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Water management received increasing attention in the last decades since it is a key to coping with climate change and global warming. Within this framework, water scarcity will be one of the main issues to be addressed by humans, mainly because of its subsequent effects on, but not limited to, the agricultural sector. To tackle this challenge, the Drought Observatory (DO) of CNR-IBE developed an operational climate service to provide seasonal forecasting, of the Standardized Precipitation Index (SPI) to support drought risk management over the Mediterranean area. The forecast tool stands on the most recent and evolute version of the ECMWF numerical seasonal forecast system, named SEAS, (5 and 5.1). Each month, from 2017 onwards, SEAS5 provides an ensemble of 51 members of daily simulations, lasting seven months each; these simulations are freely accessible from the Copernicus Data Store (CDS). In addition, from 1981 to 2016, CDS provided a hindcast of 25 members simulation runs (named System 4). SEAS daily precipitation seasonal forecasts, with a horizontal resolution of 1°x1°, are then bias adjusted using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset (version 2.8). MSWEP is a global precipitation product with an original 3-hourly, 0.1° resolution available from 1979 to the present; it merges gauges, satellite, and reanalysis data to obtain high-quality precipitation estimates at every location. The bias adjustment is computed using the CSTools R Package (CSTools: Assessing Skill of Climate Forecasts on Seasonal-to-Decadal Timescales) applying a quantile-quantile mapping algorithm. This algorithm adjusts/corrects the quantiles of the modelled distribution (the raw SEAS5 daily precipitation distribution) by using an observed distribution set as a reference (the MSWEP daily precipitation distribution). Thus each SEAS5 grid-points of each ensemble member is 1) reprojected onto the highest resolution MSWEP dataset, and then 2) the resulting high-resolution daily time-series precipitation distribution is adjusted using a quantile transformation. A 1981 – 2016 period is selected to adjust and train the quantile mapping algorithm. From the resulting high resolution and bias-adjusted daily rainfall forecast dataset, we then compute the SPI index for a series of timescales: 1, 3 and 6 months, for the period 1981 onwards. From the verification analysis seasonal forecast skills vary on time and geographical areas. It is thus possible to identify windows of opportunity for specific tasks in cooperation with users. Within this framework, bias-corrected seasonal forecasts are valuable supporting information for water resources management and decision-making processes. During the drought that occurred in the summer of 2022, the DO was widely used by national and international media to deliver accurate information on the drought trend. This fact underlines the need for timely and science-based data to inform also the wider public. These new bias-adjusted forecasts, along with the empirical seasonal forecasts and other monitoring drought and vegetation indices, will be freely accessible through the Drought Observatory Climate Service (https://drought.climateservices.it).

Tumours are nowadays the second world-leading cause of death after cardiovascular diseases. During the last decades of cancer research, lifestyle and random/genetic factors have been blamed for cancer mortality, with obesity, seden-tary habits, alcoholism, and smoking contributing as supposed major causes. However, there is an emerging consensus that environmental pollution should be considered one of the main triggers. Unfortunately, all this preliminary scien-tific evidence has not always been followed by governments and institutions, which still fail to pursue research on can-cer's environmental connections. In this unprecedented national-scale detailed study, we analyzed the links between cancer mortality, socio-economic factors, and sources of environmental pollution in Italy, both at wider regional and finer provincial scales, with an artificial intelligence approach. Overall, we found that cancer mortality does not have a random or spatial distribution and exceeds the national average mainly when environmental pollution is also higher, despite healthier lifestyle habits. Our machine learning analysis of 35 environmental sources of pollution showed that air quality ranks first for importance concerning the average cancer mortality rate, followed by sites to be reclaimed, urban areas, and motor vehicle density. Moreover, other environmental sources of pollution proved to be relevant for the mortality of some specific cancer types. Given these alarming results, we call for a rearrangement of the priority of cancer research and care that sees the reduction and prevention of environmental contamination as a priority action to put in place in the tough struggle against cancer.

