Forecasting for intended consequences

Using neural ordinary differential equations to predict complex ecological dynamics from population density data

Simple models have been used to describe ecological processes for over a century. However, the complexity of ecological systems makes simple models subject to modelling bias due to simplifying assumptions or unaccounted factors, limiting their predictive power. Neural ordinary differential equations (NODEs) have surged as a machine-learning algorithm that preserves the dynamic nature of the data (Chen et al. 2018 Adv. Neural Inf. Process. Syst.). Although preserving the dynamics in the data is an advantage, the question of how NODEs perform as a forecasting tool of ecological communities is unanswered. Here, we explore this question using simulated time series of competing species in a time-varying environment. We find that NODEs provide more precise forecasts than autoregressive integrated moving average (ARIMA) models. We also find that untuned NODEs have a similar forecasting accuracy to untuned long-short term memory neural networks and both are outperformed in accuracy and precision by empirical dynamical modelling . However, we also find NODEs generally outperform all other methods when evaluating with the interval score, which evaluates precision and accuracy in terms of prediction intervals rather than pointwise accuracy. We also discuss ways to improve the forecasting performance of NODEs. The power of a forecasting tool such as NODEs is that it can provide insights into population dynamics and should thus broaden the approaches to studying time series of ecological communities.

Adaptive Gaussian Markov random field spatiotemporal models for infectious disease mapping and forecasting

Recent disease mapping literature presents adaptively parameterized spatiotemporal (ST) autoregressive (AR) or conditional autoregressive (CAR) models for Bayesian prediction of COVID-19 infection risks. These models were motivated to capture complex spatiotemporal dynamics and heterogeneities of infection risks. In the present paper, we synthesize, generalize, and unify the ST AR and CAR model constructions for models augmented by adaptive Gaussian Markov random fields, with an emphasis on disease forecasting. A general convolution construction is presented, with illustrative models motivated to (i) characterize local risk dependencies and influences over both spatial and temporal dimensions, (ii) model risk heterogeneities and discontinuities, and (iii) predict and forecast areal-level disease risks and occurrences. The broadened constructions allow rich options of intuitive parameterization for disease mapping and spatial regression. Illustrative parameterizations are presented for Bayesian hierarchical models of Poisson, zero-inflated Poisson, and Bernoulli data models, respectively. They are also discussed in the context of quantifying time-varying or time-invariant effects of (omitted) covariates, with application to prediction and forecasting areal-level COVID-19 infection occurrences and probabilities of zero-infection. The model constructions presented herein have much wider scope in offering a flexible framework for modelling complex spatiotemporal data and for estimation, learning, and forecasting purposes.

Forecasting the influence of conservation strategies on landscape connectivity

Maintaining and enhancing landscape connectivity reduces biodiversity declines due to habitat fragmentation. Uncertainty remains, however, about the effectiveness of conservation for enhancing connectivity for multiple species on dynamic landscapes, especially over long time horizons. We forecasted landscape connectivity from 2020 to 2100 under four common conservation land-acquisition strategies: acquiring the lowest cost land, acquiring land clustered around already established conservation areas, acquiring land with high geodiversity characteristics, and acquiring land opportunistically. We used graph theoretic metrics to quantify landscape connectivity across these four strategies, evaluating connectivity for four ecologically relevant species guilds that represent endpoints along a spectrum of vagility and habitat specificity: long- versus short-distance dispersal ability and habitat specialists versus generalists. We applied our method to central North Carolina and incorporated landscape dynamics, including forest growth, succession, disturbance, and management. Landscape connectivity improved for specialist species under all conservation strategies employed, although increases were highly variable across strategies. For generalist species, connectivity improvements were negligible. Overall, clustering the development of new protected areas around land already designated for conservation yielded the largest improvements in connectivity; increases were several orders of magnitude beyond current landscape connectivity for long- and short-distance dispersing specialist species. Conserving the lowest cost land contributed the least to connectivity. Our approach provides insight into the connectivity contributions of a suite of conservation alternatives prior to on-the-ground implementation and, therefore, can inform connectivity planning to maximize conservation benefit.

Cognitive architectures for artificial intelligence ethics

As artificial intelligence (AI) thrives and propagates through modern life, a key question to ask is how to include humans in future AI? Despite human involvement at every stage of the production process from conception and design through to implementation, modern AI is still often criticized for its “black box” characteristics. Sometimes, we do not know what really goes on inside or how and why certain conclusions are met. Future AI will face many dilemmas and ethical issues unforeseen by their creators beyond those commonly discussed (e.g., trolley problems and variants of it) and to which solutions cannot be hard-coded and are often still up for debate. Given the sensitivity of such social and ethical dilemmas and the implications of these for human society at large, when and if our AI make the “wrong” choice we need to understand how they got there in order to make corrections and prevent recurrences. This is particularly true in situations where human livelihoods are at stake (e.g., health, well-being, finance, law) or when major individual or household decisions are taken. Doing so requires opening up the “black box” of AI; especially as they act, interact, and adapt in a human world and how they interact with other AI in this world. In this article, we argue for the application of cognitive architectures for ethical AI. In particular, for their potential contributions to AI transparency, explainability, and accountability. We need to understand how our AI get to the solutions they do, and we should seek to do this on a deeper level in terms of the machine-equivalents of motivations, attitudes, values, and so on. The path to future AI is long and winding but it could arrive faster than we think. In order to harness the positive potential outcomes of AI for humans and society (and avoid the negatives), we need to understand AI more fully in the first place and we expect this will simultaneously contribute towards greater understanding of their human counterparts also.

A Path Forward: Promoting Microbial-Based Methods in the Control of Invasive Plant Species

In this review, we discuss the unrealized potential of incorporating plant-microbe and microbe-microbe interactions into invasive plant management strategies. While the development of this as a viable strategy is in its infancy, we argue that incorporation of microbial components into management plans should be a priority and has great potential for diversifying sustainable control options. We advocate for increased research into microbial-mediated phytochemical production, microbial controls to reduce the competitiveness of invasive plants, microbial-mediated increases of herbicidal tolerance of native plants, and to facilitate increased pathogenicity of plant pathogens of invasive plants.