https://www.cs.princeton.edu/~schapire/papers/ecolmod.pdf

Maximum entropy modeling of species geographic distributions

Continuing population and consumption growth will mean that the global demand for food will increase for at least another 40 years. Growing competition for land, water, and energy, in addition to the overexploitation of fisheries, will affect our ability to produce food, as will the urgent requirement to reduce the impact of the food system on the environment. The effects of climate change are a further threat. But the world can produce more food and can ensure that it is used more efficiently and equitably. A multifaceted and linked global strategy is needed to ensure sustainable and equitable food security, different components of which are explored here.

/pdf/food-security-the-challenge-of-feeding-9-billion-people-7gwey15y62.pdf

Food Security: The Challenge of Feeding 9 Billion People

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

/pdf/novel-methods-improve-prediction-of-species-distributions-3cmioqeu6v.pdf

Novel methods improve prediction of species' distributions from occurrence data

Climate change over the past approximately 30 years has produced numerous shifts in the distributions and abundances of species and has been implicated in one species-level extinction. Using projections of species' distributions for future climate scenarios, we assess extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface. Exploring three approaches in which the estimated probability of extinction shows a power-law relationship with geographical range size, we predict, on the basis of mid-range climate-warming scenarios for 2050, that 15-37% of species in our sample of regions and taxa will be 'committed to extinction'. When the average of the three methods and two dispersal scenarios is taken, minimal climate-warming scenarios produce lower projections of species committed to extinction ( approximately 18%) than mid-range ( approximately 24%) and maximum-change ( approximately 35%) scenarios. These estimates show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.

/pdf/extinction-risk-from-climate-change-3pqx1sctsd.pdf

Extinction risk from climate change

Statistics for Spatial Data.

Rainfall-runoff models are frequently used for assessing climate risks by predicting changes in streamflow and other hydrological processes due to anticipated anthropogenic climate change, climate variability, and land management. Historical observations are commonly used to calibrate empirically the performance of conceptual hydrological mechanisms. As a result, calibration procedures are limited when extrapolated to novel climate conditions under future scenarios. In this paper, rainfall-runoff model performance and the simulated catchment hydrological processes were explored using the JAMS/J2000 model for the Berg River catchment in South Africa to evaluate the model in the tails of the current distribution of climatic conditions. An evolutionary multi-objective search algorithm was used to develop sets of parameters which best simulate “wet” and “dry” periods, providing the upper and lower bounds for a temporal uncertainty analysis approach to identify variables which are affected by these climate extremes. Variables most affected included soil-water storage and timing of interflow and groundwater flow, emerging as the overall dampening of the simulated hydrograph. Previous modeling showed that the JAMS/J2000 model provided a “good” simulation for periods where the yearly long-term mean precipitation shortfall was <28%. Above this threshold, and where autumn precipitation was reduced by 50%, this paper shows that the use of a set of “dry” parameters is recommended to improve model performance. These “dry” parameters better account for the change in streamflow timing of concentration and reduced peak flows, which occur in drier winter years, improving the Nash-Sutcliffe Efficiency (NSE) from 0.26 to 0.60 for the validation period 2015–2018, although the availability of climate data was still a potential factor. As the model performance was “good” (NSE > 0.7) during “wet” periods using parameters from a long-term calibration, “wet” parameters were not recommended for the Berg River catchment, but could play a large role in tropical climates. The results of this study are likely transferrable to other conceptual rainfall/runoff models, but may differ for various climates. As greater climate variability drives hydrological changes around the world, future empirically-based hydrological projections need to evaluate assumptions regarding storage and the simulated hydrological processes, to enhanced climate risk management.

/pdf/how-climate-extremes-influence-conceptual-rainfall-runoff-162o4rl6.pdf

How Climate Extremes Influence Conceptual Rainfall-Runoff Model Performance and Uncertainty

Ecosystem impacts and adaptation

One of the major concerns today is global warming and climate change impacts, and how they are changing the distribution and behaviour of the plant species. For example, Proteas species in the Cape Floristic Region, South Africa, are very sensitive to climate change. In this paper, we first arguing and the random fuzzy error structure for spatial modelling accuracy and then we are focusing on the population category of rare Proteas that has an estimated population size from 1 to 10 per sample site, which is very small. We develop a bivariate partial differential equation associated regression (PDEAR) model for investigating the impacts from rainfall and temperature on the Protea species. Under same the average biodiversity structure assumptions, we explore the future spatial change patterns of Protea species with future (average) predicted rainfall and temperature. Our investigation shows that the global climate change impacts on distributional patterns of the endangered Protea species are significant.

Climate change impacts on Protea species: PDEAR model predictions.

The Restionaceae species of the fynbos biome is part of the Cape floral kingdom, one of only six floristic kingdoms in the world. It is threatened by urbanization, agricultural expansion and groundwater extraction. Therefore, it is necessary to assess and monitor the Restionaceae species. South Africa is a semi-arid environment and hydrological factors are the main variables in the determination of species niches. This study investiagates the microclimate at Silvermine, and examines the contribution of hydrological variables to plant species distribution, thus creating a hydrological niche. This study generates its own unique microclimate hydrological datasets for modelling species niche. Additionally, this study assessed and modelled the effectiveness of the use of hydrological variables to determine species hydrological niche, at a microclimate level in a semi-arid environment. It also provided evidence regarding the importance of the study to conservation and future climate change impact analysis of plant species and species richness.

Guy F. Midgley

Papers

How Climate Extremes Influence Conceptual Rainfall-Runoff Model Performance and Uncertainty

Ecosystem impacts and adaptation

Climate change impacts on Protea species: PDEAR model predictions.

Hydrological Niche of Restionaceae Species in Silvermine South Africa

Impact of elevated CO2 on the phenology and canopy structure of a C4 grassland microcosm