Spatial and spatiotemporal variation in metapopulation structure affects population dynamics in a passively dispersing arthropod

The spatial and temporal variation in the availability of suitable habitat within metapopulations determines colonization-extinction events, regulates local population sizes and eventually affects local population and metapopulation stability. Insights into the impact of such a spatiotemporal variation on the local population and metapopulation dynamics are principally derived from classical metapopulation theory and have not been experimentally validated. By manipulating spatial structure in artificial metapopulations of the spider mite Tetranychus urticae, we test to which degree spatial (mainland-island metapopulations) and spatiotemporal variation (classical metapopulations) in habitat availability affects the dynamics of the metapopulations relative to systems where habitat is constantly available in time and space (patchy metapopulations). Our experiment demonstrates that (i) spatial variation in habitat availability decreases variance in metapopulation size and decreases density-dependent dispersal at the metapopulation level, while (ii) spatiotemporal variation in habitat availability increases patch extinction rates, decreases local population and metapopulation sizes and decreases density dependence in population growth rates. We found dispersal to be negatively density dependent and overall low in the spatial variable mainland-island metapopulation. This demographic variation subsequently impacts local and regional population dynamics and determines patterns of metapopulation stability. Both local and metapopulation-level variabilities are minimized in mainland-island metapopulations relative to classical and patchy ones

Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach

Adversarial training (AT) is a canonical method for enhancing the robustness of deep neural networks (DNNs). However, recent studies empirically demonstrated that it suffers from robust overfitting, i.e., a long time AT can be detrimental to the robustness of DNNs. This paper presents a theoretical explanation of robust overfitting for DNNs. Specifically, we non-trivially extend the neural tangent kernel (NTK) theory to AT and prove that an adversarially trained wide DNN can be well approximated by a linearized DNN. Moreover, for squared loss, closed-form AT dynamics for the linearized DNN can be derived, which reveals a new AT degeneration phenomenon: a long-term AT will result in a wide DNN degenerates to that obtained without AT and thus cause robust overfitting. Based on our theoretical results, we further design a method namely Adv-NTK, the first AT algorithm for infinite-width DNNs. Experiments on real-world datasets show that Adv-NTK can help infinite-width DNNs enhance comparable robustness to that of their finite-width counterparts, which in turn justifies our theoretical findings. The code is available at https://github.com/fshp971/adv-ntk

Dispersal and metapopulation stability

International audienc

Composition of 'fast-slow' traits drives avian community stability over North America

1. Rapid biodiversity loss has triggered decades of research on the relationships between biodiversity and community stability. Recent studies highlighted the importance of species traits for understanding biodiversity-stability relationships. The species with high growth rates ('fast' species) are expected to be less resistant to environmental stress but recover faster if disturbed; in contrast, the species with slow growth rates ('slow' species) can be more resistant but recover more slowly if disturbed. Such a 'fast-slow' trait continuum provides a new perspective for understanding community stability, but its validity has mainly been examined in plant communities. Here, we investigate how 'fast-slow' trait composition, together with species richness and environmental factors, regulate avian community stability at a continental scale. 2. We used bird population records from the North American Breeding Bird Survey during 1988-2017 and defined avian community stability as the temporal invariability of total community biomass. We calculated species richness and the community-weighted mean (CWM) and functional diversity (FD) of four key life-history traits, including body size, nestling period (i.e. period of egg incubation and young bird fledging), life span and clutch size (i.e. annual total number of eggs). Environmental factors included temperature, precipitation and leaf area index (LAI). 3. Our analyses showed that avian community stability was mainly driven by the CWM of the 'fast-slow' trait. Communities dominated by 'fast' species (i.e. species with small body size, short nestling period and life span and large clutch size) were more stable than those dominated by 'slow' species (i.e. species with large body size, long nestling period and life span and small clutch size). Species richness and the FD of the 'fast-slow' trait explained much smaller proportions of variation in avian community stability. Temperature had direct positive effects on avian community stability, while precipitation and leaf area index affected community stability indirectly by influencing species richness and trait composition. 4. Our study demonstrates that composition of 'fast-slow' traits is the major biotic driver of avian community stability over North America. Temperature is the most important abiotic factor, but its effect is weaker than that of the 'fast-slow' trait. An integrated framework combining 'fast-slow' trait composition and temperature is needed to understand the response of avian communities in a changing environment.Peer reviewe

