Crop yields are projected to decrease under future climate conditions, and recent research suggests that yields have already been impacted. However, current impacts on a diversity of crops ...subnationally and implications for food security remains unclear. Here, we constructed linear regression relationships using weather and reported crop data to assess the potential impact of observed climate change on the yields of the top ten global crops-barley, cassava, maize, oil palm, rapeseed, rice, sorghum, soybean, sugarcane and wheat at ~20,000 political units. We find that the impact of global climate change on yields of different crops from climate trends ranged from -13.4% (oil palm) to 3.5% (soybean). Our results show that impacts are mostly negative in Europe, Southern Africa and Australia but generally positive in Latin America. Impacts in Asia and Northern and Central America are mixed. This has likely led to ~1% average reduction (-3.5 X 1013 kcal/year) in consumable food calories in these ten crops. In nearly half of food insecure countries, estimated caloric availability decreased. Our results suggest that climate change has already affected global food production.
In the past, explanations for high species diversity have been sought at the species level. Theory shows that coexistence requires substantial differences between species, but species-level data ...rarely provide evidence for such differences. Using data from forests in the southeastern United States, I show here that variation evident at the individual level provides for coexistence of large numbers of competitors. Variation among individuals within populations allows species to differ in their distributions of responses to the environment, despite the fact that the populations to which they belong do not differ, on average. Results are consistent with theory predicting that coexistence depends on competition being stronger within than between species, shown here by analysis of individual-level responses to environmental fluctuation.
Trait analysis aims to understand relationships between traits, species diversity, and the environment. Current methods could benefit from a model-based probabilistic framework that accommodates ...covariance between traits and quantifies contributions from inherent trait syndromes, species interactions, and responses to the environment. I develop a model-based approach that separates these effects on trait diversity. Application to USDA Forest Inventory and Analysis (FIA) data in the eastern United States demonstrates an apparent paradox, that the analysis of species better explains and predicts traits than does direct analysis of the traits themselves; trait data contain less, not more, information than species on environmental responses. Whereas variation in some traits is dominated by inherent syndromes (tendency for certain traits to be associated with others within an individual and species), others are strongly controlled by variation in species diversity. There is substantial variation in environmental control on trait patterns, between traits and regionally. In terms of environmental response traits do not aggregate into defined plant functional types, as would be desirable for models.
The most abundant mammals on Earth Clark, James S.
Trends in ecology & evolution (Amsterdam),
July 2023, 2023-07-00, 20230701, Volume:
38, Issue:
7
Journal Article
Peer reviewed
New estimates of global mammal abundance that use relationships between traits, estimates of range size, and International Union for Conservation of Nature’s (IUCN’s) Red List categories to predict ...the biomass of thousands of species have been developed by Greenspoon et al. This approach and some of the challenges that contribute to these estimates are summarized here.
Tree species are expected to track warming climate by shifting their ranges to higher latitudes or elevations, but current evidence of latitudinal range shifts for suites of species is largely ...indirect. In response to global warming, offspring of trees are predicted to have ranges extend beyond adults at leading edges and the opposite relationship at trailing edges. Large‐scale forest inventory data provide an opportunity to compare present latitudes of seedlings and adult trees at their range limits. Using the USDA Forest Service's Forest Inventory and Analysis data, we directly compared seedling and tree 5th and 95th percentile latitudes for 92 species in 30 longitudinal bands for 43 334 plots across the eastern United States. We further compared these latitudes with 20th century temperature and precipitation change and functional traits, including seed size and seed spread rate. Results suggest that 58.7% of the tree species examined show the pattern expected for a population undergoing range contraction, rather than expansion, at both northern and southern boundaries. Fewer species show a pattern consistent with a northward shift (20.7%) and fewer still with a southward shift (16.3%). Only 4.3% are consistent with expansion at both range limits. When compared with the 20th century climate changes that have occurred at the range boundaries themselves, there is no consistent evidence that population spread is greatest in areas where climate has changed most; nor are patterns related to seed size or dispersal characteristics. The fact that the majority of seedling extreme latitudes are less than those for adult trees may emphasize the lack of evidence for climate‐mediated migration, and should increase concerns for the risks posed by climate change.
