Search

Aim: Species distribution models (SDMs) are widely used to make predictions on how species distributions may change as a response to climatic change. To assess the reliability of those predictions, they need to be critically validated with respect to what they are used for. While ecologists are typically interested in how and where distributions will change, we argue that SDMs have seldom been evaluated in terms of their capacity to predict such change. Instead, typical retrospective validation methods estimate model's ability to predict to only one static time in future. Here, we apply two validation methods, one that predicts and evaluates a static pattern, while the other measures change and compare their estimates of predictive performance. Location: Fennoscandia.Methods: We applied a joint SDM to model the distributions of 120 bird species in four model validation settings. We trained models with a dataset from 1975 to 1999 and predicted species' future occurrence and abundance in two ways: for one static time period (2013- 2016, "static validation') and for a change between two time periods (difference between 1996- 1999 and 2013- 2016, "change validation'). We then measured predictive performance using correlation between predicted and observed values. We also related predictive performance to species traits. Results: Even though static validation method evaluated predictive performance as good, change method indicated very poor performance. Predictive performance was not strongly related to any trait.Main Conclusions: Static validation method might overestimate predictive performance by not revealing the model's inability to predict change events. If species' distributions remain mostly stable, then even an unfit model can predict the near future well due to temporal autocorrelation. We urge caution when working with forecasts of changes in spatial patterns of species occupancy or abundance, even for SDMs that are based on time series datasets unless they are critically validated for forecasting such change.

1. Climate, topography and the 3D structure of forests are major drivers affecting local species communities. However, little is known about how the specific functional traits of saproxylic (wood-living) beetles, involved in the recycling of wood, might be affected by those environmental characteristics. 2. Here, we combine ecological and morphological traits available for saproxylic beetles and airborne laser scanning (ALS) data in Bayesian trait-based joint species distribution models to study how traits drive the distributions of more than 230 species in temperate forests of Europe. 3. We found that elevation (as a proxy for temperature and precipitation) and the proportion of conifers played important roles in species occurrences while variables related to habitat heterogeneity and forest complexity were less relevant. Furthermore, we showed that local communities were shaped by environmental variation primarily through their ecological traits whereas morphological traits were involved only marginally. As predicted, ecological traits influenced species' responses to forest structure, and to other environmental variation, with canopy niche, wood decay niche and host preference as the most important ecological traits. Conversely, no links between morphological traits and environmental characteristics were observed. Both models, however, revealed strong phylogenetic signal in species' response to environmental characteristics. 4. These findings imply that alterations of climate and tree species composition have the potential to alter saproxylic beetle communities in temperate forests. Additionally, ecological traits help explain species' responses to environmental characteristics and thus should prove useful in predicting their responses to future change. It remains challenging, however, to link simple morphological traits to species' complex ecological niches., Functional Ecology, 37 (1), ISSN:0269-8463, ISSN:1365-2435

Theoretical and applied cancer studies that use individual-based models (IBMs) have been limited by the lack of a mathematical formulation that enables rigorous analysis of these models. However, spatial cumulant models (SCMs), which have arisen from theoretical ecology, describe population dynamics generated by a specific family of IBMs, namely spatio-temporal point processes (STPPs). SCMs are spatially resolved population models formulated by a system of differential equations that approximate the dynamics of two STPP-generated summary statistics: first-order spatial cumulants (densities), and second-order spatial cumulants (spatial covariances).We exemplify how SCMs can be used in mathematical oncology by modelling theoretical cancer cell populations comprising interacting growth factor-producing and non-producing cells. To formulate model equations, we use computational tools that enable the generation of STPPs, SCMs and mean-field population models (MFPMs) from user-defined model descriptions (Cornell et al., 2019). To calculate and compare STPP, SCM and MFPM-generated summary statistics, we develop an application-agnostic computational pipeline. Our results demonstrate that SCMs can capture STPP-generated population density dynamics, even when MFPMs fail to do so. From both MFPM and SCM equations, we derive treatment-induced death rates required to achieve non-growing cell populations. When testing these treatment strategies in STPP-generated cell populations, our results demonstrate that SCM-informed strategies outperform MFPM-informed strategies in terms of inhibiting population growths. We thus demonstrate that SCMs provide a new framework in which to study cell-cell interactions, and can be used to describe and perturb STPP-generated cell population dynamics. We, therefore, argue that SCMs can be used to increase IBMs’ applicability in cancer research.Statements and DeclarationsThe authors have no competing interests to declare that are relevant to the content of this article.

Publisher Copyright: © 2023 The Authors The urbanization process leads to changes in bird communities’ taxonomic and functional compositions. Highly urbanized areas generally exhibit a reduced number of bird species sharing few functional traits. However, most urban bird studies focused on vegetation patches in temperate cities. In this study, we investigate how urban environmental attributes – noise, height of buildings, and urban vegetation characteristics – modulate species occurrences and the distribution of functional traits across the streetscape of a tropical metropolis. We predicted diverse trait-environment relationships, but that highly urbanized contexts (e.g., noisy streets with tall buildings) would be mostly occupied b,y a lower number of species sharing generalist traits. We also predicted to observe streets with similar community composition (profiles) shaped by environmental conditions and interspecific interactions. We applied hierarchical modelling of species communities as a flexible framework for analysis of community data. We observed that, increased noise exposure and reduced green cover were negatively related with species richness due to their negative relationships with most species’ occurrences. On the other hand, larger number of trees and higher proportion of green cover presented mostly positive relationships with occurrences, and thus with species richness. Throughout our streetscape, community composition was highly heterogeneous and similar conditions led to similar profiles. For example, noisy streets may favor the presence of omnivorous and large-bodied species, while wooded streets may allow for the presence of smaller-bodied forest specialist species that exploit lower vegetation strata. Our results indicate that streetscapes may have the potential to harbor functionally and taxonomically diverse bird communities.

1. Traits related to reproduction and dispersal drive the assembly and dynamics of species communities and can explain and predict how species respond to habitat loss and fragmentation and to the changing climate. For fungi, such links remain poorly known. 2. We examine how spore production rate, a key demographic trait, is influenced by the interaction between environmental conditions and species traits. We monitored the spore production of 97 wood-inhabiting fungal species on 107 decaying logs for two years and analyzed the data with a hierarchical community model. 3. Our analysis demonstrates clear species differences in seasonal patterns, with spring and summer release dominating in perennial species, contrary to the commonly held view of autumn as the primary ?mushroom season?. Many species follow a diurnal pattern with a higher spore release rate during the night. Such patterns in release timing have important implications for dispersal, as shown by recent model simulations. 4. The overall level of spore release was negatively correlated with spore size, providing new evidence that fungi face the classic trade-off of investing either in the number or size of offspring. 5. We found that different species within the functional group of wood-inhabiting fungi display alternative strategies in spore release timing and along the trade-off between offspring size and number. Linking our findings to previously reported correlations between spore size and other traits, we propose a new conceptualization of life history strategies in wood-inhabiting fungi, with implications for species? ability to survive the ongoing biodiversity crisis.

Aim We use lake phytoplankton community data to quantify the spatio-temporal and scale-dependent impacts of eutrophication, land-use and climate change on species niches and community assembly processes while accounting for species traits and phylogenetic constraints. Location Finland. Time period 1977–2017. Major taxa Phytoplankton. Methods We use hierarchical modelling of species communities (HMSC) to model metacommunity trajectories at 853 lakes over four decades of environmental change, including a hierarchical spatial structure to account for scale-dependent processes. Using a “region of common profile” approach, we evaluate compositional changes of species communities and trait profiles and investigate their temporal development. Results We demonstrate the emergence of novel and widespread community composition clusters in previously more compositionally homogeneous communities, with cluster-specific community trait profiles, indicating functional differences. A strong phylogenetic signal of species responses to the environment implies similar responses among closely related taxa. Community cluster-specific species prevalence indicates lower taxonomic dispersion within the current dominant clusters compared with the historically dominant cluster and an overall higher prevalence of smaller species sizes within communities. Our findings denote profound spatio-temporal structuring of species co-occurrence patterns and highlight functional differences of lake phytoplankton communities. Main conclusions Diverging community trajectories have led to a nationwide reshuffling of lake phytoplankton communities. At regional and national scales, lakes are not single entities but metacommunity hubs in an interconnected waterscape. The assembly mechanisms of phytoplankton communities are strongly structured by spatio-temporal dynamics, which have led to novel community types, but only a minor part of this reshuffling could be linked to temporal environmental change.

