainty is generally given by the sum of subjective and objective uncertainty. The subjective uncertainty generated by the modeler-choice in the manipulation of the environmental variables was analyzed. The Snowy Plover in Florida (Charadrius alexandrinus nivosus, SP), a residential shorebird whose geographic range is extended along the Panhandle-Big Bend-Peninsula Gulf coast was considered as case-study. The first fundamental step for studying the species distribution and how it will be affected by climate change is to obtain an accurate description of the shorebird coastal habitat. The land-cover was translated into ecosystem classes using a land-cover model that predicts the evolution of coastal ecosystems affected by sea-level rise scenarios. The best land-cover map decreased the objective uncertainty (intrinsically present in data or models) in representing the spatial structure of the coastal ecosystem, reduced the temporal gaps with the occurrence data, and diminished the subjective uncertainty due to the conversion from land-cover to model-classes. Multimodeling was performed to reduce the uncertainty in the prediction of the species distribution related to model uncertainty. The best representation of the species distribution was performed by MaxEnt. The area under the receiver operating characteristic curve (AUC), the omission/commission test, the similarity index of the response curves, and the jackknife test were used simultaneously as indicators of the predictability of each species distribution model. The availability of updated high-resolution biogeoclimatological data was proven to be necessary in order to properly predict the species ranges for conservation purposes.
Increased focus on predictive aspects of ecology has recently been urged by scientists and policy makers to provide solutions to pressing societal needs. Current challenges include the large knowledge gap on the spatial distribution of marine biodiversity, and its associated goods and services, and the dependence of model performance on spatial resolution. We evaluated the importance of resolution on the predictive power and precision of empirical models of distributions of marine sessile invertebrates and macroalgae along the Swedish west coast. This was done by simulating the limits to prediction, based on 2 independent simulated proportions of biological variables, and comparing these limits to observed models at different resolutions. Simulations showed the highest achievable predictive power (r(2)) and precision (RMSE) of models at fine resolutions (similar to 1 m). In contrast to the simulations, the performance of quantitative models was better at relatively coarse resolutions (similar to 100 m). Increased model performance at coarse resolutions could not be explained by differences in sampling or spatial variability. Instead, the improvement is likely caused by the mechanistic coupling (direct or indirect) between predictor variables, depth and hard substratum cover and patterns at coarser scales, whereas complex processes, e. g. biological interactions, shape patterns at finer scales. This match between resolution and the scale at which environmental variables operate may differ among systems, which could explain the discrepancy in outcomes between our study and previous studies. Furthermore, we provide an approach for error analysis that identifies contributions of different model components to the total uncertainty, thus facilitating model optimization.
Species distribution models (SDMs) are widely used in ecology and conservation. However, their performance is known to be affected by a variety of factors related to species occurrence characteristics. In this study, we used a virtual species approach to overcome the difficulties associated with testing of combined effects of those factors on performance of presence-only SDMs when using real data. We focused on the individual and combined roles of factors related to response variable (i.e. sample size, sampling bias, environmental filtering, species prevalence, and species response to environmental gradients). Results suggest that environmental filtering is not necessarily helpful and should not be performed blindly, without evidence of bias in species occurrences. The more gradual the species response to environmental gradients is, the greater is the model sensitivity to an inappropriate use of environmental filtering, although this sensitivity decreases with higher species prevalence. Results show that SDMs are affected to the greatest degree by the species response to environmental gradients, species prevalence, and sample size. Models’ accuracy decreased with sample size below 300 presences. Furthermore, a high level of interactions among individual factors was observed. Ignoring the combined effects of factors may lead to misleading outcomes and conclusions.
