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.

Global warming and climate changes are changing the environment and therefore changing the distribution and behaviour of the plant species. Plant species often move and change their distributions as they find their original habitats are no longer suitable to their needs. It is therefore important to establish a statistical model to catch up the movement and patterns of the endangered species in order to effectively manage environmental protection under the inevitable
biodiversity changes that are taking place. In this paper, 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 used the partial differential equation associated regression (PDEAR) model, which merges the partial differential equation theory, (statistical) linear model theory and random fuzzy variable theory together into a efficient small-sample oriented model, for the spatial pattern changing analysis. The regression component in a PDEAR model is in nature a special random fuzzy multivariate regression model. We developed a bivariate model for investigating the impacts from rainfall and temperature on the
Protea species in average sense in the population size of 1 to 10, in the Cape Floristic Region, from 1992 to 2002, South Africa. Under same the average biodiversity structure assumptions, we explore the future spatial change patterns of Protea species in the population size of 1 to 10 with future (average) predicted rainfall and temperature. The spatial distribution and patterns are clearly will help us to explore global climate changing impacts on endangered species.

PDEAR model prediction of Protea species in years 2070-2100

Summary If trees minimize self‐shading, new foliage in shaded parts of the crown should remain minimal. However, many species have abundant foliage on short shoots inside their crown. In this paper, we test the hypothesis that short shoots allow trees to densify their foliage in self‐shaded parts of the crown thanks to reduced costs. Using 30 woody species in Mediterranean and tropical biomes, we estimated the contribution of short shoots to total plant foliage, calculated their costs relative to long shoots including wood cost and used 3D plant simulations calibrated with field measurements to quantify their light interception, self‐shading and yield. In species with short shoots, leaves on short shoots account for the majority of leaf area. The reduced cost of short stems enables the production of leaf area with 36% less biomass. Simulations show that although short shoots are more self‐shaded, they benefit the plant because they cost less. Lastly, the morphological properties of short shoots have major implications for whole plant architecture. Taken together, our results question the validity of only assessing leaf costs to understand leaf economics and call for more integrated observations at the crown scale to understand light capture strategies in woody plants.

If self‐shading is so bad, why is there so much? Short shoots reconcile costs and benefits

Covert symptoms of pollution stress in introduced vegetation near Cape Town.

The Cape Restionaceae species, an endemic of the Fynbos Biome, is threatened by urbanization, alien plant invasion, agricultural expansion, and groundwater extraction. This is further worsened by the semi-arid conditions and hydrological variability factors, which influences species niche dynamics. Therefore, it is important to assess and monitor the Restionaceae species for preservation of their endemism and richness. This study models the hydrological niche and distribution changes of Restionaceae species at the New Years Peak (NYP) at microclimate level for biodiversity conservation. MaxEnt modelling and GIS analytical approaches were applied at various stages in niche modelling process as follows: (i) microclimatic input raster layers’ generation, (ii) ecological modelling and hydrological niche manipulation, and (iii) spatial distributional change mapping. The hydrological niches of the Restionaceae were effectively examined under the recent climate and compared with RCP2.6 and RCP8.5 future climate scenarios as the microscale environmental inputs. The results showed that most of the studied Restionaceae species positioned themselves along a hydrological gradient. Each species tolerated a range of hydrological conditions, which formed their hydrological niche. Changing climate would cause both positive and negative species range shifts. The study assists in plant species conservation and future climate change impact analysis on endangered plant species.

/pdf/the-cape-wetland-restionaceae-of-new-years-peak-under-a-xfh8chngv8.pdf

Guy F. Midgley

Papers

PDEAR model prediction of Protea species in years 2070-2100

If self‐shading is so bad, why is there so much? Short shoots reconcile costs and benefits

Covert symptoms of pollution stress in introduced vegetation near Cape Town.

The Cape Wetland Restionaceae of New Years Peak under a Changing Climate

Scenarios of Plant Diversity in South African Mountain Ranges in Relation to Climate Change