Fragmentation of habitat, for example by intensive agricultural practices, can be detrimental to local biodiversity. However, it often remains unclear whether such biodiversity declines are caused by loss of habitat area or increased fragmentation, and how habitat quality factors into it. In our study system, vegetated vineyards are typically small, and isolated from one another, potentially limiting the distribution and dispersal of organisms. In a full-factorial experiment of a priori selected vegetated vineyard patches of differing size and fragmentation, we aimed to disentangle the effects of habitat area (area of vegetated vineyards), habitat fragmentation (number of vegetated vineyards per 100 ha) and field-scale ground vegetation density on ground beetle, leafhopper and wild bee communities using a combined framework of multiscale and multispecies modelling (Hierarchical Model of Species Communities). We demonstrate variable effects of habitat area, fragmentation and local ground vegetation density on the three insect groups: Increased habitat area at fine scales favours higher species richness of leafhoppers, while local vegetation density boosts species richness of both leafhoppers and ground beetles, whereas no community-level responses were detected for wild bees. We conclude that increased ground vegetation density at both field and landscape scales (i.e. higher habitat area) favours more diverse and abundant insect communities, while fragmentation effects are highly variable and species specific. In addition, our results highlight that mainly ground beetles and leafhoppers will benefit from simple ground greening measures in vineyards, while for wild bees environmental factors other than the ones tested here may drive community structure. We recommend increasing the number and area of vegetated vineyards (even at small spatial scales) requiring more nature-friendly farming practices especially regarding a reduction or renunciation from herbicide applications, while the within-field vegetation density should optimally be intermediate or high to favour a diverse insect community. peerReviewed

Species co-occurrences in local communities can arise independent or dependent on species’ niches. However, the role of niche-dependent processes has not been thoroughly deciphered when generalized to biogeographical scales, probably due to combined shortcomings of data and methodology. Here, we explored the influence of environmental filtering and limiting similarity, as well as biogeographical processes that relate to the assembly of species’ communities and co-occurrences. We modelled jointly the occurrences and co-occurrences of 1016 tropical tree species with abundance data from inventories of 574 localities in eastern South America. We estimated species co-occurrences as raw and residual associations with models that excluded and included the environmental effects on the species’ co-occurrences, respectively. Raw associations indicate co-occurrence of species, whereas residual associations indicate co-occurrence of species after accounting for shared responses to environment. Generally, the influence of environmental filtering exceeded that of limiting similarity in shaping species’ co-occurrences. The number of raw associations was generally higher than that of the residual associations due to the shared responses of tree species to the environmental covariates. Contrary to what was expected from assuming limiting similarity, phylogenetic relatedness or functional similarity did not limit tree co-occurrences. The proportions of positive and negative residual associations varied greatly across the study area, and we found a significant tendency of some biogeographical regions having higher proportions of negative associations between them, suggesting that large-scale biogeographical processes limit the establishment of trees and consequently their co-occurrences.

Automated recorders are increasingly used in remote sensing of wildlife, yet automated methods of processing the audio remains challenging. Identifying animal sounds with machine learning provides a solution, but optimizing the models requires annotated training data. Producing such data can require much manual effort, which could be alleviated by engaging masses to contribute to research and share the workload. Birdwatchers are experts on identifying bird vocalizations and form an ideal focal audience for a citizen science project aiming for the required multitudes of annotated avian audio data. For this purpose, we launched a web portal that was targeted and advertised to Finnish birdwatchers. The users were asked to complete two kinds of tasks: 1) classify if a given bird sound belonged to the focal species and 2) classify all the bird species vocalizing in 10-second audio clips. In less than a year, the portal achieved annotations for 244,300 bird sounds and 5,358 clips, and attracted, on average, 70 visitors on daily basis. More than 200 birdwatchers took part in the classification tasks, of which 17 and 4 most dedicated users produced over half of the sound and clip classifications, respectively. As expected of birder experts, the classifications among users were highly consistent (mean agreement scores between 0.85–0.95, depending on the audio type) and resulted in high- quality training data for parameterizing machine learning models. Feedback about the web portal suggested that additional functionality such as increased freedom of choice would increase user motivation and dedication.

Species interactions are known to structure ecological communities. Still, the influence of climate change on biodiversity has primarily been evaluated by correlating individual species distributions with local climatic descriptors, then extrapolating into future climate scenarios. We ask whether predictions on arctic arthropod response to climate change can be improved by accounting for species interactions. For this, we use a 14-year-long, weekly time series from Greenland, resolved to the species level by mitogenome mapping. During the study period, temperature increased by 2 degrees C and arthropod species richness halved. We show that with abiotic variables alone, we are essentially unable to predict species responses, but with species interactions included, the predictive power of the models improves considerably. Cascading trophic effects thereby emerge as important in structuring biodiversity response to climate change. Given the need to scale up from species-level to community-level projections of biodiversity change, these results represent a major step forward for predictive ecology.

Ongoing climate change can shift organism phenology in ways that vary depending on species, habitats and climate factors studied. To probe for large-scale patterns in associated phenological change, we use 70,709 observations from six decades of systematic monitoring across the former Union of Soviet Socialist Republics. Among 110 phenological events related to plants, birds, insects, amphibians and fungi, we find a mosaic of change, defying simple predictions of earlier springs, later autumns and stronger changes at higher latitudes and elevations. Site mean temperature emerged as a strong predictor of local phenology, but the magnitude and direction of change varied with trophic level and the relative timing of an event. Beyond temperature-associated variation, we uncover high variation among both sites and years, with some sites being characterized by disproportionately long seasons and others by short ones. Our findings emphasize concerns regarding ecosystem integrity and highlight the difficulty of predicting climate change outcomes., The work was financially supported by the Academy of Finland, grants 250243 (O.O.), 284601 (O.O.), 309581 (O.O.); the European Research Council, ERC Starting Grant 205905 (O.O.) and Synergy Grant 856506 – LIFEPLAN (to O.O. and T.R.); Nordic Environment Finance Corporation Grant (O.O.); Jane and Aatos Erkko Foundation Grant (O.O., T.R., M.H., L.A.); University of Helsinki HiLIFE Fellow Grant 2017–2020 (O.O.); and the Research Council of Norway through its Centres of Excellence Funding Scheme (223257) to O.O. via Centre for Biodiversity Dynamics; the Kone Foundation 44-6977 (M.D.) and 55-14839 (G.T.); a Spanish Ramon y Cajal grant RYC-2014-16263 (M.D.); the Federal Budget for the Forest Research Institute of Karelian Research Centre Russian Academy of Sciences 220-2017-0003, 0220-2017-0005 (L.V., S.S. and J.K.); the Russian Foundation for Basic Research Grant 16-08-00510 (L.K.), and the Ministry of Education and Science of the Russian Federation 0017-2019-0009 (Keldysh Institute of Applied Mathematics, Russian Academy of Sciences) (N.I., M. Shashkov).

International audience; The tree seed mycobiome has received little attention despite its potential role in forest regeneration and health. The aim of the present study was to analyze the processes shaping the composition of seed fungal communities in natural forests as seeds transition from the mother plant to the ground for establishment.We used metabarcoding approaches and confocal microscopy to analyze the fungal communities of seeds collected in the canopy and on the ground in four natural populations of sessile oak (Quercus petraea). Ecological processes shaping the seed mycobiome were inferred using joint species distribution models.Fungi were present in seed internal tissues, including the embryo. The seed mycobiome differed among oak populations and trees within the same population. Its composition was largely influenced by the mother, with weak significant environmental influences. The models also revealed several probable interactions among fungal pathogens and mycoparasites.Our results demonstrate that maternal effects, environmental filtering and biotic interactions all shape the seed mycobiome of sessile oak. They provide a starting point for future research aimed at understanding how maternal genes and environments interact to control the vertical transmission of fungal species that could then influence seed dispersal and germination, and seedling recruitment.

Natural selection on floral scent composition is a key element of the hypothesis that pollinators and other floral visitors drive scent evolution. The measure of such selection is complicated by the high-dimensional nature of floral scent data and uncertainty about the cognitive processes involved in scent-mediated communication. We use dimension reduction through reduced-rank regression to jointly estimate a scent composite trait under selection and the strength of selection acting on this trait. To assess and compare variation in selection on scent across species, time and space, we reanalyse 22 datasets on six species from four previous studies. The results agreed qualitatively with previous analyses in terms of identifying populations and scent compounds subject to stronger selection but also allowed us to evaluate and compare the strength of selection on scent across studies. Doing so revealed that selection on floral scent was highly variable, and overall about as common and as strong as selection on other phenotypic traits involved in pollinator attraction or pollen transfer. These results are consistent with an important role of floral scent in pollinator attraction. Our approach should be useful for further studies of plant-animal communication and for studies of selection on other high-dimensional phenotypes. In particular, our approach will be useful for studies of pollinator-mediated selection on complex scent blends comprising many volatiles, and when no prior information on the physiological responses of pollinators to scent compounds is available.

Climate change is a pervasive threat to biodiversity. While range shifts are a known consequence of climate warming contributing to regional community change, less is known about how species' positions shift within their climatic niches. Furthermore, whether the relative importance of different climatic variables prompting such shifts varies with changing climate remains unclear. Here we analysed four decades of data for 1,478 species of birds, mammals, butterflies, moths, plants and phytoplankton along a 1,200 km high latitudinal gradient. The relative importance of climatic drivers varied non-uniformly with progressing climate change. While species turnover among decades was limited, the relative position of species within their climatic niche shifted substantially. A greater proportion of species responded to climatic change at higher latitudes, where changes were stronger. These diverging climate imprints restructure a full biome, making it difficult to generalize biodiversity responses and raising concerns about ecosystem integrity in the face of accelerating climate change.The authors analyse four decades of distribution data for various taxonomic groups to understand the shift of species within their climatic niches and the changing influences of different climate factors. The diverse and diverging climate imprints raise concerns about future ecosystem integrity.

