Dispersal Constraints Research Articles

Subalpine conifers in different geographical locations host highly similar foliar bacterial endophyte communities.

Pines in the subalpine environment at Niwot Ridge, CO, have been found to host communities of acetic acid bacteria (AAB) within their needles. The significance and ubiquity of this pattern is not known, but recent evidence of nitrogen (N)-fixing activity in Pinus flexilis (limber pine) foliage calls for a better understanding of the processes that regulate endophytic communities in forest tree canopies. Here, to test if AAB dominate the foliar bacterial microbiota in other subalpine locations, we compared the 16S rRNA community in needles from P. flexilis and P. contorta (lodgepole pine) growing in the Eastern Sierra Nevada, CA, and Niwot Ridge, CO. AAB made up the majority of the bacterial community in both species at both sites. Multiple distinct AAB taxa, resolved at the single nucleotide level, were shared across host species and sites, with dominant OTUs identical or highly similar to database sequences from cold environments, including high altitude air sampled in Colorado, and the endosphere of Arctic plants. Our results suggest strong selection for community composition, potentially amplified by the long lifespan of individual Pinus needles, along with low dispersal constraints on canopy bacteria.

Past influences present: Mammalian species from different biogeographic pools sort environmentally in the Indian subcontinent

Diversity-environment relationships are distinct across species pools, and as a result species from different biogeographic pools have different environmental preferences. Regional communities are drawn from available biogeographic pools, subject to environmental and dispersal constraints. Does shared biogeographic history of taxa lead to similar relationships with the environment? We test this idea in the Indian subcontinent, which is at the confluence of multiple biogeographic regions resulting in species from multiple biogeographic pools being distributed here. Species were classified as belonging to four biogeographic affinities based on their geographic distributions: eastern, northern, western and endemic. We investigated spatial patterns of species richness for all mammals (over 1° x 1° grid cells), for each biogeographic group and for 5 major mammalian orders. Generalized Additive Models (GAM) were used to investigate environment-diversity relationship for all mammals, each biogeographic group, and for major mammalian orders in the Indian subcontinent. Species richness of all mammals was found to be highest in the montane regions of the Eastern Himalayas and the Western Ghats. Species richness of each biogeographic group was highest at the border it shared with Asia, in the direction of immigration from Asia. Environment and spatial variables were both correlated with species richness in the Indian subcontinent and each biogeographic group showed a distinct richness-environment relationship. Additionally, biogeographic groups sorted along environmental space, in keeping with our predictions based on their global distributions. Finally, analyses across mammalian orders had low predictive value, suggesting that shared phylogenetic history is relatively less important than biogeographic ancestry in determining relationships to environment. We conclude that historical factors such as immigration and the distinct evolutionary histories of species impact species richness patterns in the Indian subcontinent. Our results provide insights into drivers of regional community assembly in transition zones where multiple biogeographic species pools co-exist.

Past influences present: Mammalian species from different biogeographic pools sort environmentally in the Indian subcontinent

Diversity-environment relationships are distinct across species pools, and as a result species from different biogeographic pools have different environmental preferences. Regional communities are drawn from available biogeographic pools, subject to environmental and dispersal constraints. Does shared biogeographic history of taxa lead to similar relationships with the environment? We test this idea in the Indian subcontinent, which is at the confluence of multiple biogeographic regions resulting in species from multiple biogeographic pools being distributed here. Species were classified as belonging to four biogeographic affinities based on their geographic distributions: eastern, northern, western and endemic. We investigated spatial patterns of species richness for all mammals (over 1° x 1° grid cells), for each biogeographic group and for 5 major mammalian orders. Generalized Additive Models (GAM) were used to investigate environment-diversity relationship for all mammals, each biogeographic group, and for major mammalian orders in the Indian subcontinent. Species richness of all mammals was found to be highest in the montane regions of the Eastern Himalayas and the Western Ghats. Species richness of each biogeographic group was highest at the border it shared with Asia, in the direction of immigration from Asia. Environment and spatial variables were both correlated with species richness in the Indian subcontinent and each biogeographic group showed a distinct richness-environment relationship. Additionally, biogeographic groups sorted along environmental space, in keeping with our predictions based on their global distributions. Finally, analyses across mammalian orders had low predictive value, suggesting that shared phylogenetic history is relatively less important than biogeographic ancestry in determining relationships to environment. We conclude that historical factors such as immigration and the distinct evolutionary histories of species impact species richness patterns in the Indian subcontinent. Our results provide insights into drivers of regional community assembly in transition zones where multiple biogeographic species pools co-exist.

