High mortality and enhanced recovery: modelling the countervailing effects of disturbance on population dynamics

Matrix community models for ecology and evolution

Ecological communities are shaped by biotic interactions as well as environmental forces, and both must be incorporated to obtain models capable of forecasting realistic community dynamics. Many community models first specify pairwise biotic interactions and then secondarily examine how extrinsic factors such as abiotic conditions affect species abundances. A disadvantage of this approach is that the species interactions themselves are often environment and context specific, making parameterization difficult. We propose an alternative approach, matrix community models (MCMs), which are sets of matrix population models linked by an assumption of aggregate density dependence. MCMs incorporate detailed species autecology but are neutral with respect to pairwise species interactions, instead allowing interactions to be revealed within the model structure. These model-revealed species interactions, including competitive exclusion, facilitation, and interference competition, shape the distribution and abundance of species within communities and generate empirically testable predictions about species interactions. We develop a framework for building MCMs using vital rates in a stochastic, multispecies framework. Single-species matrix population models are connected via an assumption of aggregate density dependence, pairwise species interactions are estimated with sensitivity analysis, and community trajectories are analyzed under different environmental regimes using standard statistical tools and network analysis. MCMs have the advantage that pairwise species interactions need not be specified a priori, and that mechanistic demographic-environment linkages permit forecasting of community dynamics under novel, non-stationary environmental regimes. A challenge is that species' autecological vital rates, such as fecundity, growth and survivorship, must be measured under a diverse range of environmental conditions to parameterize the models. We illustrate the approach with examples and discuss prospects for future theoretical and empirical developments.

Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience

Directly observing autotrophic biomass at ecologically relevant frequencies is difficult in many ecosystems, hampering our ability to predict productivity through time. Since disturbances can impart distinct reductions in river productivity through time by modifying underlying standing stocks of biomass, mechanistic models fit to productivity time series can infer underlying biomass dynamics. We incorporated biomass dynamics into a river ecosystem productivity model for six rivers to identify disturbance flow thresholds and understand the resilience of primary producers. The magnitude of flood necessary to disturb biomass and thereby reduce ecosystem productivity was consistently lower than the more commonly used disturbance flow threshold of the flood magnitude necessary to mobilize river bed sediment. The estimated daily maximum percent increase in biomass (a proxy for resilience) ranged from 5% to 42% across rivers. Our latent biomass model improves understanding of disturbance thresholds and recovery patterns of autotrophic biomass within river ecosystems.

Timing and magnitude of climatic extremes differentially elevate mortality but enhance recovery in a fish population

The countervailing effects of disturbances (e.g., high mortality and enhanced recovery) on population dynamics can occur through demographic processes under rapidly increasing climatic extremes. Across an extreme-event gradient, we mechanistically demonstrated how dramatic changes in streamflow have affected the population persistence of endangered salmon in monsoonal Taiwan over a three-decade period. Our modeling indicated that the dynamics of the age-structured population were attributed to demographic processes, in which extensive mortality was characterized as a function of climatic extremes and vulnerability in the young stage of fish. In the stochastic simulations, we found that the extensive mortality and high proportion of large fish resulted from extreme flooding, which caused high values of postimpact population recovery. Our empirical evidence suggests that the magnitudes and timing of disturbance can explain the population persistence when facing climatic extremes and thereby challenges the understanding of the mechanistic drivers of these countervailing phenomena under changing environmental conditions.

Integrating resilience with functional ecosystem measures: A novel paradigm for management decisions under multiple-stressor interplay in freshwater ecosystems

Moving beyond monitoring the state of water quality to understanding how the sensitive ecosystems "respond" to complex interplay of climatic and anthropogenic perturbations, and eventually the mechanisms that underpin alterations leading to transitional shifts is crucial for managing freshwater resources. The multiple disturbance dynamics-a single disturbance as opposed to multiple disturbances for recovery and other atrocities-alter aquatic ecosystem in multiple ways, yet the global models lack representation of key processes and feedbacks, impeding potential management decisions. Here, the procedure we have embarked for what is known about the biogeochemical and ecological functions in freshwaters in context of ecosystem resilience, feedbacks, stressors synergies, and compensatory dynamics, is highly relevant for process-based ecosystem models and for developing a novel paradigm toward potential management decisions. This review advocates the need for a more aggressive approach with improved understanding of changes in key ecosystem processes and mechanistic links thereof, regulating resilience and compensatory dynamics concordant with climate and anthropogenic perturbations across a wide range of spatio-temporal scales. This has relevance contexting climate change and anthropogenic pressures for developing proactive and adaptive management strategies for safeguarding freshwater resources and services they provide.