1. Fragmentation of tropical forests is increasing globally, with negative impacts for biodiversity. In Southeast Asia, expansion of oil palm agriculture has caused widespread deforestation, forest degradation and fragmentation. 2. Persistence of forest-dependent species within these fragmented landscapes is likely to depend on the capacity of individuals to move between forest patches. In oil palm landscapes, riparian buffers along streams and rivers are potential movement corridors, but their use by moving animals is poorly studied. 3. We examined how six dung beetle species traversed riparian buffers connected to a continuous forest reserve area within an oil palm plantation in Sabah, Malaysian Borneo. We used a mark–release–recapture study and a new Bayesian Joint Species Movement Modelling (JSMM) approach, extended to a continuous capture process model. 4. Dung beetle species were fairly generalist in their habitat use, but two species showed a statistically supported preference for riparian buffer forest over oil palm, and one species showed a strong preference for forest reserve over riparian buffer, indicating the importance of forested areas within oil palm landscapes for some species. 5. A land-use change simulation indicated that the loss of riparian buffers in oil palm will result in reduced movement by forest-dependent species. 6. Synthesis and applications. Our results provide evidence for the use of riparian buffers in oil palm plantations for forest-dependent dung beetle species, strengthening the case for their retention, restoration and re-establishment. Furthermore, our study demonstrates the wider applicability of the Joint Species Movement Modelling (JSMM) framework to assess movement behaviour of species in fragmented landscapes, a vital tool for future forest and landscape management and conservation prioritisation exercises.

Publisher Copyright: © 2021 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd. To associate specimens identified by molecular characters to other biological knowledge, we need reference sequences annotated by Linnaean taxonomy. In this study, we (1) report the creation of a comprehensive reference library of DNA barcodes for the arthropods of an entire country (Finland), (2) publish this library, and (3) deliver a new identification tool for insects and spiders, as based on this resource. The reference library contains mtDNA COI barcodes for 11,275 (43%) of 26,437 arthropod species known from Finland, including 10,811 (45%) of 23,956 insect species. To quantify the improvement in identification accuracy enabled by the current reference library, we ran 1000 Finnish insect and spider species through the Barcode of Life Data system (BOLD) identification engine. Of these, 91% were correctly assigned to a unique species when compared to the new reference library alone, 85% were correctly identified when compared to BOLD with the new material included, and 75% with the new material excluded. To capitalize on this resource, we used the new reference material to train a probabilistic taxonomic assignment tool, FinPROTAX, scoring high success. For the full-length barcode region, the accuracy of taxonomic assignments at the level of classes, orders, families, subfamilies, tribes, genera, and species reached 99.9%, 99.9%, 99.8%, 99.7%, 99.4%, 96.8%, and 88.5%, respectively. The FinBOL arthropod reference library and FinPROTAX are available through the Finnish Biodiversity Information Facility (www.laji.fi) at https://laji.fi/en/theme/protax. Overall, the FinBOL investment represents a massive capacity-transfer from the taxonomic community of Finland to all sectors of society.

For prioritizing conservation actions, it is vital to understand how ecologically diverse species respond to environmental change caused by human activity. This is particularly necessary considering that chronic human disturbance is a threat to biodiversity worldwide. Depending on how species tolerate and adapt to such disturbance, ecological integrity and ecosystem services will be more or less affected. Bats are a species-rich and functionally diverse group, with important roles in ecosystems, and are therefore recognized as a good model group for assessing the impact of environmental change. Their populations have decreased in several regions, especially in the tropics, and are threatened by increasing human disturbance. Using passive acoustic monitoring, we assessed how the species-rich aerial insectivorous bats—essential for insect suppression services—respond to chronic human disturbance in the Caatinga dry forests of Brazil, an area potentially harboring ca. 100 bat species (nearly 50% are insectivorous), but with > 60% its area composed of anthropogenic ecosystems under chronic pressure. Acoustic data for bat activity was collected at research sites with varying amounts of chronic human disturbance (e.g., livestock grazing and firewood gathering). The intensity of the disturbance is indicated by the global multi-metric CAD index (GMDI). Using Animal Sound Identifier (ASI) software, we identified 18 different bat taxon units. Using Hierarchical Modeling of Species Communities (HMSC), we found trends in the association of the disturbance gradient with species richness and bat activity: species richness was higher at sites with higher human disturbance, whereas bat activity decreased with increasing human disturbance. Additionally, we observed taxon-specific responses to human disturbance. We conclude that the effects of chronic anthropogenic disturbance on the insectivorous bat fauna in the Caatinga are not homogeneous and a species-specific approach is necessary when assessing the responses of local bats to human disturbances in tropical dry forests, and in other biomes under human pressure.

Our knowledge of the factors affecting species abundances is mainly based on time-series analyses of a few well-studied species at single or few localities, but we know little about whether results from such analyses can be extrapolated to the community level. We apply a joint species distribution model to long-term time-series data on British bird communities to examine the relative contribution of intra- and interspecific density dependence at different spatial scales, as well as the influence of environmental stochasticity, to spatiotemporal interspecific variation in abundance. Intraspecific density dependence has the major structuring effect on these bird communities. In addition, environmental fluctuations affect spatiotemporal differences in abundance. In contrast, species interactions had a minor impact on variation in abundance. Thus, important drivers of single-species dynamics are also strongly affecting dynamics of communities in time and space.

Caio Graco-Roza was funded by the Coordination for the Improvement of Higher Education Personnel (CAPES), the Carlos Chagas Filho Research Support Foundation (FAPERJ) and the Ella and Georg Erhnrooth Foundation; Jan Altman by research grants INTER-EXCELLENCE LTAUSA19137 provided by Czech Ministry of Education, Youth and Sports, 20-05840Y of the Czech Science Foundation, and long-term research development project no. RVO 67985939 of the Czech Academy of Sciences; Otso Ovaskainen was funded by Academy of Finland (grant no. 309581), Jane and Aatos Erkko Foundation, Research Council of Norway through its Centres of Excellence Funding Scheme (223257), and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 856506; ERC-synergy project LIFEPLAN); and Jianjun Wang was funded by CAS Key Research Program of Frontier Sciences (QYZDB-SSW-DQC043) and National Natural Science Foundation of China (91851117). The "sPlot" project was initiated by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (DFG FZT 118), and is now a platform of iDiv. The study was supported by the TRY initiative on plant traits (). We are also grateful to Jens Kattge and TRY database. TRY is hosted, developed and maintained at the Max Planck Institute for Biogeochemistry (MPI-BGC) in Jena, Germany, in collaboration with the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. The CESTES database of metacommunities is also an initiative of iDiv led by Alienor Jeliazkov. We thank sDiv for supporting the open science initiative., Aim: Understanding the variation in community composition and species abundances (i.e., β-diversity) is at the heart of community ecology. A common approach to examine β-diversity is to evaluate directional variation in community composition by measuring the decay in the similarity among pairs of communities along spatial or environmental distance. We provide the first global synthesis of taxonomic and functional distance decay along spatial and environmental distance by analysing 148 datasets comprising different types of organisms and environments. Location: Global. Time period: 1990 to present. Major taxa studied: From diatoms to mammals. Method: We measured the strength of the decay using ranked Mantel tests (Mantel r) and the rate of distance decay as the slope of an exponential fit using generalized linear models. We used null models to test whether functional similarity decays faster or slower than expected given the taxonomic decay along the spatial and environmental distance. We also unveiled the factors driving the rate of decay across the datasets, including latitude, spatial extent, realm and organismal features. Results: Taxonomic distance decay was stronger than functional distance decay along both spatial and environmental distance. Functional distance decay was random given the taxonomic distance decay. The rate of taxonomic and functional spatial distance decay was fastest in the datasets from mid-latitudes. Overall, datasets covering larger spatial extents showed a lower rate of decay along spatial distance but a higher rate of decay along environmental distance. Marine ecosystems had the slowest rate of decay along environmental distances. Main conclusions: In general, taxonomic distance decay is a useful tool for biogeographical research because it reflects dispersal-related factors in addition to species responses to climatic and environmental variables. Moreover, functional distance decay might be a cost-effective option for investigating community changes in heterogeneous environments., Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ), Ella and Georg Erhnrooth Foundation, Ministry of Education, Youth & Sports - Czech Republic LTAUSA19137 Grant Agency of the Czech Republic 20-05840Y Czech Academy of Sciences RVO 67985939, Academy of Finland 309581, Jane and Aatos Erkko Foundation, Research Council of Norway through its Centres of Excellence Funding Scheme 223257, European Research Council (ERC) 856506, CAS Key Research Program of Frontier Sciences QYZDB-SSW-DQC043, National Natural Science Foundation of China (NSFC) 91851117, German Research Foundation (DFG) DFG FZT 118, TRY initiative on plant traits

The ability of wild animals to navigate and survive in complex and dynamic environments depends on their ability to store relevant information and place it in a spatial context. Despite the centrality of spatial memory, and given our increasing ability to observe animal movements in the wild, it is perhaps surprising how difficult it is to demonstrate spatial memory empirically. We present a cognitive analysis of movements of several wolves (Canis lupus) in Finland during a summer period of intensive hunting and den-centered pup-rearing. We tracked several wolves in the field by visiting nearly all GPS locations outside the den, allowing us to identify the species, location and timing of nearly all prey killed. We then developed a model that assigns a spatially explicit value based on memory of predation success and territorial marking. The framework allows for estimation of multiple cognitive parameters, including temporal and spatial scales of memory. For most wolves, fitted memory-based models outperformed null models by 20 to 50% at predicting locations where wolves chose to forage. However, there was a high amount of individual variability among wolves in strength and even direction of responses to experiences. Some wolves tended to return to locations with recent predation success—following a strategy of foraging site fidelity—while others appeared to prefer a site switching strategy. These differences are possibly explained by variability in pack sizes, numbers of pups, and features of the territories. Our analysis points toward concrete strategies for incorporating spatial memory in the study of animal movements while providing nuanced insights into the behavioral strategies of individual predators.

