Limited climatic space for alternative ecosystem states in Africa.

One of the foundational premises of ecology is that climate determines ecosystems. This has been challenged by alternative ecosystem state models, which illustrate that internal ecosystem dynamics acting on the initial ecosystem state can overwhelm the influence of climate, and by observations suggesting that climate cannot reliably discriminate forest and savanna ecosystem types. Using a novel phytoclimatic transform, which estimates the ability of climate to support different types of plants, we show that climatic suitability for evergreen trees and C4 grasses are sufficient to discriminate between forest and savanna in Africa. Our findings reassert the dominant influence of climate on ecosystems and suggest that the role of feedbacks causing alternative ecosystem states is less prevalent than has been suggested.

Recognizability bias in citizen science photographs.

Citizen science and automated collection methods increasingly depend on image recognition to provide the amounts of observational data research and management needs. Recognition models, meanwhile, also require large amounts of data from these sources, creating a feedback loop between the methods and tools. Species that are harder to recognize, both for humans and machine learning algorithms, are likely to be under-reported, and thus be less prevalent in the training data. As a result, the feedback loop may hamper training mostly for species that already pose the greatest challenge. In this study, we trained recognition models for various taxa, and found evidence for a 'recognizability bias', where species that are more readily identified by humans and recognition models alike are more prevalent in the available image data. This pattern is present across multiple taxa, and does not appear to relate to differences in picture quality, biological traits or data collection metrics other than recognizability. This has implications for the expected performance of future models trained with more data, including such challenging species.

Insights into the quantification and reporting of model-related uncertainty across different disciplines.

Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties can lead to overstated conclusions with real-world impacts in diverse spheres, including conservation, epidemiology, climate science, and policy. Despite these potentially damaging consequences, we still know little about how different fields quantify and report uncertainty. We introduce the "sources of uncertainty" framework, using it to conduct a systematic audit of model-related uncertainty quantification from seven scientific fields, spanning the biological, physical, and political sciences. Our interdisciplinary audit shows no field fully considers all possible sources of uncertainty, but each has its own best practices alongside shared outstanding challenges. We make ten easy-to-implement recommendations to improve the consistency, completeness, and clarity of reporting on model-related uncertainty. These recommendations serve as a guide to best practices across scientific fields and expand our toolbox for high-quality research.

Rewriting results sections in the language of evidence.

Despite much criticism, black-or-white null-hypothesis significance testing with an arbitrary P-value cutoff still is the standard way to report scientific findings. One obstacle to progress is likely a lack of knowledge about suitable alternatives. Here, we suggest language of evidence that allows for a more nuanced approach to communicate scientific findings as a simple and intuitive alternative to statistical significance testing. We provide examples for rewriting results sections in research papers accordingly. Language of evidence has previously been suggested in medical statistics, and it is consistent with reporting approaches of international research networks, like the Intergovernmental Panel on Climate Change, for example. Instead of re-inventing the wheel, ecology and evolution might benefit from adopting some of the 'good practices' that exist in other fields.

Comment on "A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate", author Coro.

In this letter we present comments on the article "A global-scale ecological niche model to predict SARS-CoV-2 coronavirus" by Coro published in 2020.

Data Integration for Large-Scale Models of Species Distributions.

With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.

Integrating data from different survey types for population monitoring of an endangered species: the case of the Eld's deer.

Despite its value for conservation decision-making, we lack information on population abundances for most species. Because establishing large-scale monitoring schemes is rarely feasible, statistical methods that combine multiple data sources are promising approaches to maximize use of available information. We built a Bayesian hierarchical model that combined different survey data of the endangered Eld's deer in Shwesettaw Wildlife Sanctuary (SWS) in Myanmar and tested our approach in simulation experiments. We combined spatially-restricted line-transect abundance data with more spatially-extensive camera-trap occupancy data to enable estimation of the total deer abundance. The integrated model comprised an ecological model (common to both survey types, based on the equivalence between cloglog-transformed occurrence probability and log-transformed expected abundance) and separate observation models for each survey type. We estimated that the population size of Eld's deer in SWS is c. 1519 (1061-2114), suggesting it is the world's largest wild population. The simulations indicated that the potential benefits of combining data include increased precision and better sampling of the spatial variation in the environment, compared to separate analysis of each survey. Our analytical approach, which integrates the strengths of different survey methods, has widespread application for estimating species' abundances, especially in information-poor regions of the world.

Modelling seasonal dynamics, population stability, and pest control in Aedes japonicus japonicus (Diptera: Culicidae).

