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.

In this paper, we merge partial differential equation model, regression model and credibility measure based fuzzy mathematics proposed by Liu into a new partial differential equation motivated regression model (abbreviated as PDEMR model). The creation of PDEMR model further extends DEMR model ideation proposed by Guo et al. Furthermore, we develop a PDEMR model for the quantitative modeling on multivariate small sample data. PDEMR models will be able to establish the quantitative relationship among the main factor vector and the covariate vectors, which is a major improvement of information extraction with sparse data availability. Finally, we apply the PDEMR model to South African rare Protea species predictions and even tune up the data set for a regional GIS kriging maps.

PDEMR Model in Rare Protea Count Prediction

Extreme changes in temperature, rainfall and wind regimes have been correlated with plant species range expansion upslope on sub-Antarctic islands. Ongoing climatic changes are expected to continue driving changes in species distributions globally, but niche specialisations may limit the capacity for range shifts. We hypothesised that non-climatic characteristics of ecological niches of vascular plant species could limit climate induced range shifts. We determined the altitudinal ranges of vascular plant species (n = 13) on sub-Antarctic Marion Island and measured air temperature, topographic, foliar and soil properties along transects on geologically distinct substrates. Climatic and non-climatic associations were determined using multiple linear regression and boosted regression tree (BRT) analyses. The degree of niche specialisation was determined using outlying mean index analysis within the range of species on the island. Several species (7 of 13) exhibited niche-specialisation. Correlation analysis revealed that edaphic properties including soil depth, loss on ignition, the principal component of most soil nutrients (Mg, Cl, K, Ca, Cu, Zn, P, S), Si, Mn and clay dominated the BRT prediction of overall plant cover. Although air temperature was correlated with plant cover in linear models, model simplification dropped temperature in both BRT and linear models. As a consequence, multiple determinants, including temperature, climate, topography and soils control the distribution of vascular plant species on this sub-Antarctic island. Edaphic and topographic factors is likely to limit range-shifts of specialised vascular plant species and could limit their survival when climate change drives them beyond the extent of their particular ecological niches. Resilience of such species is likely poorly predicted by distribution models that depend heavily on climate rather than less labile non-climatic factors. Read the free Plain Language Summary for this article on the Journal blog. 

Plant specialisation may limit climate‐induced vegetation change to within topographic and edaphic niches on a
 sub‐Antarctic
 island

Changing land cover is a phenomenon that is growing in magnitude and significance, both globally and in South Africa. Changes in land cover include the conversion of natural vegetation to agricultural crops and forest plantations, changes to natural vegetation through bush encroachment and overgrazing, soil erosion, invasion by alien plant species, and accelerating urbanisation. Land-cover changes increasingly relate to climate and atmospheric changes in ways that are currently poorly understood but potentially significant, especially in terms of compromising or enhancing the delivery of vital ecosystem services from rangelands, agricultural croplands, water catchments and conservation areas.

Exploring the significance of land-cover change in South Africa : news and views

The following was written by Dr GF Midgley and Prof LG Underhill in response to the article by Prof WRJ Alexander on his multi-year climate prediction model, which appeared in the January/February 2007 edition of the Water Wheel.

Guy F. Midgley

Papers

PDEMR Model in Rare Protea Count Prediction

Plant specialisation may limit climate‐induced vegetation change to within topographic and edaphic niches on a sub‐Antarctic island

Exploring the significance of land-cover change in South Africa : news and views

Is climate prediction model flawed? : opinion

Assemblage reorganization of South African dragonflies due to climate change