Uncertainty estimates of theσ-pole determination by Padé approximants

We discuss the determination of the $f_0(500)$ (or $\sigma$) resonance by analytic continuation through Pad\'e approximants of the $\pi\pi$-scattering amplitude from the physical region to the pole in the complex energy plane. The aim is to analyze the uncertainties of the method, having in view the fact that analytic continuation is an ill-posed problem in the sense of Hadamard. Using as input a class of admissible parameterizations of the scalar-isoscalar $\pi\pi$ partial wave, which satisfy with great accuracy the same set of dispersive constraints, we find that the Roy-type integral representations lead to almost identical pole positions for all of them, while the predictions of the Pad\'e approximants have a larger spread, being sensitive to features of the input parameterization that are not controlled by the dispersive constraints. Our conservative conclusion is that the $\sigma$-pole determination by Pad\'e approximants is consistent with the prediction of Roy-type equations, but has an uncertainty almost a factor two larger.

New Multicentury Evidence for Dispersal Limitation during Primary Succession.

Primary succession is limited by both ecosystem development and plant dispersal, but the extent to which dispersal constrains succession over the long-term is unknown. We compared primary succession along two co-occurring arctic chronosequences with contrasting spatial scales: sorted circles that span a few meters and may have few dispersal constraints and glacial forelands that span several kilometers and may have greater dispersal constraints. Dispersal constraints slowed primary succession by centuries: plots were dominated by cryptogams after 20 years on circles but after 270 years on forelands; plots supported deciduous plants after 100 years on circles but after >400 years on forelands. Our study provides century-scale evidence suggesting that dispersal limitations constrain the rate of primary succession in glacial forelands.

The application of a filter‐based assembly model to develop best practices for Pannonian sand grassland restoration

Summary The application of a filter‐based assembly model is a promising concept to support ecological restoration. The filters illustrate dispersal, abiotic and biotic constraints that narrow down the species pool and determine the realized species composition. The aim of restoration interventions is the manipulation of filters to achieve a restoration target. We tested the applicability of a filter‐based assembly model in the restoration of endemic Pannonian sandy grassland on old‐fields. We report the results of a six‐year experiment to identify key filters and find possible interactions. Treatments included the following: (i) seeding (dispersal filter), (ii) carbon amendment to lower nitrogen availability (abiotic filter) and (iii) mowing (biotic filter). Treatments were carried out in 1 m2 plots following the same Latin square design in three old‐fields. Seeding was the most important treatment increasing species richness, vascular cover and enhancing target species composition. Mowing played a secondary role, acting primarily in interaction with the other treatments. Carbon amendment significantly reduced soil mineral nitrogen, but played a subordinate role in determining vegetation composition. Significant interactions were found between the biotic and dispersal and the biotic and abiotic filters determining primarily the structural characteristics of the vegetation in terms of vascular cover, moss cover, litter and bare ground. Regarding species composition, synergistic links between the dispersal and biotic filters for seeded species and long‐lived forbs, and antagonistic interaction between the dispersal and abiotic filters for seeded species were found. Synthesis and applications. Based on the impact and interactions of filters uncovered in this experiment, we propose a scheme for Pannonian grassland restoration, which can be validated and also used for other oligotrophic systems. In the proposed conceptual model, the dispersal filter plays a key role in determining the outcome of restoration measures followed by the biotic filter if manipulated by mowing, while the abiotic filter altered by nitrogen immobilization proved to have a weak effect. Based on our results, targeting several filters in parallel would improve restoration outcome. We propose that filter interactions should form an integral part of filter‐based assembly models and should be taken into account in restoration decisions.

Forecasting Large-Scale Habitat Suitability of European Bustards under Climate Change: The Role of Environmental and Geographic Variables.