Mining is an important human activity that significantly affects the landscape character, particularly through excavation of spoil material and its deposition on spoil banks. The information on terrain or vegetation cover of spoil banks is often required for two different reasons: (i) to monitor and prevent adverse effect of hazards associated with unstable terrain; and (ii) to assess restoration success. Traditionally used in situ methods for monitoring surface displacement or restoration success are restricted in terms of spatial and temporal coverage. Therefore, in this study, we assessed the value of photogrammetrically and Light Detection and Ranging (LiDAR) derived point clouds for characterizing a post-mining site. We acquired images under leaf-off and leaf-on conditions and showed that point densities of point clouds acquired photogrammetrically under leaf-off conditions exceeded densities of those acquired under leaf-on conditions and uniformly covered ground of the entire study area (an average density of 288 points per m ² ). In addition, the accuracy of the digital terrain model (DTM; 1 m resolution) derived from images acquired under leaf-off conditions was comparable to the LiDAR-derived DTM (RMSE of 0.19 m and 0.12 m, respectively). While LiDAR-derived DTM accuracies were consistent across vegetation categories (RMSE 0.12–0.14 m), accuracy of image-based DTMs declined in the following order: forest (RMSE 0.15 m), steppes (RMSE 0.21 m), and aquatic vegetation (RMSE 0.36 m). We suggest the leaf-off UAV imagery as a viable alternative for building DTMs that can be utilized for assessment of risks associated with instability of spoil banks terrain. In addition, we also suggest that a combination of acquisitions under leaf-off and leaf-on conditions have a potential to replace expensive airborne LiDAR surveys for applications requiring information on vegetation cover or vegetation height.
Preventing biological invasions in areas of conservation concern represents the most cost-effective strategy to minimize the impact of Invasive Alien Species (IAS) on native communities. In this context, invasive Species Distribution Models (iSDMs) are a sound tool for predicting invasion risk in protected areas. Although their potential to support invasive species managers is widely acknowledged, evidence of iSDMs providing actual conservation solutions is still scarce. With this work, we propose an iSDM-based methodology that accounts for the role of multiple invasion drivers to assess the risk posed by IAS in protected areas. Using a binomial Generalized Additive Model, we predicted the occurrence of a highly invasive plant in Mediterranean dune systems. According to the predicted IAS occurrence, we quantified the invasibility of protected sites inside the study area, classified each site in low to medium-high invasibility categories, and identified site-specific management perspectives. Our results encourage the use of iSDMs as a means of evaluating the risk posed by IAS in protected areas, and, at the same time, represent an attempt to fill the gap between theory and practice in conservation decision-making.
It is now widely acknowledged that the increasing availability of remotely sensed data facilitates ecological modelling. Digital elevation models (DEMs) are arguably one of the most common remote sensing products used in this context. Topographic indices (e.g. slope, orientation, rugosity) derived from DEMs are widely used as surrogates for field-measured environmental variables. Available global DEMs, such as those from the shuttle radar topography mission (SRTM), however, do not provide information on bare-earth elevation as they measure elevation of the highest objects above the ground (e.g. canopy). This affects the derived topographic indices and limits the use of global DEMs in ecological modelling. Unfortunately, most ecological studies ignore this limitation despite the fact that methods to remove the vegetation offset have been developed. We used high resolution LiDAR DTM to assess the accuracy of two newly available global bare-earth DEMs where such methods were applied and to compare them with the SRTM DEM. Furthermore, we assessed the effect of DEMs' vertical error on species distribution models (SDMs) by calculating slope and topographic wetness index (TWI) from these different models and evaluating their suitability for SDMs by adopting a virtual species approach. We simulated virtual species based on slope and TWI derived from accurate LiDAR DTM at three resolutions (30 m, 90 m and 900 m) and developed univariate generalized models to assess the performance of the bare-earth and SRTM DEMs. Our results show that the vertical error in both newly available, vegetation-corrected global DEMs is indeed successfully reduced. The overall vertical root mean squared error (RMSE) was 10.52 m for SRTM, while it was 6.80 m and 6.25 m for the two global bare-earth DEMs. The effect of the vertical error on SDMs was most significant at finer spatial resolutions. Using SRTM DEM, as opposed to a more accurate bare-earth DEM, led to a decline in area under curve (AUC) values from 0.94 to 0.77. SDMs fitted with slope and TWI derived from new global bare-earth DTMs performed slightly better than SRTM. Since methods for vegetation-offset removal in DEMs exist and corrected DEMs are freely available, we argue that the vertical accuracy of DEMs should be more consistently considered. Local, high-accuracy DEMs should be used where available; in remaining instances, however, global DEMs where vertical bias was minimized should be used in ecological modelling. Further improvement of global DEMs at 30 m and better resolutions are needed to enhance accuracy of derived indices and ecological models.