Even though a large part of the Italian peninsula is characterized by a Mediterranean climate intrinsically highly suitable for olive and grapevine cultivation, farmers may experience variable agronomic and management costs due to interannual yield variability. A synoptic picture of major climate stressors and their ongoing impacts on olive and grapevine yield variability at a broad spatio-temporal scale are scarce, but, if identified, could enhance the development of actionable services to alert stakeholders of potential climate risks. We analyzed Italian olive and grapevine yield data from the Italian National Statistics Institute (ISTAT), aggregated at the provincial level, during 2006-2021, and several climatic variables from Reanalysis v5 (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWR) to i) explore yields trends and inter-annual variations over the whole peninsula; ii) identify major climate stressors likely responsible for the largest drops in yield; iii) build a composite index that summarizes the risk of having exceptionally low yields due to the occurrence of multiple climate stressors. To this end, we defined two major classes of yield, namely exceptionally low and high yields (LY and HY, respectively), and explored the climatic variables, aggregated on a monthly (grapevine) and bimonthly (olive) time scale, determining yield in outcomes. It is worth noting that the use of monthly or bimonthly periods provides a means of examining the seasonal effects of stressors while providing the basis for near-real-time forecasting. Selected years, characterized by a conspicuous number of both LY and HY, were focused to examine whether the composite risk index has application at more local scales. Results are discussed and some possible explanations based on the current knowledge of olive and grapevine physiological developmental. We suggest our approach as a promising yet still in-progress work that could pave the way to an integrated meteorological seasonal forecast system to provide timely insight on factors affecting within-season yield development.

Even though a large part of the Italian peninsula is characterized by a Mediterranean climate intrinsically highly suitable for olive cultivation, farmers may experience variable agronomic and management costs due to interannual yield variability. A synoptic picture of major climate stressors and their ongoing impacts on olive yield variability at a broad spatio-temporal scale are scarce, but, if identified, could enhance the development of actionable services to alert stakeholders of potential climate risks. We analyzed Italian olive yield data from the Italian National Statistics Institute (ISTAT), aggregated at the provincial level, during 2006-2020, and several climatic variables from Reanalysis v5 (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWR) to i) explore olive yields trends and inter-annual variations over the whole peninsula; ii) identify major climate stressors likely responsible for the largest drops in yield; iii) build a composite index that summarizes the risk of having exceptionally low yields due to the occurrence of multiple climate stressors; to this end, we defined two major classes of yield, namely exceptionally low and high yields (LY and HY, respectively), and explored the climatic variables, aggregated on a bimonthly time scale, determining yield in outcomes. It is worth noting that the use of bimonthly periods provides a means of examining the seasonal effects of stressors while providing the basis for near-real-time forecasting. Moreover, five years (i.e., 2009, 2011, 2014, 2018, and 2019) characterized by a conspicuous number of both LY and HY were focused to examine whether the composite risk index has application at more local scales. Results are discussed and some possible explanations based on the current knowledge of olive developmental ecology are provided. We suggest our approach as a promising yet still-in-progress work that could pave the way to an integrated meteorological seasonal forecast system to provide timely insight on factors affecting within-season yield development.

The calibration of a reliable phenological model for olive grown in areas characterized by great environmental heterogeneity, like Italy, where many varieties exist, is challenging and often suffers from a lack of observations, especially on budbreak. In this study, we used a database encompassing many phenological events from different olive varieties, years, and sites scattered all over Italy to identify the phases in which site-enlarged developmental rates can be well regressed against air temperature (Developmental Rate function, DR) by testing both linear and nonlinear functions. A K-fold cross-validation (KfCV) was carried out to evaluate the ability of DR functions to predict phenological development. The cross-validation showed that the phases ranging from budbreak (BBCH 01 and 07) to flowering (BBCH 61 and 65) and from the beginning of flowering (BBCH 51) to flowering can be simulated with high accuracy (r2 = 0.93-0.96, RMSE = 3.9–6.6 days) with no appreciable difference among linear and nonlinear functions. Thus, the resulting DRs represent a simple yet reliable tool for regional phenological simulations for these phases in Italy, paving the way for a reverse modeling approach aimed at reconstructing the budbreak dates. By contrast, and despite a large number of phases explored, no appreciable results were obtained on other phases, suggesting possible interplays of different drivers that need to be further investigated.

