Spatial complementarity and the coexistence of species

Coexistence of apparently similar species remains an enduring paradox in ecology. Spatial structure has been predicted to enable coexistence even when population-level models predict competitive exclusion if it causes each species to limit its own population more than that of its competitor. Nevertheless, existing hypotheses conflict with regard to whether clustering favours or precludes coexistence. The spatial segregation hypothesis predicts that in clustered populations the frequency of intra-specific interactions will be increased, causing each species to be self-limiting. Alternatively, individuals of the same species might compete over greater distances, known as heteromyopia, breaking down clusters and opening space for a second species to invade. In this study we create an individual-based model in homogeneous two-dimensional space for two putative sessile species differing only in their demographic rates and the range and strength of their competitive interactions. We fully characterise the parameter space within which coexistence occurs beyond population-level predictions, thereby revealing a region of coexistence generated by a previously-unrecognised process which we term the triadic mechanism. Here coexistence occurs due to the ability of a second generation of offspring of the rarer species to escape competition from their ancestors. We diagnose the conditions under which each of three spatial coexistence mechanisms operates and their characteristic spatial signatures. Deriving insights from a novel metric — ecological pressure — we demonstrate that coexistence is not solely determined by features of the numerically-dominant species. This results in a common framework for predicting, given any pair of species and knowledge of the relevant parameters, whether they will coexist, the mechanism by which they will do so, and the resultant spatial pattern of the community. Spatial coexistence arises from complementary combinations of traits in each species rather than solely through self-limitation

Scientific Opinion on the safety and efficacy of Urea for ruminants

Urea supplementation to feed for ruminants provides non-protein nitrogen for microbial protein synthesis in the rumen and thus in part replaces other dietary protein sources. Urea supplementation of feed for ruminants at doses up to 1 % of complete feed DM (corresponding to 0.3 g/kg bw/day) is considered safe when given to animals with a well adapted ruminal microbiota and fed diets rich in easily digestible carbohydrates. Based on the metabolic fate of urea in ruminants, the use of urea in ruminant nutrition does not raise any concern for consumers\u2019 safety. Urea is considered to be non irritant to skin and eyes and its topical use suggests that it is not a dermal sensitiser. The risk of exposure by inhalation would be low. The substitution of protein by urea in well balanced feed for ruminants would not result in an increased environmental nitrogen load. Urea is an effective source of non-protein nitrogen substituting for dietary protein in ruminants

Psalms for Organ, Volume 11: Ulster

Pages 23-27 in Psalms for Organ, Volume 11

Stochastic population growth in spatially heterogeneous environments

Classical ecological theory predicts that environmental stochasticity increases extinction risk by reducing the average per-capita growth rate of populations. To understand the interactive effects of environmental stochasticity, spatial heterogeneity, and dispersal on population growth, we study the following model for population abundances in $n$ patches: the conditional law of $X_{t+dt}$ given $X_t=x$ is such that when $dt$ is small the conditional mean of $X_{t+dt}^i-X_t^i$ is approximately $[x^i\mu_i+\sum_j(x^j D_{ji}-x^i D_{ij})]dt$, where $X_t^i$ and $\mu_i$ are the abundance and per capita growth rate in the $i$-th patch respectivly, and $D_{ij}$ is the dispersal rate from the $i$-th to the $j$-th patch, and the conditional covariance of $X_{t+dt}^i-X_t^i$ and $X_{t+dt}^j-X_t^j$ is approximately $x^i x^j \sigma_{ij}dt$. We show for such a spatially extended population that if $S_t=(X_t^1+...+X_t^n)$ is the total population abundance, then $Y_t=X_t/S_t$, the vector of patch proportions, converges in law to a random vector $Y_\infty$ as $t\to\infty$, and the stochastic growth rate $\lim_{t\to\infty}t^{-1}\log S_t$ equals the space-time average per-capita growth rate \sum_i\mu_i\E[Y_\infty^i] experienced by the population minus half of the space-time average temporal variation \E[\sum_{i,j}\sigma_{ij}Y_\infty^i Y_\infty^j] experienced by the population. We derive analytic results for the law of $Y_\infty$, find which choice of the dispersal mechanism $D$ produces an optimal stochastic growth rate for a freely dispersing population, and investigate the effect on the stochastic growth rate of constraints on dispersal rates. Our results provide fundamental insights into "ideal free" movement in the face of uncertainty, the persistence of coupled sink populations, the evolution of dispersal rates, and the single large or several small (SLOSS) debate in conservation biology.Comment: 47 pages, 4 figure