Understanding species’ responses to environmental conditions, and how these species–environment associations shape spatial distributions, are longstanding goals in ecology and biogeography. However, an essential component of species–environment relationships – the spatial unit, or grain, at which they operate – remains unresolved. We identify three components of scale‐dependence in analyses of species–environment associations: 1) response grain, the grain at which species respond most strongly to their environment; 2) environment spatial structure, the pattern of spatial autocorrelation intrinsic to an environmental factor; and 3) analysis grain, the grain at which analyses are conducted and ecological inferences are made.
We introduce a novel conceptual framework that defines these scale components in the context of analyzing species–environment relationships, and provide theoretical examples of their interactions for species with various ecological attributes. We then use a virtual species approach to investigate the impacts of each component on common methods of measuring and predicting species–environment relationships. We find that environment spatial structure has a substantial impact on the ability of even simple, univariate species distribution models (SDMs) to recover known species–environment associations at coarse analysis grains. For simulated environments with ‘fine’ and ‘intermediate’ spatial structure, model explanatory power, and the frequency with which simple SDMs correctly estimated a virtual species’ response to the simulated environment, dramatically declined as analysis grain increased.
Informed by these results, we use a scaling analysis to identify maximum analysis grains for individual environmental factors, and a scale optimization procedure to determine the grain of maximum predictive accuracy. Implementing these analysis grain thresholds and model performance standards in an example east African study system yields more accurate distribution predictions, compared to SDMs independently constructed at arbitrary analysis grains. Finally, we integrate our conceptual framework with virtual and empirical results to provide practical recommendations for researchers asking common questions about species–environment relationships.
Climate changes pose serious challenges to the persistence of plant species, especially those with narrow habitats. Failure to adopt timely measures would lead to the permanent loss of valuable endangered species. This study used maximum entropy to predict the geographical distribution of a medicinal and vulnerable plant species, Daphne mucronata Royle, under current and future climatic conditions (A2a/HadCM3) in central Iran. A total of 100 locations with the species occurrence were recorded. Three topographic variables (slope, elevation, and aspect) and 19 bioclimatic variables derived from monthly data (spatial resolution = 1 km) were used in the modeling process. The results showed that D. mucronata distribution was largely affected by elevation, annual precipitation, and precipitation of coldest quarter. According to species response curves, this species preferred habitats with mean precipitation of coldest quarter from 120 to 140 mm, annual precipitation of 240–280 mm, and elevation more than 2600 m above sea level. The climate change projection (A2a/HadCM3) indicated that by 2030 and 2080, D. mucronata would disappear in sites located below 2000 m, remain unchanged in elevations more than 3000 m, and undergo a drastic change in sites located at 2000–3000 m above sea level. This information is essential for conservation planners and rangeland managers who work on protecting the species from extinction. A similar approach can be adopted to identify sites with high extinction probability of endangered species and to protect susceptible habitats from the effects of climate change and future modifications.
Terrain attributes (e.g. slope, rugosity) derived from digital terrain models are commonly used in environmental studies. The increasing availability of GIS tools that generate those attributes can lead users to select a sub-optimal combination of terrain attributes for their applications. Our objectives were to identify sets of terrain attributes that best capture terrain properties and to assess how they vary with surface complexity. 230 tools from 11 software packages were used to derive terrain attributes from nine surfaces of different topographic complexity levels. Covariation and independence of terrain attributes were explored using three multivariate statistical methods. Distinct groups of correlated terrain attributes were identified, and their importance in describing a surface varied with surface complexity. Terrain attributes were highly covarying and sometimes ambiguously defined within software documentation. We found that a combination of six to seven particular terrain attributes always captures more than 70% of the topographic structure of surfaces.
Aim
To synthesize the species distribution modelling (SDM) literature to inform which variables have been used in MaxEnt models for different taxa and to quantify how frequently they have been important for species’ distributions.