A relatively small number of broad-scale patterns describe the distribution of biodiversity across the earth. All of them explore biodiversity focusing on a mono or bi-dimensional space. Conversely, the volume of the forests is rarely considered. In the present work, we tested a global correlation between vascular plant species richness (S) and average forest canopy height (H), the latter regarded as a proxy of volume, using the NASA product of Global Forest Canopy Height map and the global map of plant species diversity. We found a significant correlation between H and S both at global and macro-climate scales, with strongest confidence in the tropics. Hence, two different regression models were compared and discussed to provide a possible pattern of the H–S relation. We suggested that the volume of forest ecosystems should be considered in ecological studies as well as in planning and managing natural sites, although in this first attempt, we cannot definitively prove our hypothesis. Again, high-resolution spatial data could be highly important to confirm the H–S relation, even at different scales.

In a previous work we developed a mathematical model to explain the co‐occurrence of evergreen and deciduous oak groups in the Mediterranean region, regarded as one of the distinctive features of Mediterranean biodiversity. The mathematical analysis showed that a stabilizing mechanism resulting from niche difference (i.e. different water use and water stress tolerance) between groups allows their coexistence at intermediate values of suitable soil water content. A simple formal derivation of the model expresses this hypothesis in a testable form linked uniquely to the actual evapotranspiration of forests community. In the present work we ascertain whether this simplified conclusion possesses some degree of explanatory power by comparing available data on oaks distributions and remotely sensed evapotranspiration (MODIS product) in a large‐scale survey embracing the western Mediterranean area. Our findings confirmed the basic assumptions of model addressed on large scale, but also revealed asymmetric responses to water use and water stress tolerance between evergreen and deciduous oaks that should be taken into account to increase the understating of species interactions and, ultimately, improve the modeling capacity to explain co‐occurrence.

BACKGROUND: A likely increasing demand for varieties mixtures, landraces and genetic diversity in cropping systems will underpin calls for models able to generalize phenological development at the species level, at the same time as providing the expected range of phenological variability. In the present article, we aimed to obtain a generalized phenological model of durum wheat (Triticum durum, Desf.). RESULTS: Using a large phenological dataset embracing field data collected under different sowing dates, varieties and locations over the Italian peninsula, we searched for the phenophases enabling the best linear approximations between developmental rates and air temperature, aiming to minimize the residual variability from drivers other than temperature, as genetic and environmental diversity. The developmental rates of the resulting phases were then examined with respect to the mean daylength to determine possible additional relations with photoperiod. If a correlation with daylength was also present, the developmental rate is calibrated by multiple linear regression, or otherwise by simple linear regression of temperature. The resulting calibration, tested on an independent data subset, confirms that the model is able to generalize wheat development over the Italian peninsula with high accuracy (mean absolute error =3–8 days; r2 = 0.75–0.98), regardless of the wheat variety. CONCLUSION: The generalized phenological model is potentially suitable for many agro-ecological and large-scale applications. It is hoped that the model will aid in situations where phenological observations to parameterize a model are still lacking, as is probably the case for landraces and underutilized crop varieties. © 2019 Society of Chemical Industry.