Ecosystems face both local hazards, such as over-exploitation, and global hazards, such as climate change. Since the impact of local hazards attenuates with distance from humans, local extinction risk should decrease with remoteness, making faraway areas safe havens for biodiversity. However, isolation and reduced anthropogenic disturbance may increase ecological specialization in remote communities, and hence their vulnerability to secondary effects of diversity loss propagating through networks of interacting species. We show this to be true for reef fish communities across the globe. An increase in fish-coral dependency with the distance of coral reefs from human settlements, paired with the far-reaching impacts of global hazards, increases the risk of fish species loss, counteracting the benefits of remoteness. Hotspots of fish risk from fish-coral dependency are distinct from those caused by direct human impacts, increasing the number of risk hotspots by ~30% globally. These findings might apply to other ecosystems on Earth and depict a world where no place, no matter how remote, is safe for biodiversity, calling for a reconsideration of global conservation priorities., Coral reefs face both local and global stressors. Here, the authors show how a positive relationship between distance from human settlements and ecological specialisation makes remote coral reef fish communities more vulnerable to coral loss.

1. An automatic bird sound recognition system is a useful tool for collecting data of different bird species for ecological analysis. Together with autonomous recording units (ARUs), such a system provides a possibility to collect bird observations on a scale that no human observer could ever match. During the last decades, progress has been made in the field of automatic bird sound recognition, but recognizing bird species from untargeted soundscape recordings remains a challenge. 2. In this article, we demonstrate the workflow for building a global identification model and adjusting it to perform well on the data of autonomous recorders from a specific region. We show how data augmentation and a combination of global and local data can be used to train a convolutional neural network to classify vocalizations of 101 bird species. We construct a model and train it with a global data set to obtain a base model. The base model is then fine-tuned with local data from Southern Finland in order to adapt it to the sound environment of a specific location and tested with two data sets: one originating from the same Southern Finnish region and another originating from a different region in German Alps. 3. Our results suggest that fine-tuning with local data significantly improves the network performance. Classification accuracy was improved for test recordings from the same area as the local training data (Southern Finland) but not for recordings from a different region (German Alps). Data augmentation enables training with a limited number of training data and even with few local data samples significant improvement over the base model can be achieved. Our model outperforms the current state-of-the-art tool for automatic bird sound classification. An automatic bird sound recognition system is a useful tool for collecting data of different bird species for ecological analysis. Together with autonomous recording units (ARUs), such a system provides a possibility to collect bird observations on a scale that no human observer could ever match. During the last decades, progress has been made in the field of automatic bird sound recognition, but recognizing bird species from untargeted soundscape recordings remains a challenge. In this article, we demonstrate the workflow for building a global identification model and adjusting it to perform well on the data of autonomous recorders from a specific region. We show how data augmentation and a combination of global and local data can be used to train a convolutional neural network to classify vocalizations of 101 bird species. We construct a model and train it with a global data set to obtain a base model. The base model is then fine-tuned with local data from Southern Finland in order to adapt it to the sound environment of a specific location and tested with two data sets: one originating from the same Southern Finnish region and another originating from a different region in German Alps. Our results suggest that fine-tuning with local data significantly improves the network performance. Classification accuracy was improved for test recordings from the same area as the local training data (Southern Finland) but not for recordings from a different region (German Alps). Data augmentation enables training with a limited number of training data and even with few local data samples significant improvement over the base model can be achieved. Our model outperforms the current state-of-the-art tool for automatic bird sound classification. Using local data to adjust the recognition model for the target domain leads to improvement over general non-tailored solutions. The process introduced in this article can be applied to build a fine-tuned bird sound classification model for a specific environment.

Although spatial and temporal variation are both important components structuring microbial communities, the exact quantification of temporal turnover rates of fungi and bacteria has not been performed to date. In this study, we utilised repeated resampling of bacterial and fungal communities at specific locations across multiple years to describe their patterns and rates of temporal turnover. Our results show that microbial communities undergo temporal change at a rate of 0.010-0.025 per year (in units of Sorensen similarity), and the change in soil is slightly faster in fungi than in bacteria, with bacterial communities changing more rapidly in litter than soil. Importantly, temporal development differs across fungal guilds and bacterial phyla with different ecologies. While some microbial guilds show consistent responses across regional locations, others show site-specific development with weak general patterns. These results indicate that guild-level resolution is important for understanding microbial community assembly, dynamics and responses to environmental factors.

Bioregions are important tools for understanding and managing natural resources. Bioregions should describe locations of relatively homogenous assemblages of species occur, enabling managers to better regulate activities that might affect these assemblages. Many existing bioregionalization approaches, which rely on expert-derived, Delphic comparisons or environmental surrogates, do not explicitly include observed biological data in such analyses. We highlight that, for bioregionalizations to be useful and reliable for systems scientists and managers, the bioregionalizations need to be based on biological data; to include an easily understood assessment of uncertainty, preferably in a spatial format matching the bioregions; and to be scientifically transparent and reproducible. Statistical models provide a scientifically robust, transparent, and interpretable approach for ensuring that bioregions are formed on the basis of observed biological and physical data. Using statistically derived bioregions provides a repeatable framework for the spatial representation of biodiversity at multiple spatial scales. This results in better-informed management decisions and biodiversity conservation outcomes.

Habitat loss is one of the key drivers of the ongoing decline of biodiversity. However, ecologists still argue about how fragmentation of habitat (independent of habitat loss) affects species richness. The recently proposed habitat amount hypothesis posits that species richness only depends on the total amount of habitat in a local landscape. In contrast, empirical studies report contrasting patterns: some find positive and others negative effects of fragmentation per se on species richness. To explain this apparent disparity, we devise a stochastic, spatially explicit model of competitive species communities in heterogeneous habitats. The model shows that habitat loss and fragmentation have complex effects on species diversity in competitive communities. When the total amount of habitat is large, fragmentation per se tends to increase species diversity, but if the total amount of habitat is small, the situation is reversed: fragmentation per se decreases species diversity., Habitat loss is one of the key drivers of the ongoing decline of biodiversity, but ecologists still debate how fragmentation of habitat (independent of habitat loss) affects species richness. We devise a model of competitive species communities, which shows that habitat loss and fragmentation have complex effects on species diversity. When the total amount of habitat is large, fragmentation per se tends to increase species diversity, but if the total amount of habitat is small, the situation is reversed: fragmentation per se decreases species diversity

Individual-based models, ‘IBMs’, describe naturally the dynamics of interacting organisms or social or financial agents. They are considered too complex for mathematical analysis, but computer simulations of them cannot give the general insights required. Here, we resolve this problem with a general mathematical framework for IBMs containing interactions of an unlimited level of complexity, and derive equations that reliably approximate the effects of space and stochasticity. We provide software, specified in an accessible and intuitive graphical way, so any researcher can obtain analytical and simulation results for any particular IBM without algebraic manipulation. We illustrate the framework with examples from movement ecology, conservation biology, and evolutionary ecology. This framework will provide unprecedented insights into a hitherto intractable panoply of complex models across many scientific fields., Individual-based models are widely used to simulate complex systems of interacting agents. Here the authors provide a mathematical framework that automates the analysis of any model in a wide class, facilitating a deeper understanding of the scientific questions these models are used to address.

A key challenge for community ecology is to understand to what extent observational data can be used to infer the underlying community assembly processes. As different processes can lead to similar or even identical patterns, statistical analyses of non-manipulative observational data never yield undisputable causal inference on the underlying processes. Still, most empirical studies in community ecology are based on observational data, and hence understanding under which circumstances such data can shed light on assembly processes is a central concern for community ecologists. We simulated a spatial agent-based model that generates variation in metacommunity dynamics across multiple axes, including the four classic metacommunity paradigms as special cases. We further simulated a virtual ecologist who analysed snapshot data sampled from the simulations using eighteen output metrics derived from beta-diversity and habitat variation indices, variation partitioning and joint species distribution modelling. Our results indicated two main axes of variation in the output metrics. The first axis of variation described whether the landscape has patchy or continuous variation, and thus was essentially independent of the properties of the species community. The second axis of variation related to the level of predictability of the metacommunity. The most predictable communities were niche-based metacommunities inhabiting static landscapes with marked environmental heterogeneity, such as metacommunities following the species sorting paradigm or the mass effects paradigm. The most unpredictable communities were neutral-based metacommunities inhabiting dynamics landscapes with little spatial heterogeneity, such as metacommunities following the neutral or patch sorting paradigms. The output metrics from joint species distribution modelling yielded generally the highest resolution to disentangle among the simulated scenarios. Yet, the different types of statistical approaches utilized in this study carried complementary information, and thus our results suggest that the most comprehensive evaluation of metacommunity structure can be obtained by combining them.