Location
Global.
Methods
We conducted a quantitative synthesis analysing the contribution of over 400 distinct environmental variables to 2040 MaxEnt SDMs for nearly 1900 species representing over 300 families. Environmental variables were grouped into 24 related factors and results were analysed by examining the frequency with which variables were found to be most important, the mean contribution of each variable (at various taxonomic levels), and using TrueSkill™, a Bayesian skill rating system.
Results
Precipitation, temperature, bathymetry, distance to water and habitat patch characteristics were the most important variables overall. Precipitation and temperature were analysed most frequently and one of these variables was often the most important predictor in the model (nearly 80% of models, when tested). Notably, distance to water was the most important variable in the highest proportion of models in which it was tested (42% of 225 models). For terrestrial species, precipitation, temperature and distance to water had the highest overall contributions, whereas for aquatic species, bathymetry, precipitation and temperature were most important.
Main conclusions
Over all MaxEnt models published, the ability to discriminate occurrence from reference sites was high (average AUC = 0.92). Much of this discriminatory ability was due to temperature and precipitation variables. Further, variability (temperature) and extremes (minimum precipitation) were the most predictive. More generally, the most commonly tested variables were not always the most predictive, with, for instance, ‘distance to water’ infrequently tested, but found to be very important when it was. Thus, the results from this study summarize the MaxEnt SDM literature, and can aid in variable selection by identifying underutilized, but potentially important variables, which could be incorporated in future modelling efforts.
Studies often use breeding bird atlases to assess species’ habitat requirements or to estimate species’ potential distribution under environmental changes. In breeding bird atlases, one of the attributes recorded for each grid square is evidence of breeding. The attribute represent probability of breeding (confirmed, probable, possible) categorized according to breeding behaviour. However, the majority of studies often make arbitrary decisions on which category to use. This may have severe consequences for results. This study evaluated whether models’ discrimination ability change by inclusion of ambiguous breeding categories (probable, possible). We compared models’ predictions for distribution of nine wetland birds derived from Atlas of the breeding distribution of birds in the Czech Republic. For each species, we developed generalized linear models using combinations of the breeding categories as input to model calibration and validation. Our results show that the discrimination ability (AUC) decreased in most cases when all breeding categories were uncritically used in calibration and validation process. On the other hand, however, inclusion of probable and possible breeding categories to model calibration did not affect models’ abilities to predict confirmed presences and absences. This implies that inclusion of ambiguous breeding categories has more serious impact on models’ performance when added to validation than to calibration data set. We advocate for more rigorous use of different breeding categories and emphasize that widely used atlases from citizen science programmes offer more than simple occurrence data. Additional attributes (e.g. breeding category, sampling effort) should be used to select high quality data to validate the models.
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Aims: The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive accuracy and model realism, as highlighted in multiple earlier studies. Because variable selection is directly related to a model’s capacity to capture important species’ environmental requirements, one would expect an explicit prior consideration of all ecophysiologically meaningful variables. For plants, these include temperature, water, soil nutrients, light, and in some cases, disturbances and biotic interactions. However, the set of predictors used in published correlative plant SDM studies varies considerably. No comprehensive review exists of what environmental predictors are meaningful, available (or missing) and used in practice to predict plant distributions. Contributing to answer these questions is the aim of this review.
Methods: We carried out an extensive, systematic review of recently published plant SDM studies (years 2010–2015; n = 200) to determine the predictors used (and not used) in the models. We additionally conducted an in-depth review of SDM studies in selected journals to identify temporal trends in the use of predictors (years 2000–2015; n = 40).
Results: A large majority of plant SDM studies neglected several ecophysiologically meaningful environmental variables, and the number of relevant predictors used in models has stagnated or even declined over the last 15 yr.
Conclusions: Neglecting ecophysiologically meaningful predictors can result in incomplete niche quantification and can thus limit the predictive power of plant SDMs. Some of these missing predictors are already available spatially or may soon become available (e.g. soil moisture). However, others are not yet easily obtainable across whole study extents (e.g. soil pH and nutrients), and their development should receive increased attention. We conclude that more effort should be made to build ecologically more sound plant SDMs. This requires a more thorough rationale for the choice of environmental predictors needed to
meet the study goal, and the development of missing ones. The latter calls for increased collaborative effort between ecological and geo-environmental sciences.