This is the pre-print version of the chapter"Climate change sustainable agriculture and food systems_The world after the Paris agreement"published as final paper inAchieving the Sustainable Development Goals Through Sustainable Food Systems,10 October 2019, Pages 1-262https://doi.org/10.1007/978-3-030-23969-5 lntroduction 1.1 Climate Change and Agriculture According to the fifth assessment report of the IPCC (2014), the concentrations of greenhouse gases (GHG) in the atmosphere are at the highest they have been in the past 800 thousand years. Current levels of CO2 have increased by 30% from 280 ppm in pre-industrial times to 407 ppm today (2019), and they continue to rise. Present CH4 concentrations of 2000 ppb are nearly triple their pre-industrial value of 700 ppm. N2O levels reached 328 ppb in 2019 compared with the 280 ppb of pre-industrial time. Only in the past 50 years we have doubled human population (from 4 to 7 billion), increased GHG emissions by 2.5 times, doubled freshwater withdrawal, halved the agricultural land per capita (from 1. 4 to 0.7 ha) by agricul­ture intensification. This is an unprecedented velocity of transformation that our Planet and human society had never experienced. Today, the agro-food sector alone accounts for some 80% of the world freshwa­ter use, 30% of world energy demand, and more than 12% of man-made greenhouse gas emissions worldwide, including indirect emissions such as those of deforesta­tion (Foley et al. 2011). Moreover, according to the Food and Agriculture Organization (FAO) of the United Nations, croplands and pastures occupy about 38% of Earth's terrestrial surface, the largest use of land on the planet (Foley et al. 2011). With global food production expected to increase 70% by 2050, and consid­ering the meat dietary changes, the sector is facing unprecedented resource pres­sures and strong perturbations to the climate systems. By 2050 more than nine billion of people will be in search of food and most of them (68%) will be living in mega-cities (UN DESA 2018). Under these circumstances, a substantial redefinition of the actual food supply chain is essential. Meanwhile, many rural-communities, which strongly depends on domestic-subsistence agriculture, will be exposed to food scarcity and accessibility. lndeed, in some regions of the world (i.e. tropics and part of temperate regions) increasing of climate extremes will produce adverse effects on agriculture, forestry and fisheries sectors with yield reduction of 35% in African countries and 2% globally per decade, despite the increasing food demand (Barros et al. 2014). It is time to reflect on the global agro-food systems, its para­ doxes, inequalities and capacities to support future generations. On the other side global warming can also expand the land suitability for some crops, like wheat at high latitudes (Di Paola et al. 2018). Humanity needs to act urgently and fast, push­ ing the high-level governmental agenda (SDGs, Climate Paris agreement) as well as industry sector and citizens in the most difficult and challenging transformation of our society to feed the new two billion of people expected by 2050 and, at same time, stabilize climate below 2.0° (possibly 1.5°) and reducing the pressures on natural resources. 1.2The Paris Agreement: Implications for the Agriculture Sector The significant role that agriculture can play in climate change was relatively under­ represented in the previous discussions and decisions under the frame of the United Nations Framework Convention on Climate Change (UNFCCC), before the Paris Agreement (PA) was adopted by the 21st Conference of the Parties (COP21) of the UNFCCC on the 12th of December 2015. Food security and food production are explicitly mentioned in the PA: in its pre­ amble the Parties recognize "the fundamental priority of safeguarding food security and ending hunger, and the particular vulnerabilities of food production systems to the adverse impacts of climate change", and the Artide 2 highlights the importance of "increasing the ability to adapt to the adverse impacts of climate change and foster climate resilience and low greenhouse gas emissions development, in a man­ ner that does not threaten food production". Moreover, the role of agriculture in the PA is also linked to the capacity to be a possible sink and reservoir of GHG in soils and vegetation biomass: in the Artide 5 Parties are invited to "take action to con­ serve and enhance, as appropriate, sinks and reservoirs of greenhouse gases". This opened to agriculture as a key sector, not only impacted by climate change but also with a great potential for mitigation. In fact, more in generai, the PA has the ambition to keep "the increase in the global average temperature to well below 2 °C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5 °C above pre-industrial levels, recognizing that this would significantly reduce the risks and impacts of climate change". In order to achieve this long-term temperature goal, parties are therefore committed to achieve a balance between anthropogenic emissions by sources and removals by sinks in the second half of this century. Different options are feasible and the agriculture sector can play an active role, in particular by contributing to the net emissions reduction, while guaranteeing food security. Besides, under the UNFCCC processa specific programme on agriculture was launched (the "Koronivia Joint Work on Agriculture", see Box below) that provides space and opportunities for Parties to fully engage on climate related discussions in the agriculture sector in order to foster actions related to adaptation and mitigation taking into consideration the sectors vulnerabilities. In pathways limiting global warrning to 1.5 °C with limited or no overshoot, Agriculture, Forestry and Other Land-Use (AFOLU) related carbon dioxide removal measures are projected to remove 0-5, 1-11, and 1-5 GtCO2 year- 1 in 2030, 2050, and 2100 respectively (IPCC 2018). Due to this key potential role plaid by the agri­ culture, the 80% of the Nationally Determined Contributions (NDCs) submitted by countries committed to actions on agricultural mitigation, and 90% ofNDCs selected agriculture as a priority sector for action on adaptation (CCAFS 2016; FAO 2016). Despite the inclusion of agriculture and the land sector in general in most NDC, this is still not enough to achieve the 2 °C goal and additional reduction targets are needed (Fujimori et al. 2016). According to the Fujimori et al. (2016) modelling exercise, large-scale negative CO2 emissions and land-based CO2 emissions­ reduction measures are required and the bioenergy crops need to be an important component in addition to agriculture, with an area of cropland used for bioenergy to be 24-36% of the total cropland. Indeed, the deployment of such a large-scale land­ related measures, like afforestation and bioenergy supply, can compete with food production raising not only food security concerns (IPCC 2018) but also increasing the environmental footprint, due to the increased use of natural resource (e.g. water and nitrogen for bioenergy crops) and environmental impacts (e.g. loss of biodiver­sity and increasing pollution from fertilizers). Furthermore, climate-related risks on food security are projected to increase under global warming, with projected net reductions in maize, rice, wheat and, potentially, other cereal yields in many regions of the world (IPCC 2018). Studies based on multi-model inter-comparisons (Schleussner et al. 2016) fore­ see a general crop yield reduction, particularly in tropics, for maize and wheat under increasing temperature scenarios, with more significant reductions projected at 2 °C than 1.5 °C. On the other side, local rice and soy yields are projected to increase in the tropics, as the positive effect of CO2 fertilization counterbalances the detrimen­tal impacts of climate change in the model projections (Schleussner et al. 2016). However, additional gains for warming above 1.5 °C resulted not significant, and yield reductions are expected for all the four widespread global crops. In the light of these evidences, trade-offs between mitigation and adaptation, as well as economie and environmental benefits, need to be addressed when balancing the need of land for bioenergy crops, reforestation or afforestation, versus the land needed for agricultural adaptation under a changing climate, not undermining food security, livelihoods, ecosystem functions and services and other aspects of sustain­able development.

