![]() Monahan’s is not just Ann Arbor’s best fishmonger. But ultimately the reason to come here is the great, healthy food. ![]() (I recommend the chili.) The owner is a huge community supporter and pays her server staff well, and with health benefits. Dining Detroit Filling Stationĭetroit Filling Station is Ann Arbor’s favorite vegan restaurant with a pretty cool menu. You’ll find a large selection of vegan and gluten-free baked goods here. This is my kind of bakery! They have sandwiches, hot & cold drinks, ice cream, and of course all your standard bakery goodies. They try to source as local as within 20 miles and use organic flour and cane sugar. It’s a little bit west of Information Entropy out Miller Rd from town. Filtered by forest type this was 34% for podzol forests, várzea 25%, igapó 23%, swamp forests 34%, 21% and 24% for Guyana Shield and Pebas terra firme respectively and 20% for Brazilian Shield terra firme forests (see Table S1 for detailed decomposition).DO NOT miss Big City Small World Bakery. Using a uniform prior and CWM values (Community Weighted Means) as constraints accounted for 23% on average of total deviance between observed and predicted relative abundances (measured by R 2 KL values, see Box S2 Eq. 5). Here we specifically use Shipley’s mathematical framework (CATS) for the MEF calculations, similar to earlier studies 9, 10, 11. Maximization of entropy allows quantifying the information yield for each constraint and therefor identifies which constraints reduce entropy the most. Principles from information theory 6, 7, 8 can be used in an ecological setting to predict the most likely abundance state for each taxon while simultaneously maximizing entropy based on constraints. Its application to an unprecedented large tree inventory database on genus level taxonomy consisting of > 2,000 1-ha plots distributed over Amazonia 4 and a genus trait database of 13 key functional traits representing global axes of plant strategies 5 allows us to advance the study of Amazonian tree community dynamics from a new cross-disciplinary perspective. The MEF as applied here, however, is capable of and designed to do exactly this by decomposing variation to separate information explained by each of these aspects in a four-step model (Fig. This is especially so because, although many different tests are available that link variation in taxon abundances to (1) trait variation, (2) taxon turnover between habitats or environments and (3) the distance decay of similarities between samples, none quantify the importance of these relative to each other. Quantifying the relative importance of these distinct constraints can thus provide additional answers to understand the complexity of community dynamics (see Supporting Materials SM: boxes S1– S3). functional traits or summed regional abundances) 3. The Maximum Entropy Formalism (hereafter called MEF) makes no such, potentially unjustified, a-priori assumptions in generating predictions of species abundance distributions, as such it is a useful construct to infer processes driving community dynamics given the constraints imposed by prior knowledge (e.g. Most models are based on prior assumptions of processes that drive community dynamics. near-neutral, continuum or emergent-neutral 1, 2). stochastic) and almost everything in between (e.g. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics.ĭrivers of species distributions and their predictions have been a long-standing search in ecology, with approaches varying from deterministic to neutral (i.e. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. ![]() ![]() In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. Scientific Reports volume 13, Article number: 2859 ( 2023) Henrique Eduardo Mendonça Nascimento 3,.Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology ![]()
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