Multispecies coexistence in fragmented landscapes

Spatial dynamics have long been recognized as an important driver of biodiversity. However, our understanding of species’ coexistence under realistic landscape configurations has been limited by lack of adequate analytical tools. To fill this gap, we develop a spatially explicit metacommunity model of multiple competing species and derive analytical criteria for their coexistence in fragmented heterogeneous landscapes. Specifically, we propose measures of niche and fitness differences for metacommunities, which clarify how spatial dynamics and habitat configuration interact with local competition to determine coexistence of species. We parameterize our model with a Bayesian approach using a 36-y time-series dataset of three Daphnia species in a rockpool metacommunity covering >500 patches. Our results illustrate the emergence of interspecific variation in extinction and recolonization processes, including their dependencies on habitat size and environmental temperature. We find that such interspecific variation contributes to the coexistence of Daphnia species by reducing fitness differences and increasing niche differences. Additionally, our parameterized model allows separating the effects of habitat destruction and temperature change on species extinction. By integrating coexistence theory and metacommunity theory, our study provides platforms to increase our understanding of species’ coexistence in fragmented heterogeneous landscapes and the response of biodiversity to environmental changes

Multispecies coexistence in fragmented landscapes

Publisher Copyright: Copyright © 2022 the Author(s). Published by PNAS.Spatial dynamics have long been recognized as an important driver of biodiversity. However, our understanding of species’ coexistence under realistic landscape configurations has been limited by lack of adequate analytical tools. To fill this gap, we develop a spatially explicit metacommunity model of multiple competing species and derive analytical criteria for their coexistence in fragmented heterogeneous landscapes. Specifically, we propose measures of niche and fitness differences for metacommunities, which clarify how spatial dynamics and habitat configuration interact with local competition to determine coexistence of species. We parameterize our model with a Bayesian approach using a 36-y time-series dataset of three Daphnia species in a rockpool metacommunity covering >500 patches. Our results illustrate the emergence of interspecific variation in extinction and recolonization processes, including their dependencies on habitat size and environmental temperature. We find that such interspecific variation contributes to the coexistence of Daphnia species by reducing fitness differences and increasing niche differences. Additionally, our parameterized model allows separating the effects of habitat destruction and temperature change on species extinction. By integrating coexistence theory and metacommunity theory, our study provides platforms to increase our understanding of species’ coexistence in fragmented heterogeneous landscapes and the response of biodiversity to environmental changes.Peer reviewe

A prediction model of specific productivity index using least square support vector machine method

In the design of oilfield development plans, specific productivity index plays a vital role. Especially for offshore oilfields, affected by development costs and time limits, there are shortcomings of shorter test time and fewer test sampling points. Therefore, it is very necessary to predict specific productivity index. In this study, a prediction model of the specific productivity index is established by combining the principle of least squares support vector machine (LS-SVM) with the calculation method of the specific productivity index. The model uses logging parameters, crude oil experimental parameters and the specific productivity index of a large number of test well samples as input and output items respectively, and finally predicts the specific productivity index of non-test wells. It reduces the errors caused by short training time, randomness of training results and insufficient learning. A large number of sample data from the Huanghekou Sag in Bohai Oilfield were used to verify the prediction model. Comparing the specific productivity index prediction results of LS-SVM and artificial neural networks (ANNs) with actual well data respectively, the LS-SVM model has a better fitting effect, with an error of only 3.2%, which is 12.1% lower than ANNs. This study can better reflect the impact of different factors on specific productivity index, and it has important guiding significance for the evaluation of offshore oilfield productivity.Cited as: Wu, C., Wang, S., Yuan, J., Li, C., Zhang, Q. A prediction model of specific productivity index using least square support vector machine method. Advances in Geo-Energy Research, 2020, 4(4): 460-467, doi: 10.46690/ager.2020.04.1