The most appropriate range of spatial resolutions of environmental data with which to accurately delimit potential distributions of aquatic invasive species (AIS), in shallow nearshore marine environments, using species distribution models (SDM), is currently unknown. This study used SDM to determine the optimal range of spatial resolutions of temperature and salinity data with which to predict the potential distribution of vase tunicate Ciona intestinalis on the Canadian east coast and European green crab Carcinus maenas on the Canadian west coast. Both of these problematic AIS have spread rapidly in temperate nearshore coastal waters. As the invasion success of these species in temperate seasonal environments is constrained by temperature and salinity, we used SDM, specifically MaxEnt, to correlate these environmental variables at a range of spatial resolutions (100 km to 100s of metres, the latter encompassing 100 or 500 m2 on east and west coasts, respectively) with both species' occurrence data. Increasing spatial resolution from 100 km to 100s of metres of temperature and salinity data generally resulted in more accurate estimates of each species' distribution, including a more realistic depiction of how salinity and temperature shape their distributions, with several caveats. First, increasing resolution of temperature and salinity data did not translate into proportional increases in model performance. Secondly, the highest resolution (100s of metres) did not result in the most accurate predictions of east coast C. intestinalis distribution. Finally, lower spatial resolutions (i.e. 100 km to 8 km resolution) performed worse in MaxEnt for west coast C. maenas than for east coast C. intestinalis. Overall, finer-resolution patchiness in each species' distribution was accurately resolved at or below spatial resolutions of 9 km for east coast C. intestinalis or 4 km for west coast C. maenas.
Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes.
An understanding of how terrain features influence abundance of a particular species greatly aids in the development of accurate predictive habitat suitability models. In this study, we investigated the observed seafloor coverage of cold-water coral Lophelia pertusa in relation to seabed topography at the Sotbakken and Røst Reefs on the Norwegian margin. The primary terrain features at the study sites are a SW-NE stretching mound at Sotbakken Reef and SW-NE running ridges at Røst Reef, located at depths of ~300–400 m and ~250–320 m respectively. Ship-borne multibeam bathymetry data, JAGO dive video data and JAGO positioning data were used in this study. Terrain variables were calculated at scales of 30 m, 90 m and 170 m based on the bathymetry data. Additionally, we investigated the relationships between the terrain variables at multiple scales using the Unweighted Pair Group Method.
The observed L. pertusa coverage at both reefs was found to be significantly correlated with most investigated terrain variables, with correlations increasing in strength with increase in analysis scale, suggesting that large scale terrain features likely play an important role in influencing L. pertusa distribution. Small scale terrain variations appear less important in determining the suitability of a region of seafloor for L. pertusa colonization. We conclude that bathymetric position index and curvature, as well as seabed aspect, most strongly correlate with coral coverage, indicating that local topographic highs, with an orientation into inflowing bottom currents, are most suitable for L. pertusa habitation.
These results indicate that developing habitat suitability models for L. pertusa will benefit from inclusion of particular key terrain variables (e.g. aspect, plan curvature, mean curvature and slope) and that these should ideally be computed at multiple spatial scales with a greater gap in scales than we used in this study, to maximize the inclusion of the key variables in the model whilst minimizing redundancy.
