High-integrity human intervention in ecosystems: Tracking self-organization modes.

Humans play major roles in shaping and transforming the ecology of Earth. Unlike natural drivers of ecosystem change, which are erratic and unpredictable, human intervention in ecosystems generally involves planning and management, but often results in detrimental outcomes. Using model studies and aerial-image analysis, we argue that the design of a successful human intervention form calls for the identification of the self-organization modes that drive ecosystem change, and for studying their dynamics. We demonstrate this approach with two examples: grazing management in drought-prone ecosystems, and rehabilitation of degraded vegetation by water harvesting. We show that grazing can increase the resilience to droughts, rather than imposing an additional stress, if managed in a spatially non-uniform manner, and that fragmental restoration along contour bunds is more resilient than the common practice of continuous restoration in vegetation stripes. We conclude by discussing the need for additional studies of self-organization modes and their dynamics.

The orchard plot: Cultivating a forest plot for use in ecology, evolution, and beyond.

"Classic" forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution, meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a "forest-like plot," showing point estimates (with 95% confidence intervals [CIs]) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the "orchard plot." Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also include 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

Beyond the forest plot: The drapery plot.

In the era of the "reproducibility crisis" and the "P-value controversy" new ways of presentation and interpretation of the results of a meta-analysis are desirable. One suggestion that has been made for single studies almost six decades ago and taken up now and then is the P-value function. For a given outcome, this function assigns a P-value to each possible hypothetical value, given the data. Moreover, the P-value function simultaneously provides two-sided confidence intervals for all possible alpha levels. An application to meta-analysis, while suggested early, has not been widely established. We introduce the drapery plot that presents the P-value function for all individual studies and pooled estimates in a meta-analysis as curves and the prediction range for a single future study. We also present a scaled variant with the test statistic on the y-axis. Both plots visualize the full information of a pairwise meta-analysis. We see a drapery plot as a complementary figure to a forest plot. It may be even an alternative in meta-analyses with many studies where forest plots tend to become very large and complex.

Area-based conservation in the twenty-first century.

Humanity will soon define a new era for nature-one that seeks to transform decades of underwhelming responses to the global biodiversity crisis. Area-based conservation efforts, which include both protected areas and other effective area-based conservation measures, are likely to extend and diversify. However, persistent shortfalls in ecological representation and management effectiveness diminish the potential role of area-based conservation in stemming biodiversity loss. Here we show how the expansion of protected areas by national governments since 2010 has had limited success in increasing the coverage across different elements of biodiversity (ecoregions, 12,056 threatened species, 'Key Biodiversity Areas' and wilderness areas) and ecosystem services (productive fisheries, and carbon services on land and sea). To be more successful after 2020, area-based conservation must contribute more effectively to meeting global biodiversity goals-ranging from preventing extinctions to retaining the most-intact ecosystems-and must better collaborate with the many Indigenous peoples, community groups and private initiatives that are central to the successful conservation of biodiversity. The long-term success of area-based conservation requires parties to the Convention on Biological Diversity to secure adequate financing, plan for climate change and make biodiversity conservation a far stronger part of land, water and sea management policies.

Performance of spatial capture-recapture models with repurposed data: Assessing estimator robustness for retrospective applications.

Advancements in statistical ecology offer the opportunity to gain further inferences from existing data with minimal financial cost. Spatial capture-recapture (SCR) models extend traditional capture-recapture models to incorporate spatial position of capture and enable direct estimation of animal densities across a region of interest. The additional inferences provided are both ecologically interesting and valuable for decision making, which has resulted in traditional capture-recapture data being repurposed using SCR. Yet, many capture-recapture studies were not designed for SCR and the limitations of repurposing data from such studies are rarely assessed in practice. We used simulation to evaluate the robustness of SCR for retrospectively estimating large mammal densities over a variety of scenarios using repurposed capture-recapture data collected by an asymmetrical sampling grid and covering a broad spatial extent in a heterogenous landscape. We found performance of SCR models fit using repurposed data simulated from the existing grid was not robust, but instead bias and precision of density estimates varied considerably among simulations scenarios. For example, while the smallest relatives bias of density estimates was 3%, it ranged by 14 orders of magnitude among scenarios and was most strongly influenced by detection parameters. Our results caution against the casual repurposing of non-spatial capture-recapture data using SCR and demonstrate the importance of using simulation to assessing model performance during retrospective applications.