Inbreeding is common in nature, and many laboratory studies have documented that inbreeding depression can reduce the fitness of individuals. Demonstrating the consequences of inbreeding depression on the growth and persistence of populations is more challenging because populations are often regulated by density- or frequency-dependent selection and influenced by demographic and environmental stochasticity. A few empirical studies have shown that inbreeding depression can increase extinction risk of local populations. The importance of inbreeding depression at the metapopulation level has been conjectured based on population-level studies but has not been evaluated. We quantified the impact of inbreeding depression affecting the fitness of individuals on metapopulation persistence in heterogeneous habitat networks of different sizes and habitat configuration in a context of natural butterfly metapopulations. We developed a spatial individual-based simulation model of metapopulations with explicit genetics. We used Approximate Bayesian Computation to fit the model to extensive demographic, genetic, and life-history data available for the well-studied Glanville fritillary butterfly (Melitaea cinxia) metapopulations in the Åland islands in SW Finland. We compared 18 semi-independent habitat networks differing in size and fragmentation. The results show that inbreeding is more frequent in small habitat networks, and consequently, inbreeding depression elevates extinction risks in small metapopulations. Metapopulation persistence and neutral genetic diversity maintained in the metapopulations increase with the total habitat amount in and mean patch size of habitat networks. Dispersal and mating behavior interact with landscape structure to determine how likely it is to encounter kin while looking for mates. Inbreeding depression can decrease the viability of small metapopulations even when they are strongly influenced by stochastic extinction-colonization dynamics and density-dependent selection. The findings from this study support that genetic factors, in addition to demographic factors, can contribute to extinctions of small local populations and also of metapopulations. This article is protected by copyright. All rights reserved.

Wood-living beetles make up a large proportion of forest biodiversity and contribute to important ecosystem services, including decomposition. Beetle communities in managed southern boreal forests are less species rich than in natural and near-natural forest stands. In addition, many beetle species rely primarily on specific tree species. Yet, the associations between individual beetle species, forest management category, and tree species are seldom quantified, even for red-listed beetles. We compiled a beetle capture dataset from flight intercept traps placed on Norway spruce (Picea abies), oak (Quercus sp.), and Eurasian aspen (Populus tremulae) trees in 413 sites in mature managed forest, near-natural forest, and clear-cuts in southeastern Norway. We used joint species distribution models to estimate the strength of associations for 368 saproxylic beetle species (including 20 vulnerable, endangered, or critical red-listed species) for each forest management category and tree species. Tree species on which traps were mounted had the largest effect on beetle communities; oaks had the most highly associated beetle species, including most of the red-listed species, followed by Norway spruce and Eurasian aspen. Most beetle species were more likely to be captured in near-natural than in mature managed forest. Our estimated associations were compatible ? for many species ? with categorical classifications found in several existing databases of saproxylic beetle preferences. These quantitative beetle-habitat associations will improve future analyses that have typically relied on categorical classifications. Our results highlight the need to prioritize conservation of near-natural forests and oak trees in Scandinavia to protect the habitat of many red-listed species in particular. Furthermore, we underline the importance of carefully considering the species of trees on which traps are mounted in order to representatively sample beetle communities in forest stands.

Climate warming is predicted to have major impacts on the structure of terrestrial communities, particularly in high latitude ecosystems where growing seasons are short. Higher temperatures may dampen seasonal dynamics in community composition as a consequence of earlier snowmelt, with potentially cascading effects across all levels of biological organisation. Here, we examined changes in community assembly and structure along a natural soil temperature gradient in the Hengill geothermal valley, Iceland, during the summer of 2015. Sample collection over several time points within a season allowed us to assess whether temperature alters temporal variance in terrestrial communities and compositional turnover. We found that seasonal fluctuations in species richness, diversity and evenness were dampened as soil temperature increased, whereas invertebrate biomass varied more. Body mass was found to be a good predictor of species occurrence, with smaller species found at higher soil temperatures and emerging earlier in the season. Our results provide more in-depth understanding of the temporal nature of community and population-level responses to temperature, and indicate that climate warming will likely dampen the seasonal turnover of community structure that is characteristic of high latitude invertebrate communities.

The European Union adopted the Water Framework Directive (WFD) in the year 2000 to tackle the rapid degradation of freshwater systems. However, biological, hydromorphological, and physico-chemical water quality targets are currently not met, and identifying successful policy implementation and management actions is of key importance. We built a joint species distribution model for riverine fish in Flanders (Belgium) to better understand the response of fish communities to current environmental policy goals. Environmental covariates included physico-chemical variables and hydromorphological quality indices, while waterway distances accounted for spatial effects. We detected strong effects of physico-chemistry on fish species' distributions. Evaluation of fish community responses to simulated policy scenarios revealed that targeting a 'good' status, following the WFD, increases average species richness with a fraction of species (0.13-0.69 change in accumulated occurrence probabilities). Targeting a 'very good' status, however, predicted an increase of 0.17-1.38 in average species richness. These simulations indicated that riverbed quality, nitrogen, and conductivity levels should be the focal point of policy. However, the weak response of species to a 'good' quality together with the complexity of nutrient-associated problems, suggest a challenging future for river restoration in Flanders.

Understanding the interactive dynamics between fungal and bacterial communities is important to gain predictive knowledge on ecosystem functioning. However, little is known about the mechanisms behind fungal-bacterial associations and the directionality of species interactions., Fungal-bacterial interactions play a key role in the functioning of many ecosystems. Thus, understanding their interactive dynamics is of central importance for gaining predictive knowledge on ecosystem functioning. However, it is challenging to disentangle the mechanisms behind species associations from observed co-occurrence patterns, and little is known about the directionality of such interactions. Here, we applied joint species distribution modeling to high-throughput sequencing data on co-occurring fungal and bacterial communities in deadwood to ask whether fungal and bacterial co-occurrences result from shared habitat use (i.e., deadwood’s properties) or whether there are fungal-bacterial interactive associations after habitat characteristics are taken into account. Moreover, we tested the hypothesis that the interactions are mainly modulated through fungal communities influencing bacterial communities. For that, we quantified how much the predictive power of the joint species distribution models for bacterial and fungal community improved when accounting for the other community. Our results show that fungi and bacteria form tight association networks (i.e., some species pairs co-occur more frequently and other species pairs co-occur less frequently than expected by chance) in deadwood that include common (or opposite) responses to the environment as well as (potentially) biotic interactions. Additionally, we show that information about the fungal occurrences and abundances increased the power to predict the bacterial abundances substantially, whereas information about the bacterial occurrences and abundances increased the power to predict the fungal abundances much less. Our results suggest that fungal communities may mainly affect bacteria in deadwood. IMPORTANCE Understanding the interactive dynamics between fungal and bacterial communities is important to gain predictive knowledge on ecosystem functioning. However, little is known about the mechanisms behind fungal-bacterial associations and the directionality of species interactions. Applying joint species distribution modeling to high-throughput sequencing data on co-occurring fungal-bacterial communities in deadwood, we found evidence that nonrandom fungal-bacterial associations derive from shared habitat use as well as (potentially) biotic interactions. Importantly, the combination of cross-validations and conditional cross-validations helped us to answer the question about the directionality of the biotic interactions, providing evidence that suggests that fungal communities may mainly affect bacteria in deadwood. Our modeling approach may help gain insight into the directionality of interactions between different components of the microbiome in other environments.