We modelled the distribution of two vulnerable steppe birds, Otis tarda and Tetrax tetrax, in the Western Palearctic and projected their suitability up to the year 2080. We performed two types of models for each species: one that included environmental and geographic variables (space-included model) and a second one that only included environmental variables (space-excluded model). Our assumption was that ignoring geographic variables in the modelling procedure may result in inaccurate forecasting of species distributions. On the other hand, the inclusion of geographic variables may generate an artificial constraint on future projections. Our results show that space-included models performed better than space-excluded models. While distribution of suitable areas for T. tetrax in the future was approximately the same as at present in the space-included model, the space-excluded model predicted a pronounced geographic change of suitable areas for this species. In the case of O. tarda, the space-included model showed that many areas of current presence shifted to low or medium suitability in the future, whereas a northward expansion of intermediate suitable areas was predicted by the space-excluded one. According to the best models, current distribution of these species can restrict future distribution, probably due to dispersal constraints and site fidelity. Species ranges would be expected to shift gradually over the studied time period and, therefore, we consider it unlikely that most of the current distribution of these species in southern Europe will disappear in less than one hundred years. Therefore, populations currently occupying suitable areas should be a priority for conservation policies. Our results also show that climate-only models may have low explanatory power, and could benefit from adjustments using information on other environmental variables and biological traits; if the latter are not available, including the geographic predictor may improve the reliability of predicted results.

Dispersion constraint based non-negative sparse coding algorithm

The basic Non-negation Sparse Coding (NNSC) algorithm behaves both advantages of basic Sparse Coding (SC) and Non-negative Matrix Factorization (NMF). It has been proved to be quite more reasonable than SC in explaining the receptive fields of simple cells in V1 in the primary visual system of mammals, and also suitable to process the larger and high dimension data. However, it ignores class information constraint of data, and when it is used in classification task, the advantage is not outstanding. To get better class efficiency, considering the within-class and between class dispersion constraint rule, a new NNSC algorithm based on dispersion constraint denoted by DCB-NNSC one is put forward in this paper, which combines the feature discriminability constraint supervised by classification task and the maximized sparseness criteria in the cost function. At the same time, to ensure the self-adaptive sparse measurement, the Normal Inverse Gaussian Density (NIG) density model is used as sparse penalty function of features. In test, the property of feature extraction is first discussed by implementing the image reconstruction task by using features extracted, and then, the feature recognition capability is further explored by using several classifiers. In the test, test images are provided by the PolyU database. Simulation results show that DCB-NNSC algorithm can capture some significant receptive fields with clearer sparsity and image structure, which are not only efficient in extracting image features, but also favor well the image classification task. Furthermore, comparing DCB-NNSC algorithm discussed here with basic NNSC, experimental results show that DCB-NNSC algorithm is indeed effective and has a good potential in the research task of feature extraction and classification for pattern recognition.

Achieving Domain Consistency and Counting Solutions for Dispersion Constraints

Many combinatorial problems require that their solutions achieve a certain balance of given features. For this important aspect of modeling, the spread and deviation constraints have been proposed in Constraint Programming to express balance among a set of variables by constraining their mean and overall deviation from the mean. To our knowledge, the only practical filtering algorithms known for these constraints achieve bounds consistency. In this paper we improve that filtering by presenting an efficient domain consistency algorithm that applies to both constraints. We also extend it to count solutions so that it can be used in counting-based search, a generic and effective family of branching heuristics that free the user from having to write problem-specific search heuristics. We provide a time complexity analysis of our contributions and empirically evaluate them on benchmark problems.

The contribution of migratory mesopelagic fishes to neuston fish assemblages across the Atlantic, Indian and Pacific Oceans

Surface waters are an attractive foraging ground for small fish in the open ocean. This study aims to determine the importance of vertically migrating species in the neuston of oceanic waters across the Atlantic, Indian and Pacific Oceans and to ascertain the influence of environmental variables on their distribution patterns. Neustonic fish assemblages were primarily controlled by light. They were dominated by late-larvae and juveniles of Exocoetidae, Hemiramphidae and Scomberesocidae during the day. At night, the vertical migration of mesopelagic species changed the dominance pattern in favour of Myctophidae and Scomberesocidae. The neustonic families’ distribution was primarily related to sea surface temperatures, whereas environmental variables at deeper layers were related to mesopelagic migrating families. Canonical correspondence analysis showed a low but statistically significant contribution of several environmental variables to myctophid species composition (10%), with minimum oxygen concentrations ranking first in variance explanation followed by maximum fluorescence, sea surface temperature and 400-m temperature. Spatial autocorrelation also explained 17% of the variance, indicating the influence of other factors such as historical, demographic and dispersal constraints. The low number of myctophid species in the North Pacific Equatorial Countercurrent appears to be related to the low oxygen concentrations observed in this province.

