How much noise is too much? Methods for identifying thresholds for soundscape quality and ecosystem services

The United States National Park Service mandate is to conserve park resources and provide superlative visitor experience. In the context of acoustic resources, Denali National Park and Preserve provides an advantageous opportunity to understand the effect of aircraft noise on visitor experience because it possesses high levels of air tour traffic in a park renowned for its remote, wilderness character. Park visitors in four different settings were asked to rate the acceptability of recordings of aircraft noise, presented in randomized order relative to noise level. A cumulative link mixed model fitted visitor assessments to acoustic and nonacoustic factors. In addition to noise level, interest in an air tour was an important predictor of sound clip acceptability. For visitors uninterested in an air tour, the probability of rating aircraft noise as unacceptable at 54 dB LAeq,30 s or higher was 26%. For reference, this aligns with federal guidance that identified 55 dB as a threshold for interference with outdoor activities at rural residences and schools. Predictions of visitor response were joined to a spatial model of aircraft noise propagation to map visitor acceptability of aircraft noise in Denali’s entrance area (frontcountry). This map can be used to assess the condition of park management zones, to inform hiking recommendations for visitors, and to predict the range of soundscape conditions experienced by park visitors Soundscapes Threshold Indicators Aircraft noise Spatial analysis Ecosystem servicespublishedVersio

Noise in ecosystems: a short review

Noise, through its interaction with the nonlinearity of the living systems, can give rise to counter-intuitive phenomena such as stochastic resonance, noise-delayed extinction, temporal oscillations, and spatial patterns. In this paper we briefly review the noise-induced effects in three different ecosystems: (i) two competing species; (ii) three interacting species, one predator and two preys, and (iii) N-interacting species. The transient dynamics of these ecosystems are analyzed through generalized Lotka-Volterra equations in the presence of multiplicative noise, which models the interaction between the species and the environment. The interaction parameter between the species is random in cases (i) and (iii), and a periodical function, which accounts for the environmental temperature, in case (ii). We find noise-induced phenomena such as quasi-deterministic oscillations, stochastic resonance, noise-delayed extinction, and noise-induced pattern formation with nonmonotonic behaviors of patterns areas and of the density correlation as a function of the multiplicative noise intensity. The asymptotic behavior of the time average of the \emph{$i^{th}$} population when the ecosystem is composed of a great number of interacting species is obtained and the effect of the noise on the asymptotic probability distributions of the populations is discussed.Comment: 27 pages, 16 figures. Accepted for publication in Mathematical Biosciences and Engineerin

Noise Induced Phenomena in the Dynamics of Two Competing Species

Noise through its interaction with the nonlinearity of the living systems can give rise to counter-intuitive phenomena. In this paper we shortly review noise induced effects in different ecosystems, in which two populations compete for the same resources. We also present new results on spatial patterns of two populations, while modeling real distributions of anchovies and sardines. The transient dynamics of these ecosystems are analyzed through generalized Lotka-Volterra equations in the presence of multiplicative noise, which models the interaction between the species and the environment. We find noise induced phenomena such as quasi-deterministic oscillations, stochastic resonance, noise delayed extinction, and noise induced pattern formation. In addition, our theoretical results are validated with experimental findings. Specifically the results, obtained by a coupled map lattice model, well reproduce the spatial distributions of anchovies and sardines, observed in a marine ecosystem. Moreover, the experimental dynamical behavior of two competing bacterial populations in a meat product and the probability distribution at long times of one of them are well reproduced by a stochastic microbial predictive model.Comment: 23 pages, 8 figures; to be published in Math. Model. Nat. Phenom. (2016

Resonance and frequency-locking phenomena in spatially extended phytoplankton-zooplankton system with additive noise and periodic forces

In this paper, we present a spatial version of phytoplankton-zooplankton model that includes some important factors such as external periodic forces, noise, and diffusion processes. The spatially extended phytoplankton-zooplankton system is from the original study by Scheffer [M Scheffer, Fish and nutrients interplay determines algal biomass: a minimal model, Oikos \textbf{62} (1991) 271-282]. Our results show that the spatially extended system exhibit a resonant patterns and frequency-locking phenomena. The system also shows that the noise and the external periodic forces play a constructive role in the Scheffer's model: first, the noise can enhance the oscillation of phytoplankton species' density and format a large clusters in the space when the noise intensity is within certain interval. Second, the external periodic forces can induce 4:1 and 1:1 frequency-locking and spatially homogeneous oscillation phenomena to appear. Finally, the resonant patterns are observed in the system when the spatial noises and external periodic forces are both turned on. Moreover, we found that the 4:1 frequency-locking transform into 1:1 frequency-locking when the noise intensity increased. In addition to elucidating our results outside the domain of Turing instability, we provide further analysis of Turing linear stability with the help of the numerical calculation by using the Maple software. Significantly, oscillations are enhanced in the system when the noise term presents. These results indicate that the oceanic plankton bloom may partly due to interplay between the stochastic factors and external forces instead of deterministic factors. These results also may help us to understand the effects arising from undeniable subject to random fluctuations in oceanic plankton bloom.Comment: Some typos errors are proof, and some strong relate references are adde