Toward a Generalizable Framework of Disturbance Ecology Through Crowdsourced Science

Disturbances fundamentally alter ecosystem functions, yet predicting their impacts remains a key scientific challenge. While the study of disturbances is ubiquitous across many ecological disciplines, there is no agreed-upon, cross-disciplinary foundation for discussing or quantifying the complexity of disturbances, and no consistent terminology or methodologies exist. This inconsistency presents an increasingly urgent challenge due to accelerating global change and the threat of interacting disturbances that can destabilize ecosystem responses. By harvesting the expertise of an interdisciplinary cohort of contributors spanning 42 institutions across 15 countries, we identified an essential limitation in disturbance ecology: the word ‘disturbance’ is used interchangeably to refer to both the events that cause, and the consequences of, ecological change, despite fundamental distinctions between the two meanings. In response, we developed a generalizable framework of ecosystem disturbances, providing a well-defined lexicon for understanding disturbances across perspectives and scales. The framework results from ideas that resonate across multiple scientific disciplines and provides a baseline standard to compare disturbances across fields. This framework can be supplemented by discipline-specific variables to provide maximum benefit to both inter- and intra-disciplinary research. To support future syntheses and meta-analyses of disturbance research, we also encourage researchers to be explicit in how they define disturbance drivers and impacts, and we recommend minimum reporting standards that are applicable regardless of scale. Finally, we discuss the primary factors we considered when developing a baseline framework and propose four future directions to advance our interdisciplinary understanding of disturbances and their social-ecological impacts: integrating across ecological scales, understanding disturbance interactions, establishing baselines and trajectories, and developing process-based models and ecological forecasting initiatives. Our experience through this process motivates us to encourage the wider scientific community to continue to explore new approaches for leveraging Open Science principles in generating creative and multidisciplinary ideas.

Optimal Network Architectures for Spatially Structured Populations with Heterogeneous Diffusion

The motivation of this article is to derive new management guidelines to maximize the overall population size using popular management and conservation strategies, such as protected marine areas and ecological corridors. These guidelines are based on the identification of the network architectures for which the total population size is maximized. Describing the biological roles of the typical network variables in the fate of the population is a classic problem with many practical applications. This article suggests that the optimal network architecture relies heavily on the degree of mobility of the population. The recommended network architecture for populations with reduced mobility (in the absence of cost of dispersal and landscapes made up of many sources) is a graph with a patch that has routes toward any other patch with a lower growth rate. However, for highly mobile populations there are many possible network architectures for which the total population size is maximized (e.g., any cyclic graph). We have paid special attention to species with symmetric movement in heterogeneous landscapes. A striking result is that the network architecture does not have any influence on the total population size for highly mobile populations when any pair of different patches can be connected by a sequence of paths.

Management guidelines in disturbance-Prone populations: The importance of the intervention time

The use of conservation and management practices to buffer possible damages after disturbance events is growing to become popular worldwide. However, little is known about their efficacy in real-life situations. To fill this gap, we will derive management guidelines in disturbance-prone populations regarding the external introduction of individuals and the ecological restoration. We will also discuss the efficacy of these practices in the population dynamics of three species (a fast life-cycle mayfly, a slow life-cycle dragonfly and an ostracod) when their habitat suffers from periodic controlled flooding. One of the main messages of this paper is that the interplay between the inherited parameters of the population and disturbance events is a source of rich and unexpected behaviours. More importantly, intervention time plays a critical role in the performance of some management strategies.

Modeling Flow-Ecology Responses in the Anthropocene: Challenges for Sustainable Riverine Management

Climate change will increase water stress in many regions placing greater pressures on rivers to meet human and ecological water needs. Managing rivers experiencing water stress requires a fundamental understanding of how ecosystem processes and functions respond to natural and anthropogenic drivers of flow variability and change. The field of environmental flows meets this need by defining “flow-ecology” relationships—mathematical models linking ecological characteristics and dynamics to the underlying flow regime. However, because these relationships are most often based on historical hydrologic regimes, they implicitly assume climatic stationarity. A fundamental challenge in the Anthropocene is how to model flow-ecology relationships such that the effects of nonstationarity can be captured. In the present article, we introduce a novel approach that addresses these shortcomings and show its utility through a series of conceptual and empirical examples. The framework incorporates ecological dynamics and uncertain future hydrologic conditions, as well as nonstationarity itself, thereby providing a viable framework for modeling flow-ecology responses to inform water management in a rapidly changing climate.