BACKGROUND: The invasive temperate mosquito Aedes japonicus japonicus is a potential vector for various infectious diseases and therefore a target of vector control measures. Even though established in Germany, it is unclear whether the species has already reached its full distribution potential. The possible range of the species, its annual population dynamics, the success of vector control measures and future expansions due to climate change still remain poorly understood. While numerous studies on occurrence have been conducted, they used mainly presence data from relatively few locations. In contrast, we used experimental life history data to model the dynamics of a continuous stage-structured population to infer potential seasonal densities and ask whether stable populations are likely to establish over a period of more than one year. In addition, we used climate change models to infer future ranges. Finally, we evaluated the effectiveness of various stage-specific vector control measures. RESULTS: Aedes j. japonicus has already established stable populations in the southwest and west of Germany. Our models predict a spread of Ae. j. japonicus beyond the currently observed range, but likely not much further eastwards under current climatic conditions. Climate change models, however, will expand this range substantially and higher annual densities can be expected. Applying vector control measures to oviposition, survival of eggs, larvae or adults showed that application of adulticides for 30 days between late spring and early autumn, while ambient temperatures are above 9 °C, can reduce population density by 75%. Continuous application of larvicide showed similar results in population reduction. Most importantly, we showed that with the consequent application of a mixed strategy, it should be possible to significantly reduce or even extinguish existing populations with reasonable effort. CONCLUSION: Our study provides valuable insights into the mechanisms concerning the establishment of stable populations in invasive species. In order to minimise the hazard to public health, we recommend vector control measures to be applied in 'high risk areas' which are predicted to allow establishment of stable populations to establish.

Standards for distribution models in biodiversity assessments.

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.

Disentangling synergistic disease dynamics: Implications for the viral biocontrol of rabbits.

European rabbits (Oryctolagus cuniculus) have been exposed to rabbit haemorrhagic disease virus (RHDV) and myxoma virus (MYXV) in their native and invasive ranges for decades. Yet, the long-term effects of these viruses on rabbit population dynamics remain poorly understood. In this context, we analysed 17 years of detailed capture-mark-recapture data (2000-2016) from Turretfield, South Australia, using a probabilistic state-space hierarchical modelling framework to estimate rabbit survival and epidemiological dynamics. While RHDV infection and disease-induced death were most prominent during annual epidemics in winter and spring, we found evidence for continuous infection of susceptible individuals with RHDV throughout the year. RHDV-susceptible rabbits had, on average, 25% lower monthly survival rates compared to immune individuals, while the average monthly force of infection in winter and spring was ~38%. These combined to result in an average infection-induced mortality rate of 69% in winter and spring. Individuals susceptible to MYXV and immune to RHDV had similar survival probabilities to those having survived infections from both viruses, whereas individuals susceptible to both RHDV and MYXV had higher survival probabilities than those susceptible to RHDV and immune to MYXV. This suggests that MYXV may reduce the future survival rates of individuals that endure initial MYXV infection. There was no evidence for long-term changes in disease-induced mortality and infection rates for either RHDV or MYXV. We conclude that continuous, year-round virus perpetuation (and perhaps heterogeneity in modes of transmission and infectious doses during and after epidemics) acts to reduce the efficiency of RHDV and MYXV as biocontrol agents of rabbits in their invasive range. However, if virulence can be maintained as relatively constant through time, RHDV and MYXV will likely continue realizing strong benefits as biocontrol agents.

Uncovering the drivers of host-associated microbiota with joint species distribution modelling.

In addition to the processes structuring free-living communities, host-associated microbiota are directly or indirectly shaped by the host. Therefore, microbiota data have a hierarchical structure where samples are nested under one or several variables representing host-specific factors, often spanning multiple levels of biological organization. Current statistical methods do not accommodate this hierarchical data structure and therefore cannot explicitly account for the effect of the host in structuring the microbiota. We introduce a novel extension of joint species distribution models (JSDMs) which can straightforwardly accommodate and discern between effects such as host phylogeny and traits, recorded covariates such as diet and collection site, among other ecological processes. Our proposed methodology includes powerful yet familiar outputs seen in community ecology overall, including (a) model-based ordination to visualize and quantify the main patterns in the data; (b) variance partitioning to assess how influential the included host-specific factors are in structuring the microbiota; and (c) co-occurrence networks to visualize microbe-to-microbe associations.

Disentangling the effects of multiple environmental drivers on population changes within communities.