Population Dynamics with Threshold Effects Give Rise to a Diverse Family of Allee Effects.

The Allee effect describes populations that deviate from logistic growth models and arises in applications including ecology and cell biology. A common justification for incorporating Allee effects into population models is that the population in question has altered growth mechanisms at some critical density, often referred to as a threshold effect. Despite the ubiquitous nature of threshold effects arising in various biological applications, the explicit link between local threshold effects and global Allee effects has not been considered. In this work, we examine a continuum population model that incorporates threshold effects in the local growth mechanisms. We show that this model gives rise to a diverse family of Allee effects, and we provide a comprehensive analysis of which choices of local growth mechanisms give rise to specific Allee effects. Calibrating this model to a recent set of experimental data describing the growth of a population of cancer cells provides an interpretation of the threshold population density and growth mechanisms associated with the population.

Scale-invariant topology and bursty branching of evolutionary trees emerge from niche construction.

Phylogenetic trees describe both the evolutionary process and community diversity. Recent work has established that they exhibit scale-invariant topology, which quantifies the fact that their branching lies in between the two extreme cases of balanced binary trees and maximally unbalanced ones. In addition, the backbones of phylogenetic trees exhibit bursts of diversification on all timescales. Here, we present a simple, coarse-grained statistical model of niche construction coupled to speciation. Finite-size scaling analysis of the dynamics shows that the resultant phylogenetic tree topology is scale-invariant due to a singularity arising from large niche construction fluctuations that follow extinction events. The same model recapitulates the bursty pattern of diversification in time. These results show how dynamical scaling laws of phylogenetic trees on long timescales can reflect the indelible imprint of the interplay between ecological and evolutionary processes.

Determining the optimal coefficient of the spatially periodic Fisher-KPP equation that minimizes the spreading speed.

This paper is concerned with the spatially periodic Fisher-KPP equation [Formula: see text], [Formula: see text], where d(x) and r(x) are periodic functions with period [Formula: see text]. We assume that r(x) has positive mean and [Formula: see text]. It is known that there exists a positive number [Formula: see text], called the minimal wave speed, such that a periodic traveling wave solution with average speed c exists if and only if [Formula: see text]. In the one-dimensional case, the minimal speed [Formula: see text] coincides with the "spreading speed", that is, the asymptotic speed of the propagating front of a solution with compactly supported initial data. In this paper, we study the minimizing problem for the minimal speed [Formula: see text] by varying r(x) under a certain constraint, while d(x) arbitrarily. We have been able to obtain an explicit form of the minimizing function r(x). Our result provides the first calculable example of the minimal speed for spatially periodic Fisher-KPP equations as far as the author knows.

Some Interval-Valued Intuitionistic Fuzzy Dombi Heronian Mean Operators and their Application for Evaluating the Ecological Value of Forest Ecological Tourism Demonstration Areas.

With China's sustained economic development and constant increase in national income, Chinese nationals' tourism consumption rate increases. As a major Chinese economic development engine, the domestic tourism industry has entered a transition period operation pattern featured by diversified products. Among them, as a new hot spot of the tourism industry in China, ecological tourism has enjoyed rapid development, with great potential. Thus, the ecological value evaluation of forest ecological tourism demonstration areas is very important to the domestic tourism industry. In this paper, we propose some Dombi Heronian mean operators with interval-valued intuitionistic fuzzy numbers (IVIFNs). Then, two MADM (multiple attribute decision making) methods are proposed based on IVIFWDHM (interval-valued intuitionistic fuzzy weighted Dombi Heronian mean) and IVIFWDGHM (interval-valued intuitionistic weighted Dombi geometric Heronian mean) operators. Finally, we gave an experimental case for evaluating the ecological value of forest ecological tourism demonstration area to show the proposed decision methods.

Multinomial N-mixture models for removal sampling.

Multinomial N -mixture models are commonly used to fit data from a removal sampling protocol. If the mixing distribution is negative binomial, the distribution of the counts does not appear to have been identified, and practitioners approximate the requisite likelihood by placing an upper bound on the embedded infinite sum. In this paper, the distribution which underpins the multinomial N -mixture model with a negative binomial mixing distribution is shown to belong to the broad class of multivariate negative binomial distributions. Specifically, the likelihood can be expressed in closed form as the product of conditional and marginal likelihoods and the information matrix shown to be block diagonal. As a consequence, the nature of the maximum likelihood estimates of the unknown parameters and their attendant standard errors can be examined and tests of the hypothesis of the Poisson against the negative binomial mixing distribution formulated. In addition, appropriate multinomial N -mixture models for data sets which include zero site totals can also be constructed. Two illustrative examples are provided.