Mapping and assessment of ecosystems and their services. Urban ecosystems

Action 5 of the EU Biodiversity Strategy to 2020 requires member states to Map and Assess the state of Ecosystems and their Services (MAES). This report provides guidance for mapping and assessment of urban ecosystems. The MAES urban pilot is a collaboration between the European Commission, the European Environment Agency, volunteering Member States and cities, and stakeholders. Its ultimate goal is to deliver a knowledge base for policy and management of urban ecosystems by analysing urban green infrastructure, condition of urban ecosystems and ecosystem services. This report presents guidance for mapping urban ecosystems and includes an indicator framework to assess the condition of urban ecosystems and urban ecosystem services. The scientific framework of mapping and assessment is designed to support in particular urban planning policy and policy on green infrastructure at urban, metropolitan and regional scales. The results are based on the following different sources of information: a literature survey of 54 scientific articles, an online-survey (on urban ecosystems, related policies and planning instruments and with participation of 42 cities), ten case studies (Portugal: Cascais, Oeiras, Lisbon; Italy: Padua, Trento, Rome; The Netherlands: Utrecht; Poland: Poznań; Spain: Barcelona; Norway: Oslo), and a two-day expert workshop. The case studies constituted the core of the MAES urban pilot. They provided real examples and applications of how mapping and assessment can be organized to support policy; on top, they provided the necessary expertise to select a set of final indicators for condition and ecosystem services. Urban ecosystems or cities are defined here as socio-ecological systems which are composed of green infrastructure and built infrastructure. Urban green infrastructure (GI) is understood in this report as the multi-functional network of urban green spaces situated within the boundary of the urban ecosystem. Urban green spaces are the structural components of urban GI. This study has shown that there is a large scope for urban ecosystem assessments. Firstly, urban policies increasingly use urban green infrastructure and nature-based solutions in their planning process. Secondly, an increasing amount of data at multiple spatial scales is becoming available to support these policies, to provide a baseline, and to compare or benchmark cities with respect to the extent and management of the urban ecosystem. Concrete examples are given on how to delineate urban ecosystems, how to choose an appropriate spatial scale, and how to map urban ecosystems based on a combination of national or European datasets (including Urban Atlas) and locally collected information (e.g., location of trees). Also examples of typologies for urban green spaces are presented. This report presents an indicator framework which is composed of indicators to assess for urban ecosystem condition and for urban ecosystem services. These are the result of a rigorous selection process and ensure consistent mapping and assessment across Europe. The MAES urban pilot will continue with work on the interface between research and policy. The framework presented in this report needs to be tested and validated across Europe, e.g. on its applicability at city scale, on how far the methodology for measuring ecosystem condition and ecosystem service delivery in urban areas can be used to assess urban green infrastructure and nature-based solutions

Role of the Colored Noise in Spatio-Temporal Behavior of Two Competing Species

We study the spatial distributions of two randomly interacting species, in the presence of an external multiplicative colored noise. The dynamics of the ecosystem is described by a coupled map lattice model. We find a nonmonotonic behavior in the formation of large scale spatial correlations as a function of the multiplicative colored noise intensity. This behavior is shifted towards higher values of the noise intensity for increasing correlation time of the noise.Comment: 6 pages, 3 figure

Moving forward in circles: challenges and opportunities in modelling population cycles

Population cycling is a widespread phenomenon, observed across a multitude of taxa in both laboratory and natural conditions. Historically, the theory associated with population cycles was tightly linked to pairwise consumer–resource interactions and studied via deterministic models, but current empirical and theoretical research reveals a much richer basis for ecological cycles. Stochasticity and seasonality can modulate or create cyclic behaviour in non-intuitive ways, the high-dimensionality in ecological systems can profoundly influence cycling, and so can demographic structure and eco-evolutionary dynamics. An inclusive theory for population cycles, ranging from ecosystem-level to demographic modelling, grounded in observational or experimental data, is therefore necessary to better understand observed cyclical patterns. In turn, by gaining better insight into the drivers of population cycles, we can begin to understand the causes of cycle gain and loss, how biodiversity interacts with population cycling, and how to effectively manage wildly fluctuating populations, all of which are growing domains of ecological research

Empirical analysis of vegetation dynamics and the possibility of a catastrophic desertification transition

The process of desertification in the semi-arid climatic zone is considered by many as a catastrophic regime shift, since the positive feedback of vegetation density on growth rates yields a system that admits alternative steady states. Some support to this idea comes from the analysis of static patterns, where peaks of the vegetation density histogram were associated with these alternative states. Here we present a large-scale empirical study of vegetation dynamics, aimed at identifying and quantifying directly the effects of positive feedback. To do that, we have analyzed vegetation density across $~2.5 \times 10^6 \ \rm{km}^2$ of the African Sahel region, with spatial resolution of $30 \times 30$ meters, using three consecutive snapshots. The results are mixed. The local vegetation density (measured at a single pixel) moves towards the average of the corresponding rainfall line, indicating a purely negative feedback. On the other hand, the chance of spatial clusters (of many "green" pixels) to expand in the next census is growing with their size, suggesting some positive feedback. We show that these apparently contradicting results emerge naturally in a model with positive feedback and strong demographic stochasticity, a model that allows for a catastrophic shift only in a certain range of parameters. Static patterns, like the double peak in the histogram of vegetation density, are shown to vary between censuses, with no apparent correlation with the actual dynamical features

Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models