Species distribution models (SDMs) are increasingly used to predict species ranges and their shifts under future scenarios of global environmental change (GEC). SDMs are thus incorporating key drivers of GEC (e.g. climate, land use) to improve predictions of species' habitat suitability (i.e. as an indicator of species occurrence). Yet, most SDMs incorporating land use only consider dominant land cover types, largely ignoring other key aspects of land use such as land management intensity and livestock. We developed SDMs including main land use components (i.e. land cover, livestock and its management intensity) to assess their relative importance in shaping habitat suitability for the Egyptian vulture, an endangered raptor linked to livestock presence. We modelled current and future (2020 and 2050) habitat suitability for this vulture using an organism-centred approach. This allowed us to account for basic species' habitat needs (i.e. nesting cliff) while gaining insight into our variables of interest (i.e. livestock and land cover). Once nest-site requirements were fulfilled, land use variables (i.e. openland and sheep and goat density) were the main factors determining species' habitat suitability. Current suitable area could decrease by up to 6.81% by 2050 under scenarios with rapid economic growth but no focus on environmental conservation and rural development. Local solutions to environmental sustainability and rural development could double current habitat suitability by 2050. Land use is expected to play a key role in determining Egyp-tian vulture's distribution through land cover change but also through changes in livestock management (i.e. species and stocking density). Change in stocking densities (sheep and goats/km 2) becomes thus an indicator of habitat suitability for this vulture in our study area. Abandonment of agro-pastoral practises (i.e. below ∼15–20 sheep and goats/km 2) will negatively influence the species distribution. Nonetheless, livestock densities above these values will not further increase habitat suitability. Given the widespread impacts of livestock on ecosystems, the role of livestock and its management intensity in SDMs for other (non-livestock-related) species should be further explored.
Modelling and mapping habitat suitability of species in a given study area has become a popular method to assess biogeographical questions on the habitat requirements of species, and to study the effects of land use or climate change on species distributions. Beside purely academic aims, model projections are increasingly used for devising conservation and management strategies and they are an established tool in conservation planning. Species distribution models (SDMs) link geographic presence records of species with environmental conditions found at these locations, and extrapolate the occurrence probabilities of species throughout the study area. While the modelling framework is similar in the terrestrial, marine and freshwater realms, each realm comprises specific challenges for combining the spatial scale, the environmental data and the species records for building reliable models. In this paper, we focus on these key issues from a stream ecology perspective and highlight three critical challenges to be kept in mind when building SDMs in stream ecosystems: (1) the spatial configuration in terms of the hierarchical structure of catchments and dendritic stream networks; (2) obtaining relevant and spatially continuous environmental predictors along the stream network, sourcing from three interfaces (stream vs. atmosphere, catchment area, groundwater) and (3) species detectability and thus the challenge of obtaining freshwater species occurrence data along the stream network. To depict freshwater species distributions at the stream level in the best possible way, multi-scale models are recommended, which account for scale dependence, a stronger integration of various environmental data types and sources, and heterogeneous species data. While model projections have the potential to serve as reliable tools for conservation and management purposes, this is only true if these relevant factors are considered in the modelling framework.
This paper investigates differences between morphometric parameters (slope and aspect) derived from a resampled DEM and resampled morphometric data derived from a medium resolution DEM, with examples for three study areas in South America selected to represent flatlands, hilly terrain, and mountain ranges. Using a low resolution DEM for regional scale morphometric analysis is not an optimal choice, since attenuation of elevation will strongly affect the distribution of calculated parameters. Unless bounded by computational constraints, one should choose to derive basic morphometric parameters from higher resolution data, and resample it to a coarser resolution as needed.
It is argued that the problem of pattern and scale is the central problem in ecology, unifying population biology and ecosystems science, and marrying basic and applied ecology. Applied challenges, such as the prediction of the ecological causes and consequences of global climate change, require the interfacing of phenomena that occur on very different scales of space, time, and ecological organization. Furthermore, there is no single natural scale at which ecological phenomena should be studied; systems generally show characteristic variability on a range of spatial, temporal, and organizational scales. The observer imposes a perceptual bias, a filter through which the system is viewed. This has fundamental evolutionary significance, since every organism is an "observer" of the environment, and life history adaptations such as dispersal and dormancy alter the perceptual scales of the species, and the observed variability. It likewise has fundamental significance for our own study of ecological systems, since the patterns that are unique to any range of scales will have unique causes and biological consequences. The key to prediction and understanding lies in the elucidation of mechanisms underlying observed patterns. Typically, these mechanisms operate at different scales than those on which the patterns are observed; in some cases, the patterns must be understood as emerging form the collective behaviors of large ensembles of smaller scale units. In other cases, the pattern is imposed by larger scale constraints. Examination of such phenomena requires the study of how pattern and variability change with the scale of description, and the development of laws for simplification, aggregation, and scaling. Examples are given from the marine and terrestrial literatures.