Phenological field observations were used to parameterize and test a Degree Day model for durum wheat (Triticum durum, Desf.) across the Italian peninsula. Data come from different experimental fields located in northern, central and southern Italy and encompass scalar sowing dates, allowing to parameterize a model representative for a wide range of sites and growing seasons. To assure the basic assumption of the Degree Day method, wheat subphases were identified to enable the best linear relationship (less data dispersion and higher coefficient of determination) between developmental rates and average air temperature. Under proven linear temperature responses, the model gave satisfactory simulations of wheat phenology over different locations and sowing period (R2 = 0.96; RMSE = 8,4 days; no bias, minimal complexity), even if the heat supply (growing degree days counted from 0°C) was very different among sites.

Summary In this paper, an updated global map of the current climate zoning and of its projections, according to the Koppen–Geiger classification, is first provided. The map at high horizontal resolution (0.5° × 0.5°), representative of the current (i.e. 1961–2005) conditions, is based on the Climate Research Unit dataset holding gridded series of historical observed temperature and precipitation, while projected conditions rely on the simulated series, for the same variables, by the General Circulation Model CMCC-CM. Modeled variables were corrected for their bias and then projections of climate zoning were generated for the medium term (2006–2050) and long term (2056–2100) future periods, under RCP 4.5 and RCP 8.5 emission scenarios. Results show that Equatorial and Arid climates will spread at the expenses of Snow and Polar climates, with the Warm Temperate experiencing more moderate increase. Maps of climate zones are valuable for a wide range of studies on climate change and its impacts, especially those regarding the water cycle that is strongly regulated by the combined conditions of precipitation and temperature. As example of large scale hydrological applications, in this work we tested and implemented a spatial statistical procedure, the geographically weighted regression among climate zones’ surface and mean annual discharge (MAD) at hydrographic basin level, to quantify likely changes in MAD for the main world rivers monitored through the Global Runoff Data Center database. The selected river basins are representative of more than half of both global superficial freshwater resources and world’s land area. Globally, a decrease in MAD is projected both in the medium term and long term, while spatial differences highlight how some areas require efforts to avoid consequences of amplified water scarcity, while other areas call for strategies to take the opportunity from the expected increase in water availability. Also the fluctuations of trends between the medium to long term time frames is viewed in order to pay attention to the lifespan of investments and decisions. Despite preliminary and illustrative, this study suggests how large scale valuable information can be extracted even through indicators derived from statistical spatial modeling procedures, without the pretention to replace more sophisticated studies that allow process based reproduction of inter-annual and intra-annual discharge variability, and on which a robust and comprehensive assessment of future water resource reliability should be however also based.

