# Carsten F. Dormann's research while affiliated with University of Freiburg and other places

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## Publications (352)

In our article ‘European agroforestry has no unequivocal effect on biodiversity: a time-cumulative meta-analysis’ (BMC Ecology and Evolution, 2021) we synthesize the effect of agroforestry on biodiversity. Boinot et al. (BMC Ecology and Evolution, 2022) criticise our approach arguing that our definitions of agroforestry and biodiversity are too nar...

This study investigates wolf (Canis lupus) dietary preferences and their geographical variation by calculating frequency of occurrence and biomass consumption in three areas in the north-western Iberian Peninsula that differ in terms of the type and abundance of potential prey. Wolf dietary preferences were expected to be related to the availabilit...

Deep learning and particularly Convolutional Neural Networks (CNN) in concert with remote sensing are becoming standard analytical tools in the geosciences. A series of studies has presented the seemingly outstanding performance of CNN for predictive modelling. However, the predictive performance of such models is commonly estimated using random cr...

Density is a key trait of populations and an essential parameter in ecological research, wildlife conservation and management. Several models have been developed to estimate population density based on camera trapping data, including the random encounter model (REM) and camera trap distance sampling (CTDS). Both models need to account for variation...

Population density is a key parameter in ecology and conservation, and estimates of population density are required for a wide variety of applications in fundamental and applied ecology. Yet, in terrestrial mammals these data are available for only a minority of species, and their availability is taxonomically and geographically biased. Here, we pr...

The crisis facing Africa’s elephant populations is a notorious example of ongoing wildlife declines caused by illegal harvesting. Targeted conservation interventions require detailed knowledge about changes in population sizes and the effect of illegal activities. However, accurately quantifying poaching intensity is a difficult task: commonly calc...

Avila, I.C., Kaschner, K & C. Dormann (2021) Threatened marine mammal species: which ecological traits make them more vulnerable? Abstract ID 813, 30th International Congress for Conservation Biology ICCB 2021. 12-16 December 2021, Kigali, Rwanda.

Background
Agroforestry is a production system combining trees with crops or livestock. It has the potential to increase biodiversity in relation to single-use systems, such as pastures or cropland, by providing a higher habitat heterogeneity. In a literature review and subsequent meta-analysis, we investigated the relationship between biodiversity...

Bayesian inference has become an important framework for calibrating complex ecological and environmental models. Markov-Chain Monte Carlo (MCMC) algorithms are the methodological backbone of this framework, but they are not easily parallelizable and can thus not make optimal use of modern computer architectures. A possible solution is the use of S...

Humans have transformed most landscapes across the globe, forcing other species to adapt in order to persist in increasingly anthropogenic landscapes. Wide-ranging solitary species, such as wild felids, struggle particularly in such landscapes. Conservation planning and management for their long-term persistence critically depends on understanding...

Temporal variability of plant–pollinator interactions is important for fully understanding the structure, function, and stability of plant–pollinator networks, but most network studies so far have ignored within-day dynamics. Strong diel dynamics (e.g., a regular daily cycle) were found for networks with Cichorieae, which typically close their flow...

In the field of electronic roundwood measurement, 3D-laser systems are becoming more
and more important for the determination of log volume and quality. Especially the outer shape of the logs is of particular importance for the yield and should therefore be described in as much detail as possible. This study focuses on the parameter curvature and c...

In recent decades, European temperate forests have repeatedly suffered from severe droughts. Drought‐weakened forests have often become more susceptible to pest outbreaks such as bark beetle infestations. Tree species diversity is expected to increase resistance to drought and pests, but evidence for a positive tree diversity effect on insect pest...

Aim: Nestedness is a common pattern in metacommunities and interaction networks, whose causes are still discussed. Nestedness inference is challenging because, beyond calculating an index, we need to compare observed values with values generated with a null model. There are different null models and the choice between them affects test outcomes. Fu...

Ecological interactions link species in networks. Loss of species from or introduction of new species into an existing network may have substantial effects for interaction patterns. Predicting changes in interaction frequency while allowing for rewiring of existing interactions-and hence estimating the consequences of community compositional change...

Strong declines of grassland species diversity in small and isolated grassland patches have been observed at local and landscape scales. Here, we study how plant–herbivore interaction webs and habitat specialisation of leafhopper communities change with the size of calcareous grassland fragments and landscape connectivity. We surveyed leafhoppers a...

Recent studies hint at consistent declines of insect abundance across taxa. However, detailed data from long-term surveys are rare in ecological studies, and yet are required in order to accurately infer trends and their causes. In the following, we analyse a dataset from pitfall traps sampled at a monthly resolution over a 33-year period (1979–201...