Phytoplankton niche generation by interspecific stoichiometric variation

For marine biogeochemical models used in simulations of climate change scenarios, the ability to account for adaptability of marine ecosystems to environmental change becomes a concern. The potential for adaptation is expected to be larger for a diverse ecosystem compared to a monoculture of a single type of (model) algae, such as typically included in biogeochemical models. Recent attempts to simulate phytoplankton diversity in global marine ecosystem models display remarkable qualitative agreement with observed patterns of species distributions. However, modeled species diversity tends to be systematically lower than observed and, in many regions, is smaller than the number of potentially limiting nutrients. According to resource competition theory, the maximum number of coexisting species at equilibrium equals the number of limiting resources. By simulating phytoplankton communities in a chemostat model and in a global circulation model, we show here that a systematic underestimate of phytoplankton diversity may result from the standard modeling assumption of identical stoichiometry for the different phytoplankton types. Implementing stoichiometric variation among the different marine algae types in the models allows species to generate different resource supply niches via their own ecological impact. This is shown to increase the level of phytoplankton coexistence both in a chemostat model and in a global self-assembling ecosystem model. Key Points: - Common Redfield stoichiometry in plankton models impedes phytoplankton diversity - Stoichiometric plasticity increases the chance for sustained diversity - Modelers should go beyond Redfield stoichiometry in multi-phytoplankton model

Standard atomic weights of the elements 2021 (IUPAC Technical Report)

Following the reviews of atomic-weight determinations and other cognate data in 2015, 2017, 2019 and 2021, the IUPAC (International Union of Pure and Applied Chemistry) Commission on Isotopic Abundances and Atomic Weights (CIAAW) reports changes of standard atomic weights. The symbol A r°(E) was selected for standard atomic weight of an element to distinguish it from the atomic weight of an element E in a specific substance P, designated A r(E, P). The CIAAW has changed the values of the standard atomic weights of five elements based on recent determinations of terrestrial isotopic abundances: Ar (argon): from 39.948 ± 0.001 to [39.792, 39.963] Hf (hafnium): from 178.49 ± 0.02 to 178.486 ± 0.006 Ir (iridium): from 192.217 ± 0.003 to 192.217 ± 0.002 Pb (lead): from 207.2 ± 0.1 to [206.14, 207.94] Yb (ytterbium): from 173.054 ± 0.005 to 173.045 ± 0.010 The standard atomic weight of argon and lead have changed to an interval to reflect that the natural variation in isotopic composition exceeds the measurement uncertainty of A r(Ar) and A r(Pb) in a specific substance. The standard atomic weights and/or the uncertainties of fourteen elements have been changed based on the Atomic Mass Evaluations 2016 and 2020 accomplished under the auspices of the International Union of Pure and Applied Physics (IUPAP). A r° of Ho, Tb, Tm and Y were changed in 2017 and again updated in 2021: Al (aluminium), 2017: from 26.981 5385 ± 0.000 0007 to 26.981 5384 ± 0.000 0003 Au (gold), 2017: from 196.966 569 ± 0.000 005 to 196.966 570 ± 0.000 004 Co (cobalt), 2017: from 58.933 194 ± 0.000 004 to 58.933 194 ± 0.000 003 F (fluorine), 2021: from 18.998 403 163 ± 0.000 000 006 to 18.998 403 162 ± 0.000 000 005 (Ho (holmium), 2017: from 164.930 33 ± 0.000 02 to 164.930 328 ± 0.000 007) Ho (holmium), 2021: from 164.930 328 ± 0.000 007 to 164.930 329 ± 0.000 005 Mn (manganese), 2017: from 54.938 044 ± 0.000 003 to 54.938 043 ± 0.000 002 Nb (niobium), 2017: from 92.906 37 ± 0.000 02 to 92.906 37 ± 0.000 01 Pa (protactinium), 2017: from 231.035 88 ± 0.000 02 to 231.035 88 ± 0.000 01 Pr (praseodymium), 2017: from 140.907 66 ± 0.000 02 to 140.907 66 ± 0.000 01 Rh (rhodium), 2017: from 102.905 50 ± 0.000 02 to 102.905 49 ± 0.000 02 Sc (scandium), 2021: from 44.955 908 ± 0.000 005 to 44.955 907 ± 0.000 004 (Tb (terbium), 2017: from 158.925 35 ± 0.000 02 to 158.925 354 ± 0.000 008) Tb (terbium), 2021: from 158.925 354 ± 0.000 008 to 158.925 354 ± 0.000 007 (Tm (thulium), 2017: from 168.934 22 ± 0.000 02 to 168.934 218 ± 0.000 006) Tm (thulium), 2021: from 168.934 218 ± 0.000 006 to 168.934 219 ± 0.000 005 (Y (yttrium), 2017: from 88.905 84 ± 0.000 02 to 88.905 84 ± 0.000 01) Y (yttrium), 2021: from 88.905 84 ± 0.000 01 to 88.905 838 ± 0.00