Explaining and modeling species communities is more than ever a central goal of ecology. Recently, joint species distribution models (JSDMs), which extend species distribution models (SDMs) by ...considering correlations among species, have been proposed to improve species community analyses and rare species predictions while potentially inferring species interactions. Here, we illustrate the mathematical links between SDMs and JSDMs and their ecological implications and demonstrate that JSDMs, just like SDMs, cannot separate environmental effects from biotic interactions. We provide a guide to the conditions under which JSDMs are (or are not) preferable to SDMs for species community modeling. More generally, we call for a better uptake and clarification of novel statistical developments in the field of biodiversity modeling.
In an era of global changes, developing reliable biodiversity models has become an important research area.Species distribution models are the common tools to understand and predict the distributions of species across space and time. However, they fail to explicitly account for species interactions.To this aim, joint species distribution models were introduced to tease apart the effect of the environment from that of species interactions, to improve rare species modeling, to account for functional traits, and to improve the predictive power of biodiversity models.Nevertheless, most announced advantages have remained unfulfilled, and there is still a need to better integrate the effect of species interactions in the response of species to environmental change.
Probabilistic forecasts of species distribution and abundance require models that accommodate the range of ecological data, including a joint distribution of multiple species based on combinations of ...continuous and discrete observations, mostly zeros. We develop a generalized joint attribute model (GJAM), a probabilistic framework that readily applies to data that are combinations of presence-absence, ordinal, continuous, discrete, composition, zero-inflated, and censored. It does so as a joint distribution over all species providing inference on sensitivity to input variables, correlations between species on the data scale, prediction, sensitivity analysis, definition of community structure, and missing data imputation. GJAM applications illustrate flexibility to the range of species-abundance data. Applications to forest inventories demonstrate species relationships responding as a community to environmental variables. It shows that the environment can be inverse predicted from the joint distribution of species. Application to microbiome data demonstrates how inverse prediction in the GJAM framework accelerates variable selection, by isolating effects of each input variable's influence across all species.
Pervasive shifts in forest dynamics in a changing world McDowell, Nate G.; Allen, Craig D.; Anderson-Teixeira, Kristina ...
Science (American Association for the Advancement of Science),
05/2020, Volume:
368, Issue:
6494
Journal Article
Peer reviewed
Open access
Forest dynamics are the processes of recruitment, growth, death, and turnover of the constituent tree species of the forest community. These processes are driven by disturbances both natural and ...anthropogenic. McDowell et al. review recent progress in understanding the drivers of forest dynamics and how these are interacting and changing in the context of global climate change. The authors show that shifts in forest dynamics are already occurring, and the emerging pattern is that global forests are tending toward younger stands with faster turnover as old-growth forest with stable dynamics are dwindling.
Advances in computational statistics provide a general framework for the high‐dimensional models typically needed for ecological inference and prediction. Hierarchical Bayes (HB) represents a ...modelling structure with capacity to exploit diverse sources of information, to accommodate influences that are unknown (or unknowable), and to draw inference on large numbers of latent variables and parameters that describe complex relationships. Here I summarize the structure of HB and provide examples for common spatiotemporal problems. The flexible framework means that parameters, variables and latent variables can represent broader classes of model elements than are treated in traditional models. Inference and prediction depend on two types of stochasticity, including (1) uncertainty, which describes our knowledge of fixed quantities, it applies to all ‘unobservables’ (latent variables and parameters), and it declines asymptotically with sample size, and (2) variability, which applies to fluctuations that are not explained by deterministic processes and does not decline asymptotically with sample size. Examples demonstrate how different sources of stochasticity impact inference and prediction and how allowance for stochastic influences can guide research.
The Unified Neutral Theory of Biodiversity (UNTB), proposed as an alternative to niche theory, has been viewed as a theory that species coexist without niche differences, without fitness differences, ...or with equal probability of success. Support is claimed when models lacking species differences predict highly aggregated metrics, such as species abundance distributions (SADs) or species area distributions (SARs). Here, I summarize why UNTB generates confusion, and is not actually relevant to niche theory (i.e. an explanation for why and how many species coexist). Equal probability is not a theory, but lack of one; it does not include or exclude any process relevant to coexistence of competitors. Models lacking explicit species can make useful predictions, but this does not support neutral theory. I provide s suggestions that could help reduce confusion generated by the debate.
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