Inference in MCMC step selection models.

Habitat selection models are used in ecology to link the spatial distribution of animals to environmental covariates and identify preferred habitats. The most widely used models of this type, resource selection functions, aim to capture the steady-state distribution of space use of the animal, but they assume independence between the observed locations of an animal. This is unrealistic when location data display temporal autocorrelation. The alternative approach of step selection functions embed habitat selection in a model of animal movement, to account for the autocorrelation. However, inferences from step selection functions depend on the underlying movement model, and they do not readily predict steady-state space use. We suggest an analogy between parameter updates and target distributions in Markov chain Monte Carlo (MCMC) algorithms, and step selection and steady-state distributions in movement ecology, leading to a step selection model with an explicit steady-state distribution. In this framework, we explain how maximum likelihood estimation can be used for simultaneous inference about movement and habitat selection. We describe the local Gibbs sampler, a novel rejection-free MCMC scheme, use it as the basis of a flexible class of animal movement models, and derive its likelihood function for several important special cases. In a simulation study, we verify that maximum likelihood estimation can recover all model parameters. We illustrate the application of the method with data from a zebra.

Pervasive decreases in living vegetation carbon turnover time across forest climate zones.

Forests play a major role in the global carbon cycle. Previous studies on the capacity of forests to sequester atmospheric CO2 have mostly focused on carbon uptake, but the roles of carbon turnover time and its spatiotemporal changes remain poorly understood. Here, we used long-term inventory data (1955 to 2018) from 695 mature forest plots to quantify temporal trends in living vegetation carbon turnover time across tropical, temperate, and cold climate zones, and compared plot data to 8 Earth system models (ESMs). Long-term plots consistently showed decreases in living vegetation carbon turnover time, likely driven by increased tree mortality across all major climate zones. Changes in living vegetation carbon turnover time were negatively correlated with CO2 enrichment in both forest plot data and ESM simulations. However, plot-based correlations between living vegetation carbon turnover time and climate drivers such as precipitation and temperature diverged from those of ESM simulations. Our analyses suggest that forest carbon sinks are likely to be constrained by a decrease in living vegetation carbon turnover time, and accurate projections of forest carbon sink dynamics will require an improved representation of tree mortality processes and their sensitivity to climate in ESMs.

Recent climate-driven ecological change across a continent as perceived through local ecological knowledge.

Documenting effects of climate change is an important step towards designing mitigation and adaptation responses. Impacts of climate change on terrestrial biodiversity and ecosystems have been well-documented in the Northern Hemisphere, but long-term data to detect change in the Southern Hemisphere are limited, and some types of change are generally difficult to measure. Here we present a novel approach using local ecological knowledge to facilitate a continent-scale view of climate change impacts on terrestrial biodiversity and ecosystems that people have perceived in Australia. We sought local knowledge using a national web-based survey, targeting respondents with close links to the environment (e.g. farmers, ecologists), and using a custom-built mapping tool to ask respondents to describe and attribute recent changes they had observed within an area they knew well. Results drawn from 326 respondents showed that people are already perceiving simple and complex climate change impacts on hundreds of species and ecosystems across Australia, significantly extending the detail previously reported for the continent. While most perceived trends and attributions remain unsubstantiated, >35 reported anecdotes concurred with examples in the literature, and >20 were reported more than once. More generally, anecdotes were compatible with expectations from global climate change impact frameworks, including examples across the spectrum from organisms (e.g. increased mortality in >75 species), populations (e.g. changes in recruitment or abundance in >100 species, phenological change in >50 species), and species (e.g. >80 species newly arriving or disappearing), to communities and landscapes (e.g. >50 examples of altered ecological interactions). The overarching pattern indicated by the anecdotes suggests that people are more often noticing climate change losers (typically native species) than winners in their local areas, but with observations of potential 'adaptation in action' via compositional and phenological change and through arrivals and range shifts (particularly for native birds and exotic plants). A high proportion of climate change-related anecdotes also involved cumulative or interactive effects of land use. We conclude that targeted elicitation of local ecological knowledge about climate change impacts can provide a valuable complement to data-derived knowledge, substantially extending the volume of explicit examples and offering a foundation for further investigation.