Most studies of plant–animal mutualistic networks have come from a temporally static perspective. This approach has revealed general patterns in network structure, but limits our ability to understand the ecological and evolutionary processes that shape these networks and to predict the consequences of natural and human‐driven disturbance on specie...

Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready‐to‐use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservat...

At the end of this chapter ……you will be able to fit and visualise regression models with two predictors.…you will have two ideas for how to deal with correlated predictors: Principal component analysis and cluster analysis.…you will be able to calculate a 2-way ANOVA by hand, if necessary.…you will be able to execute a step-wise simplification of...

At the end of this chapter ……you will have learned the different steps that are necessary to do after formulating the model.…you will know how predictors should be distributed.…you will know that there are more than one type of residual, and that these can provide important information about the model fit.…you will be able to test for “outliers” an...

At the end of this (long) chapter ……you will know the t-test in its many different variations.…You will understand that the idea of variance analysis is to divide the total variance into explainable and unexplained variance.…you will know the F-test for calculating the significance of a variance analysis.…you will understand the close relationship...

At the end of this chapter ……you will be able to visualise correlations and quantify their strength using different measures.…you will be able to implement a χ2-test of association for two categorical variables, and if its assumptions are not met, switch to the Fisher’s sign-rank test. you will be able to visualise correlations and quantify their s...

At the end of this chapter……it should be clear to you that data collection should be planned in advance and you should know what an ideal data sheet looks like.…you will be able to read data into R.…you will have the skills to transform raw data in R into a histogram.…you will be able to calculate the sample statistics we learned in Chap. 1 in R.…y...

At the end of this chapter……you will know what a distribution is and what properties it has.…you will understand the concept of likelihood.…you will understand how to use “maximum likelihood” to achieve the best possible fit of a distribution for empirical data.…you will know a few important distributions, such as the normal distribution, the Poiss...

At the end of this chapter……a GLM with a single predictor will flow easily from your fingertips.…you will be able to interpret the output of the summary(glm(.)) function.…you will be capable of drawing the regression line and confidence interval in a plot of data points from a glm-analysis.…you will be able to calculate a GLM using an optimisation...

By the end of this (long) chapter ……the difference between correlation and regression should be obvious. Specifically, you will recognise that with a regression there is always an x and a y, that is, a variable that explains and one that responds. A predictor and an answer—an independent and a dependent variable. The dependent variable is always sh...

At the end of this chapter ……you will understand how a factorial design leads to higher sensitivity in the analysis than two separate experiments.…you will be able to calculate a two-way ANOVA by hand if necessary.…you will know what a statistical interaction is and how it should be interpreted.…you will be familiar with principal component analysi...

At the end of this chapter …… you will have been introduced to around 20 continuous and discrete distribution functions and will be able to graphically display each of these in R.… you will know what the acronym ECDF stands for and can even plot it in R.… you will be able to use the Kolmogorov-Smirnov test to formally compare samples and multiple c...

At the end of this chapter……you will be able to execute the most important steps of model diagnostics.…you will no longer be impressed simply by a good-looking fit: the residuals and results of alternative model structures are also important.…you will see that the graphic representation of the relationship is just as important as plotting the data...

At the end of this chapter ……you will be able to calculate different t-test variants with R.…you will be able to calculate a simple ANOVA in R.…you will be able to switch back and forth between glm and aov and retrieve the information that is most relevant for you.…you will be able to calculate the corresponding P-value for F-values.…you will know...

At the end of this chapter ……you should know what a sample is.…the terms mean, median, standard deviation, variance, and standard error should be so familiar to you that you can recall their formulas from memory. You should also be comfortable with the concepts of skew, kurtosis and interquartile range.…you should be familiar with the histogram as...

This book set the foundations for the most common and widespread type of statistics: parametric statistics. It builds on assumptions about the distribution of the response variable, allowing us to leverage the framework of maximum likelihood. Of course, statistics is much wider, and in the following I would like to offer a few pointers towards othe...

At the end of this chapter……the idea of a correlation will be familiar to you.…you will be able to use the Pearson’s r as a measurement for interpreting the strength of a correlation.…you will know when you need to use Spearman’s ρ or Kendall’s τ instead.…you will have comprehended the principle of association with the help of the χ2-test. the idea...

At the end of this chapter ……you will know the basics of designing an experiment.…you will have gotten to know the most important experimental designs.…The terms “pseudoreplication”, “control” and “random effect” will mean something to you.…you will know that Stuart Hurlbert’s publication from 1984 is required reading. you will know the basics of d...