Made available in DSpace on 2021-06-25T11:48:11Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-02-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) National Science Foundation Proyectos de Investigacion Cientifica y Tecnologica Academy of Finland Research Council of Norway (Centre of Excellence grant) The interaction between fruit chemistry and the physiological traits of frugivores is expected to shape the structure of mutualistic seed dispersal networks, but it has been understudied compared with the role of morphological trait matching in structuring interaction patterns. For instance, highly frugivorous birds (i.e., birds that have fruits as the main component of their diets), which characteristically have fast gut passage times, are expected to avoid feeding on lipid-rich fruits because of the long gut retention times associated with lipid digestion. Here, we compiled data from 84 studies conducted in the Neotropics that used focal plant methods to record 35,815 feeding visits made by 317 bird species (155 genera in 28 families) to 165 plant species (82 genera in 48 families). We investigated the relationship between the degree of frugivory of birds (i.e., how much of their diet is composed of fruit) at the genus level and their visits to plant genera that vary in fruit lipid content. We used a hierarchical modeling of species communities approach that accounted for the effects of differences in body size, bird and plant phylogeny, and spatial location of study sites. We found that birds with a low degree of frugivory (e.g., predominantly insectivores) tend to have the highest increase in visitation rates as fruits become more lipid rich, while birds that are more frugivorous tend to increase visits at a lower rate or even decrease visitation rates as lipids increase in fruits. This balance between degree of frugivory and visitation rates to lipid-poor and lipid-rich fruits provides a mechanism to explain specialized dispersal systems and the occurrence of certain physiological nutritional filters, ultimately helping us to understand community-wide interaction patterns between birds and plants. Univ Estadual Paulista, Dept Biodiversidade, Inst Biociencias, BR-13506900 Rio Claro, SP, Brazil Consejo Nacl Invest Cient & Tecn, Inst Invest Biodiversidad & Medioambiente, RA-8400 San Carlos De Bariloche, Rio Negro, Argentina Univ Nacl Comahue, RA-8400 San Carlos De Bariloche, Rio Negro, Argentina Univ Helsinki, Organismal & Evolutionary Biol Res Programme, POB 65, FI-00014 Helsinki, Finland Norwegian Univ Sci & Technol, Dept Biol, Ctr Biodivers Dynam, N-7491 Trondheim, Norway Penn State Univ, Dept Biol, University Pk, PA 16802 USA Penn State Univ, Ecol Program, University Pk, PA 16802 USA Univ Estadual Paulista, Dept Biodiversidade, Inst Biociencias, BR-13506900 Rio Claro, SP, Brazil CNPq: 304244/2016-3 National Science Foundation: DEB-1556719 Proyectos de Investigacion Cientifica y Tecnologica: 2015 0815 Academy of Finland: 1273253 Academy of Finland: 250444 Research Council of Norway (Centre of Excellence grant): 223257

Aim The aim of this study was to investigate the role of traits in beetle community assembly and test for consistency in these effects among several bioclimatic regions. We asked (1) whether traits predicted species’ responses to environmental gradients (i.e. their niches), (2) whether these same traits could predict co-occurrence patterns and (3) how consistent were niches and the role of traits among study regions. Location Boreal forests in Norway and Finland, temperate forests in Germany. Taxon Wood-living (saproxylic) beetles. Methods We compiled capture records of 468 wood-living beetle species from the three regions, along with nine morphological and ecological species traits. Eight climatic and forest covariates were also collected. We used Bayesian hierarchical joint species distribution models to estimate the influence of traits and phylogeny on species’ niches. We also tested for correlations between species associations and trait similarity. Finally, we compared species niches and the effects of traits among study regions. Results Traits explained some of the variability in species’ niches, but their effects differed among study regions. However, substantial phylogenetic signal in species niches implies that unmeasured but phylogenetically structured traits have a stronger effect. Degree of trait similarity was correlated with species associations but depended idiosyncratically on the trait and region. Species niches were much more consistent—widespread taxa often responded similarly to an environmental gradient in each region. Main conclusions The inconsistent effects of traits among regions limit their current use in understanding beetle community assembly. Phylogenetic signal in niches, however, implies that better predictive traits can eventually be identified. Consistency of species niches among regions means niches may remain relatively stable under future climate and land use changes; this lends credibility to predictive distribution models based on future climate projections but may imply that species’ scope for short-term adaptation is limited. ISSN:0305-0270 ISSN:1365-2699

Land-use changes are a main driver of modifications in tropical ecosystems, leading to the loss of species and ecological traits and affecting key ecological functions. Although much attention has been given to predict the effects of species loss on ecological processes, information on the large-scale effects of land-use changes over ecological functions is scarce. Here, we detected erosion in the prevalence of ecological functions performed by mammals in response to land-use changes in the Atlantic Forest of Brazil. By analyzing the loss of different ecological functions (vertebrate and invertebrate predation, seed dispersal, seed depredation, herbivory) performed by mammal assemblages in a deforestation gradient, we observed that vulnerable functions (performed by sensitive species, such as browsing, seed depredation, medium and large vertebrate predation) were positively related to patch size and forest cover and negatively related to anthropogenic cover. These relationships were reversed for persistent functions (performed by resilient species, such as grazing, small seed dispersal, small vertebrate and invertebrate predation). Vulnerable functions were virtually restricted to large forest remnants, while persistent functions were prevalent in human-modified landscapes. Disturbed forests are not necessarily empty of mammal species, but there is a substantial loss of ecological functions across most of the Atlantic Forest. Five out of ten ecological functions lose prevalence in small forest remnants. Nonetheless, these small remnants serve as refuges for the remaining biodiversity and are on the verge of the functional extinction of important processes. The erosion of ecological functions provided by mammals compromise the persistence of Atlantic Forest's biodiversity. (C) 2021 Associacao Brasileira de Ciencia Ecologica e Conservacao. Published by Elsevier B.V.

doi: 10.1111/oik.07502 Inferring interspecific interactions indirectly from community data is of central interest in community ecology. Data on species communities can be surveyed using different methods, each of which may differ in the amount and type of species detected, and thus produce varying information on interaction networks. Since fruit bodies reflect only a fraction of the wood-inhabiting fungal diversity, there is an ongoing debate in fungal ecology on whether fruit body?based surveys are a valid method for studying fungal community dynamics compared to surveys based on DNA metabarcoding. In this paper, we focus on species-to-species associations and ask whether the associations inferred from data collected by fruit-body surveys reflect the ones found from data collected by DNA-based surveys. We estimate and compare the association networks resulting from different survey methods using a joint species distribution model. We recorded both raw and residual associations that respectively do not and do correct for the influence of the abiotic predictors when estimating the species-to-species associations. The analyses of the DNA data yielded a larger number of species-to-species associations than the analyses of the fruit body?based data as expected. Yet, we estimated unique associations also from the fruit-body data. Our results show that the directions of estimated residual associations were consistent between the data types, whereas the raw associations were much less consistent, highlighting the need to account for the influence of relevant environmental covariates when estimating association networks. We conclude that even though DNA-based survey methods are more informative about the total number of interacting species, fruit-body surveys are also an adequate method for inferring association networks in wood-inhabiting fungi. Since the DNA and fruit-body data carry on complementary information on fungal communities, the most comprehensive insights are obtained by combining the two survey methods. This article is protected by copyright. All rights reserved.

1. According to metacommunity theories, the structure of natural communities is the result of both environmental filtering and spatial processes, with their relative importance depending on factors including local habitat characteristics, functional features of organisms, and the spatial scale considered. However, few studies have explored environmental and spatial processes in riverine systems at local scales, explicitly incorporating spatial coordinates into multi-taxa distribution models. To address this gap, we conducted a small-scale study to discriminate between abiotic and biotic factors affecting the distribution of aquatic macroin-vertebrates, applying metacommunity concepts.\ud \ud 2. We studied a mountain section in each of three perennial streams within the Po River Basin (northern Italy). We sampled macroinvertebrates both in summer and winter, using specific in situ 50-point random sampling grids. Environmental factors , including benthic organic matter (BOM), flow velocity, water depth, and sub-strate were recorded together with spatial coordinates for each sampling point. The relationships between community metrics (taxon richness, abundance, bio-mass, biomass-abundance ratio, and functional feeding groups) and explanatory variables (environmental and spatial) were assessed using generalised additive models. The influence of the explanatory variables on community structure was analysed with joint species distribution models.\ud \ud 3. Environmental variables-primarily BOM-were the main drivers affecting community metrics, whereas the effects of spatial variables varied among metrics, streams, and seasons. During summer, community structure was strongly affected by BOM and spatial position within the riverbed, the latter probably being a proxy for mass effects mediated by biotic and stochastic processes. In contrast, community structure was mainly shaped by hydraulic variables in winter.\ud \ud 4. Using macroinvertebrate communities as a model group, our results demonstrate that metacommunity concepts can explain small-scale variability in community structure. We found that both environmental filtering and biotic processes shape local communities, with the strength of these drivers depending on the season. These insights provide baseline knowledge that informs our understanding of ecological responses to environmental variability in contexts including restoration ecology, habitat suitability modelling, and biomonitoring.

Agent-based models are used to study complex phenomena in many fields of science. While simulating agent-based models is often straightforward, predicting their behaviour mathematically has remained a key challenge. Recently developed mathematical methods allow the prediction of the emerging spatial patterns for a general class of agent-based models, whereas the prediction of spatio-temporal pattern has been thus far achieved only for special cases. We present a general and mathematically rigorous methodology that allows deriving the spatio-temporal correlation structure for a general class of individual-based models. To do so, we define an auxiliary model, in which each agent type of the primary model expands to three types, called the original, the past and the new agents. In this way, the auxiliary model keeps track of both the initial and current state of the primary model, and hence the spatio-temporal correlations of the primary model can be derived from the spatial correlations of the auxiliary model. We illustrate the agreement between analytical predictions and agent-based simulations using two example models from theoretical ecology. In particular, we show that the methodology is able to correctly predict the dynamical behaviour of a host–parasite model that shows spatially localized oscillations.