Dispersion Constraint Based Non-negative Sparse Coding Model

Based on advantages of basic non-negative sparse coding (NNSC) model, and considered the prior class constraint of image features, a novel NNSC model is discussed here. In this NNSC model, the sparseness criteria is selected as a two-parameter density estimation model and the dispersion ratio of within-class and between-class is used as the class constraint. Utilizing this NNSC model, image features can be extracted successfully. Further, the feature recognition task by using different classifiers can be implemented well. Simulation results prove that our NNSC model proposed is indeed effective in extracting image features and recognition task in application.

Tracking the distribution and impacts of diseases with biological records and distribution modelling

Species distribution modelling is widely used in epidemiology for mapping spatial patterns and the risk of introduction of diseases and vectors and also for predicting how exposure may alter given future environmental change, motivated by the high societal impact and the multiple environmental drivers of disease outbreaks. Although pathogens and vectors have historically been sparsely recorded, monitoring systems and media sources are generating novel, online data sources on occurrence. Moreover, increasing ecological realism is being incorporated into distribution modelling techniques, focussing on dispersal, biotic interactions and evolutionary constraints that shape species distributions alongside abiotic factors and biases in recording effort, common to pathogens and vectors and wildlife species. Considering pathogens and arthropod vector systems with high impact on plant, animal and human health, the present review describes how biological records for vectors and pathogens arise, introduces the concepts behind distribution models and illustrates the potential for ecologically realistic distribution models to yield insight into the establishment and spread of pathogens. Because distribution modellers aim to provide policy makers with evidence and maps for planning and evaluation of disease mitigation measures, we highlight factors that currently constrain direct translation of models to policy. Disease distributions will be better understood and mapped in the future given improved occurrence data access and integration and combined (correlative and mechanistic) modelling approaches that are developed iteratively in concert with stakeholders.

Niche breadth and geographic range size as determinants of species survival on geological time scales

AbstractAimDetermining which species are more prone to extinction is vital for conservingEarth's biodiversity and for providing insight into macroevolutionary processes. This paper utilizes the Pliocene to Recent fossil record of mollusks to identify determinants of species' extinction over the past three million years ofEarth history.LocationWesternAtlantic.MethodsWe focus on 92 bivalve and gastropod species that lived during the mid‐Pliocene Warm Period (mPWP; ∼3.264–3.025 Ma) and have either since gone extinct or are still extant. We used ecological niche modeling (ENM) to assess the vulnerability of these species to extinction as a function of both fundamental (FN) and realized (RN) niche breadth proxies, geographic range size, and amount of suitable area available to them during the Last Glacial Maximum (LGM; ∼21 Ka).ResultsGeographic range size emerged as a key predictor of extinction for the studied mollusk species, withRNbreadth and amount of suitable area available during theLGMas secondary predictors. By contrast,FNbreadth was not a significant predictor of extinction risk.Main conclusionsThe failure to recoverFNbreadth as a predictor of extinction may suggest that extinction resistance is achieved when species are more successful in filling the geographic extent of their fundamental tolerances. That is, when it comes to species' survival, being a generalist or specialistsensu strictomay be secondary to the unique historical, dispersal, and biotic constraints that dictate a species' occupation of suitable environments, and consequently of geographic space, at a particular time. Identifying the factors that promote extinction is important because of the time‐intensive nature of estimating extinction risk for individual species and populations, and because of the rising concerns about the future of marine ecosystems and biodiversity.

Environmental factors prevail over dispersal constraints in determining the distribution and assembly of Trichoptera species in mountain lakes.