At the end of this chapter ……you will have an idea of the role that hypotheses play in scientific research.…you will know that you can not verify a hypothesis, but rather can only falsify, and that for this reason, the falsified null hypothesis has an important role in statistical hypothesis testing!…you will know what type 1 and type 2 errors are...

Description of the subject. This study evaluates the application of Boosted Regression Trees (BRT) for predicting beech dominant height in the Hyrcanian forests of Iran, inscribed as a UNESCO’s World Heritage due to its remarkable biodiversity.
Objectives. It is widely accepted that tree growth can be influenced by a wide variety of factors such a...

Mapping aboveground forest biomass is central for assessing the global carbon balance. However, current large-scale maps show strong disparities, despite good validation statistics of their underlying models. Here, we attribute this contradiction to a flaw in the validation methods, which ignore spatial autocorrelation (SAC) in data, leading to ove...

Retention forestry intends to promote biodiversity by retaining deadwood and tree-related microhabitats. Simultaneously, production forests undergo major structural changes by conversion into near-natural forests. As insect biomass is declining, it is important to understand how insect communities respond to management-related changes in forest str...

The importance of using evidence in decision-making is frequently highlighted in policy reports and scientific papers. However, subjective judgments of the reliability of environmental evidence vary widely, and large-scale systematic searches for evidence are only common for climate-related topics. In the medical field, evidence-based guidelines ar...

Background: Agroforestry is a production system combining trees with crops or livestock. It has the potential to increase biodiversity in relation to single-use systems, such as pastures or conventional agriculture, by providing a higher habitat heterogeneity. In a literature review and subsequent meta-analysis, we investigated the relationship bet...

Background
Temperate forest understorey vegetation poses an excellent study system to investigate whether increases in resource availability lead to an increase in plant species richness. Most sunlight is absorbed by the species-poor tree canopy, making the much more species-rich understorey species inhabit a severely resource-limited habitat. Addi...

Background: Understanding the relationships between forest structure, in particular attainable height, and the environment is important for sustainable forest management. Similarly, modeling structural attributes improve our understanding of forest growth dynamics and may identify key drivers of long-term changes in the forest ecosystem. Due to the...

Background: Species distribution models are commonly used tools to describe diversity patterns and support conservation
measures. There is a wide range of approaches to developing SDMs, each highlighting different characteristics
of both the data and the ecology of the species or assemblages represented by the data. Yet, signals of species
co-occur...

Background: Spatial conservation prioritisation (SCP) is a set of computational tools designed to support the efficient spatial allocation of priority areas for conservation actions, but it is subject to many sources of uncertainty which should be accounted for during the prioritisation process. We quantified the sensitivity of an SCP application (...

Supporting information for the publication:
El-Gabbas et al. (2020) Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using multiple taxa - BMC Ecology.
DOI: https://www.doi.org/10.1186/s12898-020-00305-7
-----------------------------------------------
Background: Spatial conservation prioritisation...

Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready-to-use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservat...

The study of mutualistic interaction networks has led to valuable insights into ecological and evolutionary processes. However, our understanding of network structure may depend upon the temporal scale at which we sample and analyze network data. To date, we lack a comprehensive assessment of the temporal scale‐dependence of network structure acros...

Wild boar space use and the determinants of its variation are crucial information for understanding wild boar Sus scrofa (L.) ecology and for wild boar management. Wild boar space use has mostly been investigated on broad temporal scales such as annual or seasonal home ranges. Ranges can vary depending on the observed timespan and on the temporal s...

1. Conflict between humans and large carnivores hinders carnivore conservation worldwide. Livestock depredations by large carnivores is the main cause of conflict, triggering poaching and retaliatory killings by humans. Resolving this conflict requires an understanding of the factors that cause large carnivores to select livestock over wild prey. I...

Camera traps have become an important tool in wildlife monitoring. However, an issue in interpreting their data in statistical analyses of population densities, demography or behaviour is that the probability of detecting the target animals and their behaviours may vary depending on environmental and methodologi-cal factors. A specific problem is t...

Aim
Predictions from statistical models may be uncalibrated, meaning that the predicted values do not have the nominal coverage probability. This is easiest seen with probability predictions in machine‐learning classification, including the common species occurrence probabilities. Here, a predicted probability of, say, .7 should indicate that out o...

Retention forestry, which retains a portion of the original stand at the time of harvesting to maintain continuity of structural and compositional diversity, has been originally developed to mitigate the impacts of clear‐cutting. Retention of habitat trees and deadwood has since become common practice also in continuous‐cover forests of Central Eur...

Background:
Wild boars (Sus scrofa L.) are globally widely distributed, and their populations have increased in Europe during recent decades. Encounters between humans and wild boars are rare because of the predominantly nocturnal lifestyle of the latter, and wild boar management by hunting is a challenging task. Animal activity patterns are impor...