This is the pre-print version of the article "The expansion of Russian wheat land suitability in response to climate change", published as final paper in Land Use PolicyVolume 78, November 2018, Pages 70-77https://doi.org/10.1016/j.landusepol.2018.06.035 ABSTRACT We provided an examination of the past-to-future thermal suitability for winter wheat cultivation over Russia to better understand how the increasing warming is affecting the Russian wheat sector and discuss some realistic opportunities. The analyses were carried out by means of a multi-model ensemble analysis from the most updated bias-corrected outputs from five CMIP5 Earth System Models (1951-2099) under two representative concentration pathways (RCP 4.5 and RCP 8.5), and the Era-Interim dataset (1979-2016). We found an average increase of thermal suitability of 10 Mha per decade over the past 30 years, consistently with the observed switch from spring to the higher yielding winter. An increase of thermal suitability toward the north-western regions and in the Far East also resulted from future projections. Conversely, an increasing risk of extreme heat events is expected to occur in the southern regions of Russia, which currently represent the most productive and intensively managed wheat cultivation area. We discussed in deep some adaptation strategies able both to take advantage of the agricultural expansion and to face the increasing temperature extremes, such as the agricultural land recovery, identification of the optimal sowing period, crop and variety switching.

Currently, a large portion of grain production is funneled into animal feed despite widespread hunger and undernutrition. In the present work we: (i) estimated the area, water and carbon footprints of animal-source proteins (AP) obtained from intensive farming systems and compared them with those from producing an equivalent amount of plant-source proteins (PP); (ii) postulated a set of straightforward hypotheses to recover environmental resources by cutting down a surplus in the per capita protein intake from three representative regions where intensive animal farming systems account for a great share of animal food production. Our major findings revealed that AP from intensive farming were approximately 2.4 to 33 more expensive in terms of area and water demand and 2.4 to 240 more pollutant in terms of greenhouse gas emissions when compared with PP. Environmental recoveries varied widely according to the hypothesized scenarios, but even the lowest estimates suggested remarkable results. Whether additional proteins supply would be required, crops with large protein content as peas, chickpeas, soybeans, and lupins could help to meet food security, while better compromise between dietary habits and environmental protection could be reached in rich countries by a moderate consumption of meat produced with non-feed grain systems.

A new algorithm for the estimation of atmospheric temperature (T) and water vapor (WV) vertical profiles in nonprecipitating conditions is presented. The microwave random forest temperature and water vapor (MiRTaW) profiling algorithm is based on the random forest (RF) technique and it uses microwave (MW) sounding from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. Three different data sources were chosen for both training and validation purposes, namely, the ERA-Interim from the European Centre for Medium-Range Weather Forecasts (ECMWF), the Infrared Atmospheric Sounding Interferometer Atmospheric Temperature Water Vapour and Surface Skin Temperature (IASI L2 v6) from the Meteorological Operational satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the radiosonde observations from the Integrated Global Radiosonde Archive (IGRA). The period from 2012 to 2016 was considered in the training dataset; particular attention was paid to the instance selection procedure, in order to reduce the full training dataset with negligible information loss. The out-of-bag (OOB) error was computed and used to select the optimal RF parameters. Different RFs were trained, one for each vertical level: 32 levels for T (within 10–1000 hPa) and 23 levels for WV (200–1000 hPa). The validation of the MiRTaW profiling algorithm was conducted on a dataset from 2017. The mean bias error (MBE) of T vertical profiles ranges within about (−0.4–0.4) K, while for the WV mixing ratio, the MBE starts at ~0.5 g/kg near the surface and decreases to ~0 g/kg at 200 hPa level, in line with the expectations.