The effects of different environmental drivers on the changes in species' population abundances can be difficult to disentangle as they often act simultaneously. Researchers have built statistical models that include environmental variables (such as annual temperature) or species attributes (such as a species' temperature preference), which are assumed to detect the impacts of specific drivers (such as climate change). However, these approaches are often applied separately or, if combined, not explicitly compared. We show the complementary insights gained by applying both these approaches to a community dataset on Danish terrestrial birds. We use our analysis to compare the relative importance of climate change and agricultural land-use change for the abundance changes within the community between 1983 and 2013. Population models were fitted to the community data of species' annual abundances with predictors comprising: species attributes (species' temperature and habitat preferences), environmental variables (climatic and agricultural land-use change variables) or both. Relationships between species' abundances and environmental variables were used to identify the drivers associated with average abundance changes of species in the community. Relationships between species' abundances and their attributes were used to understand the drivers causing interspecific variation in abundance changes. Warmer winters were positively associated with community-level abundances, and warm-adapted species had more positive abundance changes than cold-adapted ones. Agricultural land-use area was negatively associated with community-level abundances, and birds using a high proportion of meadow and habitat specialists had more negative abundance changes than birds using other habitats and habitat generalists. Effect sizes of environmental variables were larger for agricultural land-use change while those of species attributes were larger for climate change. The environmental data approach suggested that agricultural land-use change has decreased the average abundances of species in the community, affecting total community size while the species attribute-based approach suggested that climate change has caused variation in abundance among species, affecting community composition. Environmental variables and species attributes that are hypothesized to link to specific drivers can be used together to provide complementary information on the impacts of different drivers on communities.

Effects of changing climate on European stream invertebrate communities: A long-term data analysis.

Long-term observations on riverine benthic invertebrate communities enable assessments of the potential impacts of global change on stream ecosystems. Besides increasing average temperatures, many studies predict greater temperature extremes and intense precipitation events as a consequence of climate change. In this study we examined long-term observation data (10-32years) of 26 streams and rivers from four ecoregions in the European Long-Term Ecological Research (LTER) network, to investigate invertebrate community responses to changing climatic conditions. We used functional trait and multi-taxonomic analyses and combined examinations of general long-term changes in communities with detailed analyses of the impact of different climatic drivers (i.e., various temperature and precipitation variables) by focusing on the response of communities to climatic conditions of the previous year. Taxa and ecoregions differed substantially in their response to climate change conditions. We did not observe any trend of changes in total taxonomic richness or overall abundance over time or with increasing temperatures, which reflects a compensatory turnover in the composition of communities; sensitive Plecoptera decreased in response to warmer years and Ephemeroptera increased in northern regions. Invasive species increased with an increasing number of extreme days which also caused an apparent upstream community movement. The observed changes in functional feeding group diversity indicate that climate change may be associated with changes in trophic interactions within aquatic food webs. These findings highlight the vulnerability of riverine ecosystems to climate change and emphasize the need to further explore the interactive effects of climate change variables with other local stressors to develop appropriate conservation measures.

Cross-taxa generalities in the relationship between population abundance and ambient temperatures.

Identifying patterns in the effects of temperature on species' population abundances could help develop a general framework for predicting the consequences of climate change across different communities and realms. We used long-term population time series data from terrestrial, freshwater, and marine species communities within central Europe to compare the effects of temperature on abundance across a broad range of taxonomic groups. We asked whether there was an average relationship between temperatures in different seasons and annual abundances of species in a community, and whether species attributes (temperature range of distribution, range size, habitat breadth, dispersal ability, body size, and lifespan) explained interspecific variation in the relationship between temperature and abundance. We found that, on average, warmer winter temperatures were associated with greater abundances in terrestrial communities (ground beetles, spiders, and birds) but not always in aquatic communities (freshwater and marine invertebrates and fish). The abundances of species with large geographical ranges, larger body sizes, and longer lifespans tended to be less related to temperature. Our results suggest that climate change may have, in general, positive effects on species' abundances within many terrestrial communities in central Europe while the effects are less predictable in aquatic communities.

Experience drives innovation of new migration patterns of whooping cranes in response to global change.

Anthropogenic changes in climate and land use are driving changes in migration patterns of birds worldwide. Spatial changes in migration have been related to long-term temperature trends, but the intrinsic mechanisms by which migratory species adapt to environmental change remain largely unexplored. We show that, for a long-lived social species, older birds with more experience are critical for innovating new migration behaviours. Groups containing older, more experienced individuals establish new overwintering sites closer to the breeding grounds, leading to a rapid population-level shift in migration patterns. Furthermore, these new overwintering sites are in areas where changes in climate have increased temperatures and where food availability from agriculture is high, creating favourable conditions for overwintering. Our results reveal that the age structure of populations is critical for the behavioural mechanisms that allow species to adapt to global change, particularly for long-lived animals, where changes in behaviour can occur faster than evolution.

Millions of reads, thousands of taxa: microbial community structure and associations analyzed via marker genes.

With high-throughput sequencing (HTS), we are able to explore the hidden world of microscopic organisms to an unpre-cedented level. The fast development of molecular technology and statistical methods means that microbial ecologists must keep their toolkits updated. Here, we review and evaluate some of the more widely adopted and emerging techniques for analysis of diversity and community composition, and the inference of species interactions from co-occurrence data generated by HTS of marker genes. We emphasize the importance of observational biases and statistical properties of the data and methods. The aim of the review is to critically discuss the advantages and disadvantages of established and emerging statistical methods, and to contribute to the integration of HTS-based marker gene data into community ecology.