Environmental Data Analysis is an introductory statistics textbook for environmental science. It covers descriptive, inferential and predictive statistics, centred on the Generalized Linear Model. The key idea behind this book is to approach statistical analyses from the perspective of maximum likelihood, essentially treating most analyses as (mult...

During the austral winter, G-stock humpback whales, Megaptera novaeangliae, migrate to the Tropical Eastern Pacific to breed. To analyse if the whale migration times have changed over time, we analysed 31 years (1988-2018) of arrival and departure times to Gorgona National Park, Colombia, an important breeding site. During this period, whales have...

Through changes in policy and practice, the inherent intent of the ecosystem services (ES) concept is to safeguard ecosystems for human wellbeing. While impact is intrinsic to the concept, little is known about how and whether ES science leads to impact. Evidence of impact is needed. Given the lack of consensus on what constitutes impact, we differ...

Ecosystem service research is high on the policy agenda. Strategies to synthesize individual success stories and derive generalized results to provide guidance for policymakers and stakeholder is central to many science-policy initiatives, such as Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services and The Economics of...

Managing agricultural landscapes to support biodiversity and ecosystem services is a key aim of a sustainable agriculture. However, how the spatial arrangement of crop fields and other habitats in landscapes impacts arthropods and their functions is poorly known. Synthesising data from 49 studies (1515 landscapes) across Europe, we examined effects...

Nestedness and modularity have been recurrently observed in species interaction networks. Some studies argue that those topologies result from selection against unstable networks, while others propose that they likely emerge from processes driving the interactions between pairs of species. Here we present a model that simulates the evolution of con...

Poaching is contributing to rapid declines in elephant populations across Africa. Following high-profile changes in the political environment, the overall number of illegally killed elephants in Africa seems to be falling, but to evaluate potential conservation interventions we must understand the processes driving poaching rates at local and globa...

Model transferability is an emerging and important branch of predictive science that has grown primarily from a need to produce ecological forecasts in the face of widespread data deficiency and escalating environmental novelty. In our recent article in Trends in Ecology and Evolution, we outlined some of the major roadblocks that currently undermi...

Individual maps for female orangutans Barcelona (12 years old at time of release, 13 relocations, top left), Chaka (13 years old at time of release, 48 relocations, top right), Rimbani (13 years old at time of release, 43 relocations, bottom left) and Delavita (14 years old at time of release, 7 relocations, bottom right). Red dots represent locati...

Individual maps for female orangutans Suri (5 years old at time of release, 8 relocations, top left), Miriam (6 years old at time of release, 7 relocations, top right), Willy (6 years old at time of release, 26 relocations, bottom left) and Sakdiah (11 years old at time of release, 51 relocations, bottom right). Red dots represent locations where o...

Individual map for female orangutan Sasha (21 years old at time of release, 43 relocations, top left) and male orangutans Julius (5 years old at time of release, 28 relocations, top right), Ken (5 years old at time of release, 82 relocations, bottom left) and Jarot (6 years old at time of release, 20 relocations, bottom right). Red dots represent l...

Individual maps for male orangutans Evan (9 years old at time of release, 27 relocations, top left), Sun_Go_Kong (10 years old at time of release, 4 relocations, top right), Vewe (10 years old at time of release, 93 relocations, bottom left) and Alex (12 years old at time of release, 100 relocations, bottom right). Red dots represent locations wher...

Individual maps for male orangutans Ongki (6 years old at time of release, 5 relocations, top left), Lindung (7 years old at time of release, 65 relocations, top right), Mambo (7 years old at time of release, 48 relocations, bottom left) and Semeru (7 years old at time of release, 7 relocations, bottom right). Red dots represent locations where ora...

Individual maps for male orangutans JunaDesky (12 years old at time of release, 3 relocations, top left), Nyoman (12 years old at time of release, 88 relocations, top right), Windas (12 years old at time of release, 7 relocations, bottom left) and Beckham (13 years old at time of release, 21 relocations, bottom right). Red dots represent locations...

Relative probability of selection in 32 orangutans (n = 13 females, n = 19 males) as a function of the number of days spent in rehabilitation prior to their release (seven different scenarios) interacted with the distance to the FZS station (first row, in meters), the distance to the nearest river (second row, in meters), and elevation (third row,...

Individual maps for male orangutans Joko (14 years old at time of release, 31 relocations, top left), Rencong (14 years old at time of release, 4 relocations, top right), Abel (16 years old at time of release, 71 relocations, bottom left) and Mamut (18 years old at time of release, 16 relocations, bottom right). Red dots represent locations where o...