Tropical rainforests are among the most threatened ecosystems by large-scale fragmentation due to human activity such as heavy logging and agricultural clearance. Although, they provide crucial ecosystem goods and services, such as sequestering carbon from the atmosphere, protecting watersheds and conserving biodiversity. In several countries forest resource extraction has experienced a shift from clearcutting to selective logging to maintain a significant forest cover and understock of living biomass. However the knowledge on the short and long-term effects of removing selected species in tropical rainforest are scarce and need to be further investigated. One of the main effects of selective logging on forest dynamics seems to be the local disturbance which involve the invasion of open space by weed, vines and climbers at the expense of the late-successional state cenosis. We present a simple deterministic model that describes the dynamics of tropical rainforest subject to selective logging to understand how and why weeds displace native species. We argue that the selective removal of tallest tropical trees carries out gaps of light that allow weeds, vines and climbers to prevail on native species, inhibiting the possibility of recovery of the original vegetation. Our results show that different regime shifts may occur depending on the type of forest management adopted. This hypothesis is supported by a dataset of trees height and weed/vines cover that we collected from 9 plots located in Central and West Africa both in untouched and managed areas.

The scientific community offers numerous crop models with different levels of sophistication. In such a wide range of crop models, users should have the possibility to choose the most suitable, in terms of detail, scale and representativeness, to their objectives. However, even when an appropriate choice is made, model limitations should be clarified such that modelling studies are put in the proper perspective and robust applications are achieved. This work is an overview of available models to simulate crop growth and yield. A summary matrix with more than 70 crop models is provided, storing the main model characteristics that can help users to choose the proper tool according to their purposes. Overall, we found that two main aspects of models, despite their importance, are not always clear from the published references, i.e. the versatility of the models, in terms of reliable transferability to different conditions, and the degree of complexity. Hence, the developers of models should be encouraged to pay more attention to clarifying the model limitations and limits of applicability, and users should make an effort in proper model selection, to save time often devoted to iteration of tuning steps to force an inappropriate model to be adapted to their own purpose.

Tropical deforestation is well known to have serious negative consequences for biodiversity, terrestrial carbon sinks and the balance of atmospheric greenhouse gases. By contrast, selective logging of tropical forests is often regarded as having a lesser impact on the ecosystem particularly in long terms, even though there have been few critical evaluations of the practice, particularly in Africa. We compared field data from 511 plots in the tropical forest of Sierra Leone, Ghana, Cameroon and Gabon. These plots were subject to different forest management practices: no recent logging (primary forests), selective logging (up to 30years old) and re-grown secondary forests post clear-cutting (at least 20years ago). Our findings suggest that the vertical structure and plant richness of the selectively logged and secondary forests change in different amplitude from those of primary forests, but stem density and the prevalence of vine and weed species differ greatly. We show that the effects of selective logging are greater than those expected simply from the removal of commercial species, and can persist for decades. Selective logging, unless it is practiced at very low harvest intensities, can significantly reduce the biomass of a tropical forest for many decades, seriously diminishing aboveground carbon storage capacity, and create opportunities for weeds and vines to spread and slow down the ecological succession.

An error was introduced in the preparation of this article for publication. Equations 1 and 2 in the Materials and Methods section under the subheading "0.1 Model formation" should not begin with a minus sign. Please view the corrected equations 1 and 2 here

The Mediterranean region is one of the hot spots of climate change. This study aims at understanding what are the conditions sustaining tree diversity in Mediterranean wet forests under future scenarios of altered hydrological regimes. The core of the work is a quantitative, dynamic model describing the coexistence of different Mediterranean tree species, typical of arid or semi-arid wetlands. Two kind of species, i.e. Hygrophilous (drought sensitive, flood resistant) and Non-hygrophilous (drought resistant, flood sensitive), are broadly defined according to the distinct adaptive strategies of trees against water stress of summer drought and winter flooding. We argue that at intermediate levels of water supply the dual role of water (resource and stress) results in the coexistence of the two kind of species. A bifurcation analysis allows us to assess the effects of climate change on the coexistence of the two species in order to highlight the impacts of predicted climate scenarios on tree diversity. Specifically, the model has been applied to Mediterranean coastal swamp forests of Central Italy located at Castelporziano Estate and Circeo National Park. Our results show that there are distinct rainfall thresholds beyond which stable coexistence becomes impossible. Regional climatic projections show that the lower rainfall threshold may be approached or crossed during the XXI century, calling for an urgent adaptation and mitigation response to prevent biodiversity losses.