Aiming to elucidate whether large-scale dispersal factors or environmental species sorting prevail in determining patterns of Trichoptera species composition in mountain lakes, we analyzed the distribution and assembly of the most common Trichoptera (Plectrocnemia laetabilis, Polycentropus flavomaculatus, Drusus rectus, Annitella pyrenaea, and Mystacides azurea) in the mountain lakes of the Pyrenees (Spain, France, Andorra) based on a survey of 82 lakes covering the geographical and environmental extremes of the lake district. Spatial autocorrelation in species composition was determined using Moran’s eigenvector maps (MEM). Redundancy analysis (RDA) was applied to explore the influence of MEM variables and in-lake, and catchment environmental variables on Trichoptera assemblages. Variance partitioning analysis (partial RDA) revealed the fraction of species composition variation that could be attributed uniquely to either environmental variability or MEM variables. Finally, the distribution of individual species was analyzed in relation to specific environmental factors using binomial generalized linear models (GLM). Trichoptera assemblages showed spatial structure. However, the most relevant environmental variables in the RDA (i.e., temperature and woody vegetation in-lake catchments) were also related with spatial variables (i.e., altitude and longitude). Partial RDA revealed that the fraction of variation in species composition that was uniquely explained by environmental variability was larger than that uniquely explained by MEM variables. GLM results showed that the distribution of species with longitudinal bias is related to specific environmental factors with geographical trend. The environmental dependence found agrees with the particular traits of each species. We conclude that Trichoptera species distribution and composition in the lakes of the Pyrenees are governed predominantly by local environmental factors, rather than by dispersal constraints. For boreal lakes, with similar environmental conditions, a strong role of dispersal capacity has been suggested. Further investigation should address the role of spatial scaling, namely absolute geographical distances constraining dispersal and steepness of environmental gradients at short distances.

Range contraction and loss of genetic variation of the Pyrenean endemic newt Calotriton asper due to climate change

Many studies have identified climate warming to be among the most important threats to biodiversity. Climate change is expected to have stronger effects on species with low genetic diversity, ectothermic physiology, small ranges, low effective populations sizes, specific habitat requirements and limited dispersal capabilities. Despite an ever-increasing number of studies reporting climate change-induced range shifts, few of these have incorporated species’ specific dispersal constraints into their models. Moreover, the impacts of climate change on genetic variation within populations and species have rarely been assessed, while this is a promising direction for future research. Here we explore the effects of climate change on the potential distribution and genetic variation of the endemic Pyrenean newt Calotriton asper over the period 2020–2080. We use species distribution modelling in combination with high-resolution gridded climate data while subsequently applying four different dispersal scenarios. We furthermore use published data on genetic variation of both mtDNA and AFLP loci to test whether populations with high genetic diversity (nucleotide diversity and expected heterozygosity) or evolutionary history (unique haplotypes and K clusters) have an increased extinction risk from climate change. The present study indicates that climate change drastically reduces the potential distribution range of C. asper and reveals dispersal possibilities to be minimal under the most realistic dispersal scenarios. Despite the major loss in suitable climate, the models highlight relatively large stable areas throughout the species core distribution area indicating persistence of populations over time. The results, however, show a major loss of genetic diversity and evolutionary history. This highlights the importance of accounting for intraspecific genetic variation in climate change impact studies. Likewise, the integration of species’ specific dispersal constraints into projections of species distribution models is an important step to fully explore the effects of climate change on species potential distributions.

Incorporating movement in species distribution models

Movement in the context of species distribution models (SDMs) generally refers to a species’ ability to access suitable habitat. Movement ability can be determined by some combination of dispersal constraints or migration rates, landscape factors such as patch configuration, disturbance, and barriers, and demographic factors related to age at maturity, mortality, and fecundity. Including movement ability can result in more precise projections that help to distinguish suitable habitat that is or can be potentially occupied, from suitable habitat that is inaccessible. While most SDM studies have ignored movement or conceptualized it in overly simplistic ways (e.g. no dispersal versus unlimited dispersal), it is increasingly important to incorporate realistic information on movement ability, particularly for studies that aim to project future distributions such as climate change forecasting and invasive species applications. This progress report addresses the increasingly complex ways in which movement has been incorporated in SDM and outlines directions for further study.

Relaxed dispersion constraints and metamaterial effective parameters with physical meaning for all frequencies

Metamaterial effective parameters may exhibit freedom from typical dispersion constraints. For instance, the emergence of a magnetic response in arrays of split ring resonators for long wavelengths cannot be attained in a passive continuous system obeying the Kramers-Kronig relations. We characterize such freedom by identifying the three possible asymptotes which effective parameters can approach when analytically continued. Apart from their dispersion freedom, we also demonstrate that the effective parameters may be redefined in such a way that they have a certain physical meaning for all frequencies. There exist several possible definitions for the effective permittivity and permeability whereby this is achieved, thereby giving several possible frequency variations for high frequencies, while nevertheless converging to the same dispersion for long wavelengths.