Fire history influences large-herbivore behavior at circadian, seasonal, and successional scales

Recurrent environmental changes often prompt animals to alter their behavior leading to predictable patterns across a range of temporal scales. The nested nature of circadian and seasonal behavior complicates tests for effects of rarer disturbance events like fire. Fire can dramatically alter plant community structure, with important knock-on effects at higher trophic levels, but the strength and timing of fire's effects on herbivores remain unclear. We combined prescribed fire treatments with fine-scale location data to quantify herbivore responses to fire across three temporal scales. Between 2001 and 2003, 26 stands of fir (Abies spp.) and Douglas-fir (Pseudotsuga menziesii) were thinned and burned; 27 similar stands were left untreated as experimental controls. Analyzing female elk (Cervus canadensis) locations across 21 yr (1996-2016), we found crepuscular, seasonal, and successional shifts in behavioral responses to fire. Elk displayed "commuting" behavior, avoiding burns during the day, but selecting them at night. Elk selection for burns was strongest in early summer and the relative probability of elk using burns peaked quickly (5 yr post burn) before gradually returning to pre-treatment levels (15 yr post burn). Our results demonstrate that fire history has complex, persistent effects on herbivore behavior, and suggest that herbivores benefit from heterogeneous landscapes containing a range of successional stages.

Do latitudinal gradients exist in New Zealand stream invertebrate metacommunities?

That biodiversity declines with latitude is well known, but whether a metacommunity process is behind this gradient has received limited attention. We tested the hypothesis that dispersal limitation is progressively replaced by mass effects with increasing latitude, along with a series of related hypotheses. We explored these hypotheses by examining metacommunity structure in stream invertebrate metacommunities spanning the length of New Zealand’s two largest islands (∼1,300 km), further disentangling the role of dispersal by deconstructing assemblages into strong and weak dispersers. Given the highly dynamic nature of New Zealand streams, our alternative hypothesis was that these systems are so unpredictable (at different stages of post-flood succession) that metacommunity structure is highly context dependent from region to region. We rejected our primary hypotheses, pinning this lack of fit on the strong unpredictability of New Zealand’s dynamic stream ecosystems and fauna that has evolved to cope with these conditions. While local community structure turned over along this latitudinal gradient, metacommunity structure was highly context dependent and dispersal traits did not elucidate patterns. Moreover, the emergent metacommunity types exhibited no trends, nor did the important environmental variables. These results provide a cautionary tale for examining singular metacommunities. The considerable level of unexplained contingency suggests that any inferences drawn from one-off snapshot sampling may be misleading and further points to the need for more studies on temporal dynamics of metacommunity processes.

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

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

Critical thresholds for eventual extinction in randomly disturbed population growth models

This paper considers several single species growth models featuring a carrying capacity, which are subject to random disturbances that lead to instantaneous population reduction at the disturbance times. This is motivated in part by growing concerns about the impacts of climate change. Our main goal is to understand whether or not the species can persist in the long run. We consider the discrete-time stochastic process obtained by sampling the system immediately after the disturbances, and find various thresholds for several modes of convergence of this discrete process, including thresholds for the absence or existence of a positively supported invariant distribution. These thresholds are given explicitly in terms of the intensity and frequency of the disturbances on the one hand, and the population's growth characteristics on the other. We also perform a similar threshold analysis for the original continuous-time stochastic process, and obtain a formula that allows us to express the invariant distribution for this continuous-time process in terms of the invariant distribution of the discrete-time process, and vice versa. Examples illustrate that these distributions can differ, and this sends a cautionary message to practitioners who wish to parameterize these and related models using field data. Our analysis relies heavily on a particular feature shared by all the deterministic growth models considered here, namely that their solutions exhibit an exponentially weighted averaging property between a function of the initial condition, and the same function applied to the carrying capacity. This property is due to the fact that these systems can be transformed into affine systems.