A unified framework for analysis of individual-based models in ecology and beyond.

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.

Comprehensive Evaluation and Spatial Difference Analysis of Regional Ecological Carrying Capacity: A Case Study of the Yangtze River Urban Agglomeration.

Ecological carrying capacity is an important factor of sustainable development for cities, and a critical part of achieving the coordinated development of the social economic and ecological environment for urban agglomerations. In order to evaluate the regional ecological carrying capacity and provide a basis for decision-making for regional sustainable development, this paper constructs an ecological carrying capacity model for the urban agglomeration from two dimensions: ecological carrying elastic force and ecological carrying pressure. The analytic hierarchy process is utilized to determine the weights of nine indices in these two dimensions. For the Yangtze River urban agglomeration, the comprehensive index of its ecological carrying capacity is investigated quantitatively, and the spatial distribution map of its comprehensive index measuring ecological carrying capacity is computed. The results show that Nanjing, Yangzhou, Taizhou, and Changzhou are in the stage of high load carrying; Suzhou, Wuxi, Nantong, and Zhenjiang are in the stage of low load carrying. In addition, the environmental protection investment has the greatest impact on ecological carrying elastic force, followed by the proportion of the tertiary industry; wastewater discharge has the greatest impact on ecological carrying pressure. The level of ecological carrying capacity varies within the region. It is necessary to take measures to increase the ecological carrying elastic force and reduce the ecological carrying pressure according to the actual conditions in each region. Meanwhile, exchanges and cooperation between different regions should be strengthened to stimulate the coordinated and sustainable development.

Male principal investigators (almost) don't publish with women in ecology and zoology.

Representation of women in science drops substantially at each career stage, from early student to senior investigator. Disparities in opportunities for women to contribute to research metrics, such as distinguished speaker events and authorship, have been reported in many fields in the U.S.A. and Europe. However, whether female representation in scientific contributions differs in other regions, such as Latin America, is not well understood. In this study, in order to determine whether female authorship is influenced by gender or institutional location of the last (senior) author or by subfield within ecology, we gathered author information from 6849 articles in ten ecological and zoological journals that publish research articles either in or out of Latin America. We found that female authorship has risen marginally since 2002 (27 to 31%), and varies among Latin American countries, but not between Latin America and other regions. Last author gender predicted female co-authorship across all journals and regions, as research groups led by women published with over 60% female co-authors whereas those led by men published with less than 20% female co-authors. Our findings suggest that implicit biases and stereotype threats that women face in male-led laboratories could be sources of female withdrawal and leaky pipelines in ecology and zoology. Accordingly, we encourage every PI to self-evaluate their lifetime percentage of female co-authors. Female role models and cultural shifts-especially by male senior authors-are crucial for female retention and unbiased participation in science.

Beware z-scores.

Song, Rohr, and Saavedra (2017) have proposed a methodology to compare network properties across systems with different sizes and constraints, in response to the fact that z-scores cannot be used for such purposes. Simmons, Hoeppke, and Sutherland (2019) have shown that part of the methodology can be improved. Here, we show that all previous results hold and are strengthened by the new methodology.

Exact inference for integrated population modelling.

Integrated population modelling is widely used in statistical ecology. It allows data from population time series and independent surveys to be analysed simultaneously. In classical analysis the time-series likelihood component can be conveniently approximated using Kalman filter methodology. However, the natural way to model systems which have a discrete state space is to use hidden Markov models (HMMs). The proposed method avoids the Kalman filter approximations and Monte Carlo simulations. Subject to possible numerical sensitivity analysis, it is exact, flexible, and allows the use of standard techniques of classical inference. We apply the approach to data on Little owls, where the model is shown to require a one-dimensional state space, and Northern lapwings, with a two-dimensional state space. In the former example the method identifies a parameter redundancy which changes the perception of the data needed to estimate immigration in integrated population modelling. The latter example may be analysed using either first- or second-order HMMs, describing numbers of one-year olds and adults or adults only, respectively. The use of first-order chains is found to be more efficient, mainly due to the smaller number of one-year olds than adults in this application. For the lapwing modelling it is necessary to group the states in order to reduce the large dimension of the state space. Results check with Bayesian and Kalman filter analyses, and avenues for future research are identified.