When are hypotheses useful in ecology and evolution?

Betts, Matthew G.; Hadley, Adam S.; Frey, David W.; Frey, Sarah J. K.; Gannon, Dusty; Harris, Scott H.; Kim, Hankyu; Kormann, Urs G.; Leimberger, Kara G.; Moriarty, Katie M.; Northrup, Joseph M.; Phalan, Ben; Rousseau, Josée S.; Stokely, Thomas D.; Valente, Jonathon J.; Wolf, Christian; Zárrate-Charry, Diego

doi:10.1002/ece3.7365

Cited by 40 publications

(43 citation statements)

References 58 publications

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“…From this functional-substitutability hypothesis one can ask (Betts et al 2021) – if there is sufficient overlap in trait-space between livestock and native herbivores, then to what extent can they act as surrogates? It is challenging to address this question because many modern livestock-production systems no longer have native counterparts within comparable ecological settings that can allow a fair comparison.…”

Section: Introductionmentioning

confidence: 99%

Functional substitutability of native herbivores by livestock for soil carbon depends on microbial decomposers

Roy¹,

Bagchi²

2022

Preprint

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The functional influence of native mammalian herbivores on ecosystem functions is declining as they get gradually replaced by domestic livestock who have become the predominant herbivores across the world. Alongside rewilding, restoration, and conservation, it is now important to address whether livestock can maintain erstwhile functions of the native herbivores they have displaced across the world's grasslands, savannas, and steppes. If yes, this has implications for the future of biodiversity, ecosystem functions and services, food-security, and other social and environmental dimensions. We test competing alternative hypotheses with a replicated, long-term, landscape-level experiment to ask whether livestock in the Trans-Himalayan ecosystem can match decadal-scale soil-C sequestration in soils under native herbivores. Over decadal time-scale (2006-16) livestock were imperfect substitutes since they could only attain three-fourths of the total soil-C stocks under native herbivores. Structural equation models supported the hypotheses that livestock alter soil microbial communities towards lower fungal abundance, which is detrimental for microbial carbon use efficiency (CUE) and for soil-C storage. This lag between livestock and native herbivores can be addressed through microbial restoration and rewilding of soil. Therefore, the idea of functional substitutability must be leveraged alongside microbial rewilding to restore and conserve ecosystem functions. Together they can reconcile conflicting demands from food-security and ecosystem services, and should become an integral part of nature-based climate solutions.

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Section: Introductionmentioning

confidence: 99%

Functional substitutability of native herbivores by livestock for soil carbon depends on microbial decomposers

Roy¹,

Bagchi²

2022

Preprint

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“…This new phase has been revitalized by embracing advances in technology that transform the anecdotal character of few direct observations into scientific studies that rely on systematically collected observations using remotely sensed and non-invasive methods (Figure 1). Such technological advances propel natural history into a contemporary, meticulous, and systematic discipline with utility in both the hypotheticodeductive approach to science (Winker, 2004;Tewksbury et al, 2014;Betts et al, 2021) and the conservation and management of species.…”

Section: Introductionmentioning

confidence: 99%

The Rapid Rise of Next-Generation Natural History

et al. 2021

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Many ecologists have lamented the demise of natural history and have attributed this decline to a misguided view that natural history is outdated and unscientific. Although there is a perception that the focus in ecology and conservation have shifted away from descriptive natural history research and training toward hypothetico-deductive research, we argue that natural history has entered a new phase that we call “next-generation natural history.” This renaissance of natural history is characterized by technological and statistical advances that aid in collecting detailed observations systematically over broad spatial and temporal extents. The technological advances that have increased exponentially in the last decade include electronic sensors such as camera-traps and acoustic recorders, aircraft- and satellite-based remote sensing, animal-borne biologgers, genetics and genomics methods, and community science programs. Advances in statistics and computation have aided in analyzing a growing quantity of observations to reveal patterns in nature. These robust next-generation natural history datasets have transformed the anecdotal perception of natural history observations into systematically collected observations that collectively constitute the foundation for hypothetico-deductive research and can be leveraged and applied to conservation and management. These advances are encouraging scientists to conduct and embrace detailed descriptions of nature that remain a critically important component of the scientific endeavor. Finally, these next-generation natural history observations are engaging scientists and non-scientists alike with new documentations of the wonders of nature. Thus, we celebrate next-generation natural history for encouraging people to experience nature directly.

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“…However its potential has been unleashed mostly in applied domains, as predictions done with Machine Learning approaches often lack the interpretability needed to explain the mechanisms behind the algorithm’s decisions. As scientists, we are often uncomfortable with predictions that have no theoretical basis [ 27 ]. However, we can combine the power of data-driven models with stronger theoretical foundations [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble

et al. 2021

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Prediction is one of the last frontiers in ecology. Indeed, predicting fine-scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models to predict species abundances using reasonably easy to obtain data. We propose a sequential data-driven modeling approach that in a first step predicts the potential species abundances based on abiotic variables, and in a second step uses these predictions to model the realized abundances once accounting for species competition. Using a curated data set over five years we predict fine-scale species abundances in a highly diverse annual plant community. Our models show a remarkable spatial predictive accuracy using only easy-to-measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggests that predicting future abundances requires longer time series analysis to capture enough variability. In addition, we show that these data-driven models can also suggest how to improve mechanistic models by adding missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Robust models for predicting fine-scale species composition informed by the mechanistic understanding of the underlying abiotic and biotic processes can be a pivotal tool for conservation, especially given the human-induced rapid environmental changes we are experiencing. This objective can be achieved by promoting the knowledge gained with classic modelling approaches in ecology and recently developed data-driven models.

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When are hypotheses useful in ecology and evolution?

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 40 publications

References 58 publications

Functional substitutability of native herbivores by livestock for soil carbon depends on microbial decomposers

Functional substitutability of native herbivores by livestock for soil carbon depends on microbial decomposers

The Rapid Rise of Next-Generation Natural History

Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble

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