Tree species is one of the most important determinants of wood-inhabiting fungal community composition, yet its relationship with fungal reproductive and dispersal traits remains poorly understood. We studied fungal communities (total of 657 species) inhabiting broadleaved and coniferous dead wood (total of 192 logs) in 12 semi-natural boreal forests. We utilized a trait-based hierarchical joint species distribution model to examine how the relationship between dead wood quality and species occurrence correlates with reproductive and dispersal morphological traits. Broadleaved trees had higher species richness than conifers, due to discomycetoids and pyrenomycetoids specializing in them. Resupinate and pileate species were generally specialized in coniferous dead wood. Fungi inhabiting broadleaved trees had larger and more elongated spores than fungi in conifers. Spore size was larger and spore shape more spherical in species occupying large dead wood units. These results indicate the selective effect of dead wood quality, visible not only in species diversity, but also in reproductive and dispersal traits. (C) 2019 Elsevier Ltd and British Mycological Society. All rights reserved. peerReviewed

Understanding the role of interspecific interactions in shaping ecological communities is one of the central goals in community ecology. In fungal communities, measuring interspecific interactions directly is challenging because these communities are composed of large numbers of species, many of which are unculturable. An indirect way of assessing the role of interspecific interactions in determining community structure is to identify the species co-occurrences that are not constrained by environmental conditions. In this study, we investigated co-occurrences among root-associated fungi, asking whether fungi co-occur more or less strongly than expected based on the environmental conditions and the host plant species examined. We generated molecular data on root-associated fungi of five plant species evenly sampled along an elevational gradient at a high arctic site. We analysed the data using a joint species distribution modelling approach that allowed us to identify those co-occurrences that could be explained by the environmental conditions and the host plant species, as well as those co-occurrences that remained unexplained and thus more probably reflect interactive associations. Our results indicate that not only negative but also positive interactions play an important role in shaping microbial communities in arctic plant roots. In particular, we found that mycorrhizal fungi are especially prone to positively co-occur with other fungal species. Our results bring new understanding to the structure of arctic interaction networks by suggesting that interactions among root-associated fungi are predominantly positive.

Spatially distinct pairs of sites may have similarly fluctuating population dynamics across large geographical distances, a phenomenon called spatial synchrony. However, species rarely exist in isolation, but rather as members of interactive communities, linked with other communities through dispersal (i.e. a metacommunity). Using data on Finnish moth communities sampled across 65 sites for 20 years, we examine the complex synchronous/anti-synchronous relationships among sites using the geography of synchrony framework. We relate site-level synchrony to mean and temporal variation in climatic data, finding that colder and drier sites—and those with the most drastic temperature increases—are important for spatial synchrony. This suggests that faster-warming sites contribute most strongly to site-level estimates of synchrony, highlighting the role of a changing climate to spatial synchrony. Considering the spatial variability in climate change rates is therefore important to understand metacommunity dynamics and identify habitats which contribute most strongly to spatial synchrony.

Contains fulltext : 217063.pdf (Publisher’s version ) (Open Access) Abstract Joint Species Distribution Modelling (JSDM) is becoming an increasingly popular statistical method for analysing data in community ecology. Hierarchical Modelling of Species Communities (HMSC) is a general and flexible framework for fitting JSDMs. HMSC allows the integration of community ecology data with data on environmental covariates, species traits, phylogenetic relationships and the spatio-temporal context of the study, providing predictive insights into community assembly processes from non-manipulative observational data of species communities. The full range of functionality of HMSC has remained restricted to Matlab users only. To make HMSC accessible to the wider community of ecologists, we introduce Hmsc 3.0, a user-friendly r implementation. We illustrate the use of the package by applying Hmsc 3.0 to a range of case studies on real and simulated data. The real data consist of bird counts in a spatio-temporally structured dataset, environmental covariates, species traits and phylogenetic relationships. Vignettes on simulated data involve single-species models, models of small communities, models of large species communities and models for large spatial data. We demonstrate the estimation of species responses to environmental covariates and how these depend on species traits, as well as the estimation of residual species associations. We demonstrate how to construct and fit models with different types of random effects, how to examine MCMC convergence, how to examine the explanatory and predictive powers of the models, how to assess parameter estimates and how to make predictions. We further demonstrate how Hmsc 3.0 can be applied to normally distributed data, count data and presence?absence data. The package, along with the extended vignettes, makes JSDM fitting and post-processing easily accessible to ecologists familiar with r.

The work was funded by Academy of Finland, grants 250243, 284601, 309581 (OO); the European Research Council, ERC Starting Grant 205905 (OO); Nordic Environment Finance Corporation Grant (OO); Jane and Aatos Erkko Foundation Grant (OO and TR); University of Helsinki HiLIFE Fellow Grant 2017–2020 (OO); the Kone Foundation 44-6977 (MD); Spanish Ramon y Cajal grant RYC-2014-16263 (MD); the Federal Budget for the Forest Research Institute of Karelian Research Centre Russian Academy of Sciences 220-2017-0003,0220-2017-0005 (LV, SS and JK); the Russian Foundation for Basic Research Grant 16-08-00510 (LK), and the Ministry of Education and Science of the Russian Federation 0017-2019-0009 (Keldysh Institute of Applied Mathematics, Russian Academy of Sciences) (NI, MSh)., Ovaskainen, O., Meyke, E., Lo, C., Tikhonov, G., Delgado, M.M., Roslin, T., Gurarie, E., Abadonova, M., Abduraimov, O., Adrianova, O., Akimova, T., Akkiev, M., Ananin, A., Andreeva, E., Andriychuk, N., Antipin, M., Arzamascev, K., Babina, S., Babushkin, M., Bakin, O., Barabancova, A., Basilskaja, I., Belova, N., Belyaeva, N., Bespalova, T., Bisikalova, E., Bobretsov, A., Bobrov, V., Bobrovskyi, V., Bochkareva, E., Bogdanov, G., Bolshakov, V., Bondarchuk, S., Bukharova, E., Butunina, A., Buyvolov, Y., Buyvolova, A., Bykov, Y., Chakhireva, E., Chashchina, O., Cherenkova, N., Chistjakov, S., Chuhontseva, S., Davydov, E.A., Demchenko, V., Diadicheva, E., Dobrolyubov, A., Dostoyevskaya, L., Drovnina, S., Drozdova, Z., Dubanaev, A., Dubrovsky, Y., Elsukov, S., Epova, L., Ermakova, O.S., Ermakova, O., Esengeldenova, A., Evstigneev, O., Fedchenko, I., Fedotova, V., Filatova, T., Gashev, S., Gavrilov, A., Gaydysh, I., Golovcov, D., Goncharova, N., Gorbunova, E., Gordeeva, T., Grishchenko, V., Gromyko, L., Hohryakov, V., Hritankov, A., Ignatenko, E., Igosheva, S., Ivanova, U., Ivanova, N., Kalinkin, Y., Kaygorodova, E., Kazansky, F., Kiseleva, D., Knorre, A., Kolpashikov, L., Korobov, E., Korolyova, H., Korotkikh, N., Kosenkov, G., Kossenko, S., Kotlugalyamova, E., Kozlovsky, E., Kozsheechkin, V., Kozurak, A., Kozyr, I., Krasnopevtseva, A., Kruglikov, S., Kuberskaya, O., Kudryavtsev, A., Kulebyakina, E., Kulsha, Y., Kupriyanova, M., Kurbanbagamaev, M., Kutenkov, A., Kutenkova, N., Kuyantseva, N., Kuznetsov, A., Larin, E., Lebedev, P., Litvinov, K., Luzhkova, N., Mahmudov, A., Makovkina, L., Mamontov, V., Mayorova, S., Megalinskaja, I., Meydus, A., Minin, A., Mitrofanov, O., Motruk, M., Myslenkov, A., Nasonova, N., Nemtseva, N., Nesterova, I., Nezdoliy, T., Niroda, T., Novikova, T., Panicheva, D., Pavlov, A., Pavlova, K., Petrenko, P., Podolski, S., Polikarpova, N., Polyanskaya, T., Pospelov, I., Pospelova, E., Prokhorov, I., Prokosheva, I., Puchnina, L., Putrashyk, I., Raiskaya, J., Rozhkov, Y., Rozhkova, O., Rudenko, M., Rybnikova, I., Rykova, S., Sahnevich, M., Samoylov, A., Sanko, V., Sapelnikova, I., Sazonov, S., Selyunina, Z., Shalaeva, K., Shashkov, M., Shcherbakov, A., Shevchyk, V., Shubin, S., Shujskaja, E., Sibgatullin, R., Sikkila, N., Sitnikova, E., Sivkov, A., Skok, N., Skorokhodova, S., Smirnova, E., Sokolova, G., Sopin, V., Spasovski, Y., Stepanov, S., Stratiy, V.І., Strekalovskaya, V., Sukhov, A., Suleymanova, G., Sultangareeva, L., Teleganova, V., Teplov, V., Teplova, V., Tertitsa, T., Timoshkin, V., Tirski, D., Tolmachev, A., Tomilin, A., Tselishcheva, L., Turgunov, M., Tyukh, Y., Vladimir, V., Vargot, E., Vasin, A., Vasina, A., Vekliuk, A., Vetchinnikova, L., Vinogradov, V., Volodchenkov, N., Voloshina, I., Xoliqov, T., Yablonovska-Grishchenko, E., Yakovlev, V., Yakovleva, M., Yantser, O., Yarema, Y., Zahvatov, A., Zakharov, V., Zelenetskiy, N., Zheltukhin, A., Zubina, T., Kurhinen, J.