Bathymetry data from multibeam echosounders and other acoustic sources are now widely available in the form of digital terrain models, which are conveniently displayed as raster grids in desktop geographic information systems. Calculation of terrain variables such as slope is a simple push-button operation in most geographic information systems; however, as we demonstrate here, there can be a great variation in the slope values obtained due to computation algorithms and resolution or analysis scale. This article also demonstrates how Monte Carlo simulation can be used to visualise uncertainty in the underlying bathymetry dataset and also how this uncertainty impacts on slope calculations.
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Given species' vulnerability to climate change, land use change, and habitat loss, it is pertinent to examine how the distribution of a particular species is related to those factors. We assessed the use of climate, habitat, and topography data for modeling the distributions of 14 central European wetland birds, and compared the relative importance of these factors among bird groups with differing latitudinal distributions in Europe. We used the Third Atlas of Breeding Birds in the Czech Republic as a source of species distribution data. Variables were derived from Corine Land Cover, WorldClim, and Shuttle Radar Topography Mission (SRTM) data. Hierarchical partitioning and multiple logistic models identified climatic, topographical, and habitat predictors as important determinants of distribution for each of the species under study. However, the relative contributions of particular variables differed among the species. Climatic, topographical, and habitat factor groups also differed in their importance to latitudinal species groups. Our results indicated that wetland birds with range margins close to the Czech Republic were potentially limited by two different factors: climate conditions impact the southerly distributed species and the availability of suitable habitat affects the northerly distributed species. The accuracy of the study models varied from fair to high (the area under curve values was 0.60-0.89) and revealed negative correlations with the relative occurrence area. In this study, we propose that any difference in model performance is more attributable to data characteristics than to a species' geographical characteristics.
Species distribution models (SDMs) are an important tool in biogeography and ecology and are widely used for both fundamental and applied research purposes. SDMs require spatially explicit information about species occurrence and environmental covariates to produce a set of rules that identify and scale the environmental space where the species was observed and that can further be used to predict the suitability of a site for the species. More spatially accurate data are increasingly available, and the number of publications on the influence of spatial inaccuracies on the performance of modelling procedures is growing exponentially. Three main sources of uncertainty are associated with the three elements of a predictive function: the dependent variable, the explanatory variables and the algorithm or function used to relate these two variables. In this study, we review how spatial uncertainties influence model accuracy and we propose some methodological issues in the application of SDMs with regard to the modelling of fundamental and realized niches of species. We distinguish two cases suitable for different types of spatial data accuracy. For modelling the realized distribution of a species, particularly for management and conservation purposes, we suggest using only accurate species occurrence data and large sample sizes. Appropriate data filtering and examination of the spatial autocorrelation in predictors should be a routine procedure to minimize the possible influence of positional uncertainty in species occurrence data. However, if the data are sparse, models of the potential distribution of species can be created using a relatively small sample size, and this can provide a generalized indication of the main regional drivers of the distribution patterns. By this means, field surveys can be targeted to discover unknown populations and species in poorly surveyed regions in order to improve the robustness of the data for later modelling of the realized distributions. Based on this review, we conclude that (1) with data that are currently available, studies performed at a resolution of 1-100 km(2) are useful for hypothesizing about the environmental conditions that limit the distribution of a species and (2) incorporating coarse resolution species occurrence data in a model, despite an increase in sample size, lowers model performance.
From the start, the discipline of MODERN Landscape Ecology has focused on the interaction between spatial pattern and ecological
processes. One area of focus has been to better understand how the patterns of environmental features, habitats, and resources
(e.g., gradients, patches) influence patterns of species distribution. Some of the earliest predictive modeling papers in
the journal Landscape Ecology dealt with predicting vegetation patterns based on topography (e.g., Bolstad et al. 1998; Ostendorm and Reynolds 1998). One
of the early tools developed for wildlife management was “Habitat Evaluation Procedures” (HEP) (Schamberger 1982; Urich and
Graham 1983; Mladenoff et al. 1995).