In the right place at the right time: habitat representation in protected areas of South American Nothofagus-dominated plants after a dispersal constrained climate change scenario.

In order to assess the effects of climate change in temperate rainforest plants in southern South America in terms of habitat size, representation in protected areas, considering also if the expected impacts are similar for dominant trees and understory plant species, we used niche modeling constrained by species migration on 118 plant species, considering two groups of dominant trees and two groups of understory ferns. Representation in protected areas included Chilean national protected areas, private protected areas, and priority areas planned for future reserves, with two thresholds for minimum representation at the country level: 10% and 17%. With a 10% representation threshold, national protected areas currently represent only 50% of the assessed species. Private reserves are important since they increase up to 66% the species representation level. Besides, 97% of the evaluated species may achieve the minimum representation target only if the proposed priority areas were included. With the climate change scenario representation levels slightly increase to 53%, 69%, and 99%, respectively, to the categories previously mentioned. Thus, the current location of all the representation categories is useful for overcoming climate change by 2050. Climate change impacts on habitat size and representation of dominant trees in protected areas are not applicable to understory plants, highlighting the importance of assessing these effects with a larger number of species. Although climate change will modify the habitat size of plant species in South American temperate rainforests, it will have no significant impact in terms of the number of species adequately represented in Chile, where the implementation of the proposed reserves is vital to accomplish the present and future minimum representation. Our results also show the importance of using migration dispersal constraints to develop more realistic future habitat maps from climate change predictions.

Dispersal constraints for the conservation of the grassland herb Thymus pulegioides L. in a highly fragmented agricultural landscape

Species-rich grassland communities are one of the most important habitats for biodiversity and of high conservation priority in Europe. Restoration actions are mainly focused on the improvement of abiotic conditions, such as nutrient depletion techniques, and are generally based on the assumption that the target community will re-establish at the restored site when the target species exist in the neighborhood. Information on the contemporary seed-dispersal range is therefore crucial to develop effective conservation measures. Here, we investigated the contemporary long-distance seed dispersal and genetic structure of the grassland herb Thymus pulegioides in an intensively managed agricultural landscape in Flanders (Northern Belgium). Assignment tests based on amplified fragment length polymorphisms revealed very low levels of effective seed dispersal between populations although seed availability and seed viability was not a limiting factor. The process of fragmentation has resulted in a high population differentiation and without further incoming gene flow the remnant populations are prone to further genetic erosion and perhaps extinction. Our findings illustrate that restoring suitable abiotic habitat conditions in the neighborhood of existing populations does likely not guarantee colonization for this grassland specialist. For the survival of the species, existing populations should be functionally connected and seed addition may be necessary for successful conservation to overcome dispersal-limitation.

From dispersal constraints to landscape connectivity: lessons from species distribution modeling

Connectivity plays a crucial role in determining the spread, viability, and persistence of populations across space. Dispersal across landscapes, or the movement of individuals or genes among resource patches, is critical for functional connectivity. Yet current connectivity modelling typically uses information on species location or habitat preference rather than movement, which unfortunately may not capture key dispersal limitations. We argue that recent developments in species distribution modelling provide insightful lessons for addressing this gap and advancing our understanding of connectivity. We suggest shifting the focus of connectivity modelling from locating where animals potentially disperse to a process‐based approach directed towards understanding and mapping factors that limit successful dispersal. To do so, we propose defining species dispersal requirements through identifying spatial, environmental and intrinsic constraints to successful dispersal, analogous to identifying environmental dimensions that define niches. We discuss the benefits of this constraint‐based framework for understanding the distribution of species, predicting species responses to climate change, and connectivity conservation practice. We illustrate how the framework can aid in identifying potential detrimental effects of human activities on connectivity and species persistence, and can spur the implementation of innovative conservation strategies. The proposed framework clarifies the validity and contextual utility of objectives and measures in existing connectivity models, and identifies gaps that may impede our understanding of connectivity and its integration into successful conservation strategies. We expect that this framework will facilitate a mechanistic approach to understanding and conserving connectivity, which will aid in effectively predicting and mitigating effects of ongoing environmental change.