Made available in DSpace on 2020-12-12T01:46:49Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-02-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Academy of Finland Norges Forskningsråd Aim: Forest fragmentation is among the principal causes of global biodiversity loss, yet how it affects mutualistic interactions between plants and animals at large spatial scale is poorly understood. In particular, tropical forest regeneration depends on animal-mediated seed dispersal, but the seed-dispersing animals face rapid decline due to forest fragmentation and defaunation. Here, we assess how fragmentation influences the pairwise interactions between 407 seed disperser and 1,424 tree species in a highly fragmented biodiversity hotspot. Location: Atlantic Forest, South America. Methods: We predicted interaction networks in 912 sites covering the entire biome by combining verified interaction data with co-occurrence probabilities obtained from a spatially explicit joint species distribution model. We identified keystone seed dispersers by computing a species-specific keystone index and by selecting those species belonging to the top 5% quantile. Results: We show that forest fragmentation affects seed dispersal interactions negatively, and the decreased area of functionally connected forest, rather than increased edge effects, is the main driver behind the loss of interactions. Both the seed disperser availability for the local tree communities and in particular the proportion of interactions provided by keystone seed dispersers decline with increasing degree of fragmentation. Importantly, just 21 keystone species provided >40% of all interactions. The numbers of interactions provided by keystone and non-keystone species, however, were equally negatively affected by fragmentation, suggesting that seed dispersal interactions may not be rewired under strong fragmentation effects. Conclusions: We highlight the importance of understanding the fragmentation-induced compositional shifts in seed disperser communities as they may lead to lagged and multiplicative effects on tree communities. Our results illustrate the utility of model-based prediction of interaction networks as well as model-based identification of keystone species as a tool for prioritizing conservation efforts. Similar modelling approaches could be applied to other threatened ecosystems and interaction types globally. Centre for Biodiversity Dynamics Norwegian University of Science and Technology Department of Agricultural Sciences University of Helsinki Departamento de Ecologia Instituto de Biociências Universidade de São Paulo Departamento de Ecologia Instituto de Biociências Universidade Estadual Paulista (UNESP) Departamento de Zoologia e Centro de Aquicultura Instituto de Biociências Universidade Estadual Paulista (UNESP) Instituto de Ciências da Natureza Universidade Federal de Alfenas IPÊ – Instituto de Pesquisas Ecológicas Department of Natural Resources Management Texas Tech University Museum of Texas Tech University Department of Biology University of Miami Faculty of Biological and Environmental Sciences University of Helsinki Departamento de Ecologia Instituto de Biociências Universidade Estadual Paulista (UNESP) Departamento de Zoologia e Centro de Aquicultura Instituto de Biociências Universidade Estadual Paulista (UNESP) FAPESP: #2013/08722-5 FAPESP: #2013/25441-0 FAPESP: #2013/50421-2 FAPESP: #2014/01986-0 FAPESP: #2014/14739-0 FAPESP: #2014/18800-6 FAPESP: #2015/17739-4 FAPESP: #2017/08440-0 FAPESP: #2017/09676-8 FAPESP: #2017/21816-0 Academy of Finland: 1273253 Norges Forskningsråd: 223257 Academy of Finland: 250444 Academy of Finland: 284601

The ongoing global change and the increased interest in macroecological processes call for the analysis of spatially extensive data on species communities to understand and forecast distributional changes of biodiversity. Recently developed joint species distribution models can deal with numerous species efficiently, while explicitly accounting for spatial structure in the data. However, their applicability is generally limited to relatively small spatial data sets because of their severe computational scaling as the number of spatial locations increases. In this work, we propose a practical alleviation of this scalability constraint for joint species modeling by exploiting two spatial-statistics techniques that facilitate the analysis of large spatial data sets: Gaussian predictive process and nearest-neighbor Gaussian process. We devised an efficient Gibbs posterior sampling algorithm for Bayesian model fitting that allows us to analyze community data sets consisting of hundreds of species sampled from up to hundreds of thousands of spatial units. The performance of these methods is demonstrated using an extensive plant data set of 30,955 spatial units as a case study. We provide an implementation of the presented methods as an extension to the hierarchical modeling of species communities framework.

Made available in DSpace on 2020-12-12T02:41:39Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-08-01 In a rapidly changing world, it is important to understand how environmental modifications by humans affect species behavior. This is not a simple task, since we need to deal with a multitude of species and the different external contexts that affect their behavior. Here, we investigate how interpatch short-distance movements of 73 common forest bird species can be predicted by forest cover and forest isolation. We modeled bird movement as a function of environmental covariates, species traits – body mass and feeding habit – and phylogenetic relationships using Joint Species Movement Models. We used field data collected in forest edges and open pastures of six 600 × 600 m plots in the Atlantic Forest biodiversity hotspot. We found that birds fly larger distances and visit more forest patches and remnant trees with decreasing forest cover. Increasing landscape isolation results in larger flight distances, and it increases the use of trees as stepping-stones for most species. Our results show that birds can adjust their behavior as a response to spatial modification in resource distribution and landscape connectivity. These adjusted behaviors can potentially contribute to ecosystem responses to habitat modification. Univ. Estadual Paulista (UNESP) Depto de Ecologia Inst. de Biociências Univ. Estadual Paulista (UNESP) Depto de Zoologia Inst. de Biociências Grupo de Ecología Cuantitativa INIBIOMA-CRUB CONICET S. C. de Bariloche Organismal and Evolutionary Biology Research Programme Univ. of Helsinki Centre for Biodiversity Dynamics Dept of Biology Norwegian Univ. of Science and Technology Univ. Estadual Paulista (UNESP) Depto de Ecologia Inst. de Biociências Univ. Estadual Paulista (UNESP) Depto de Zoologia Inst. de Biociências

Automated audio recording offers a powerful tool for acoustic monitoring schemes of bird, bat, frog and other vocal organisms, but the lack of automated species identification methods has made it difficult to fully utilise such data. We developed Animal Sound Identifier (ASI), a MATLAB software that performs probabilistic classification of species occurrences from field recordings. Unlike most previous approaches, ASI locates training data directly from the field recordings and thus avoids the need of pre-defined reference libraries. We apply ASI to a case study on Amazonian birds, in which we classify the vocalisations of 14 species in 194504 one-minute audio segments using in total two weeks of expert time to construct, parameterise, and validate the classification models. We compare the classification performance of ASI (with training templates extracted automatically from field data) to that of monitoR (with training templates extracted manually from the Xeno-Canto database), the results showing ASI to have substantially higher recall and precision rates.

A large array of species distribution model (SDM) approaches has been developed for explaining and predicting the occurrences of individual species or species assemblages. Given the wealth of existing models, it is unclear which models perform best for interpolation or extrapolation of existing data sets, particularly when one is concerned with species assemblages. We compared the predictive performance of 33 variants of 15 widely applied and recently emerged SDMs in the context of multispecies data, including both joint SDMs that model multiple species together, and stacked SDMs that model each species individually combining the predictions afterward. We offer a comprehensive evaluation of these SDM approaches by examining their performance in predicting withheld empirical validation data of different sizes representing five different taxonomic groups, and for prediction tasks related to both interpolation and extrapolation. We measure predictive performance by 12 measures of accuracy, discrimination power, calibration, and precision of predictions, for the biological levels of species occurrence, species richness, and community composition. Our results show large variation among the models in their predictive performance, especially for communities comprising many species that are rare. The results do not reveal any major trade-offs among measures of model performance; the same models performed generally well in terms of accuracy, discrimination, and calibration, and for the biological levels of individual species, species richness, and community composition. In contrast, the models that gave the most precise predictions were not well calibrated, suggesting that poorly performing models can make overconfident predictions. However, none of the models performed well for all prediction tasks. As a general strategy, we therefore propose that researchers fit a small set of models showing complementary performance, and then apply a cross-validation procedure involving separate data to establish which of these models performs best for the goal of the study., This work was funded by the Research Foundation of the University of Helsinki (A. Norberg), the Academy of Finland (CoE grant 284601 and grant 309581 to O. Ovaskainen, grant 308651 to N. Abrego, grant 1275606 to A. Lehikoinen), the Research Council of Norway (CoE grant 223257), the Jane and Aatos Erkko Foundation, and the Ministry of Science, Innovation and Universities (grant CGL2015‐68438‐P to M. B. Araújo).

International Scientific Symposium:Ecology and evolution: New challenges. Ekaterinburg, Russia, April 1–5, 2019, p. 67-68. The material for the report was compiled during eight years of operation of the international network of cooperation “Eurasian Chronicle of Nature - Large Scale Analysis of Changing Ecosystems” (“Chronicle of the nature of Eurasia: a large-scale analysis of changing ecosystems”). The material for the report was compiled during eight years of operation of the international network of cooperation “Eurasian Chronicle of Nature - Large Scale Analysis of Changing Ecosystems” (“Chronicle of the nature of Eurasia: a large-scale analysis of changing ecosystems”).