Petra Šímová

Influence of positional accuracy, sample size and scale on modelling species distributions: a review

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 km2 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.

The common cuckoo is an effective indicator of high bird species richness in Asia and Europe

Common cuckoo Cuculus canorus is a charismatic bird species with a dominant presence in human culture: from folklore legends to nowadays there is evidence of cuckoos being a prime candidate as a surrogate of bird diversity. Recent studies demonstrated that the cuckoo can predict hotspots of taxonomic diversity and functional diversity of bird communities in European countries. In this study, we demonstrated that the cuckoo is an excellent bioindicator at multi-spatial scale, extending cuckoo surrogacy from Europe to Asia. Even using three different survey methods (transect, square, point counts), comparing the new findings with results of our research in Europe, sites where the cuckoo is present were characterized by greater species richness, while the cuckoo was absent from sites with low species richness. The goodness of fit of models based on point counts ranged between 71 and 92%. Furthermore, the cuckoo population trend mirrors the average population trend and climate suitability of overall bird communities in Europe. The common cuckoo is therefore a suitable intercontinental bioindicator of hotspots of bird richness, even under climate change scenarios or in areas where the species co-occurs with other cuckoo species, opening a new avenue for standardized citizen science on bird biodiversity surveys worldwide.

Refugial role of urbanized areas and colonization potential for declining Crested Lark (Galerida cristata) populations in the Czech Republic, Central Europe

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 twentieth 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 the 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.ZusammenfassungDie Rolle verstädterter Bereiche als Zufluchtsgebiete und das Besiedlungspotenzial für Populationen der Haubenlerche (Galerida cristata) in Tschechien, Mitteleuropa Eine städtische Umwelt wird nur selten als exklusives Zufluchtsgebiet für rapide abnehmende Vogelarten betrachtet. Die Haubenlerche (Galerida cristata) ist eine Art in Mitteleuropa, deren Synanthropisierung in der zweiten Hälfte des 20. Jahrhunderts aufgrund dramatischer Veränderungen von Landwirtschaft und Landschaftsstruktur begann. Wir analysieren, inwieweit Veränderungen in diesen Verbreitungsmustern Landschaftsstrukturen widerspiegeln, und zeigen das Besiedlungspotenzial von benachbarten Zufluchtsgebieten auf. Wir haben Daten aus dem tschechischen Brutvogelatlas für zwei Zeiträume, 1985–1989 und 2001–2003, verwendet, um Veränderungen in den Verbreitungsmustern von Haubenlerchen in zwei Landesteilen, Böhmen und Mähren, zu modellieren. Die Erfassungseinheiten bestanden aus Kartierungsquadraten, und die binomiale An- oder Abwesenheit der Art wurde benutzt, um die Veränderungsmuster mit Hilfe generalisierter linearer Modelle zu modellieren, mit Landschaftsstruktur, demographischen Daten und dominanten Habitateigenschaften als Vorhersagevariablen. Trotz ähnlicher Merkmale von Landschaftsstrukturen in Böhmen und Mähren unterschieden sich Haubenlerchen deutlich in ihren Aussterbemustern. Im isolierten Böhmen waren die übriggebliebenen Subpopulationen auf frühe Sukzessionsflächen um städtische Gewerbegebiete herum begrenzt. In Mähren schien lediglich die Höhenlage mit dem Verschwinden der Art in Verbindung zu stehen. Mähren ist im Süden mit der Pannonischen Tiefebene verbunden, wo Haubenlerchenpopulationen stabil sind oder zunehmen, was wir für die signifikant höheren Anzahlen neu besiedelter Quadrate verantwortlich machen. Unsere Ergebnisse deuten darauf hin, dass die stärker isolierte böhmische Population eher Gefahr läuft auszusterben als die Population in Mähren mit ihrem größeren Besiedlungspotenzial. Diese Studie stellt ein seltenes Beispiel für Stadtzonen dar, die als Zufluchtsgebiete für eine Vogelart dienen, die auf frühe Sukzessionsflächen angewiesen ist.

Identifying HNV Areas Using Geographic Information Systems and Landscape Metrics

This chapter describes some spatial tools suitable to map and identify High Nature Value (HNV) farmlands, applying geographical information systems (GIS). It provides a list and definitions of some of the most adequate landscape metrics that can be used to identify HNV areas from maps, as well as the main issues that operators can find when handling GIS.

Impact of input data (in)accuracy on overestimation of visible area in digital viewshed models

Viewshed analysis is a GIS tool in standard use for more than two decades to perform numerous scientific and practical tasks. The reliability of the resulting viewshed model depends on the computational algorithm and the quality of the input digital surface model (DSM). Although many studies have dealt with improving viewshed algorithms, only a few studies have focused on the effect of the spatial accuracy of input data. Here, we compare simple binary viewshed models based on DSMs having varying levels of detail with viewshed models created using LiDAR DSM. The compared DSMs were calculated as the sums of digital terrain models (DTMs) and layers of forests and buildings with expertly assigned heights. Both elevation data and the visibility obstacle layers were prepared using digital vector maps differing in scale (1:5,000, 1:25,000, and 1:500,000) as well as using a combination of a LiDAR DTM with objects vectorized on an orthophotomap. All analyses were performed for 104 sample locations of 5 km2, covering areas from lowlands to mountains and including farmlands as well as afforested landscapes. We worked with two observer point heights, the first (1.8 m) simulating observation by a person standing on the ground and the second (80 m) as observation from high structures such as wind turbines, and with five estimates of forest heights (15, 20, 25, 30, and 35 m). At all height estimations, all of the vector-based DSMs used resulted in overestimations of visible areas considerably greater than those from the LiDAR DSM. In comparison to the effect from input data scale, the effect from object height estimation was shown to be secondary.

Fine scale waterbody data improve prediction of waterbird occurrence despite coarse species data

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.