High‐resolution prediction of leaf onset date in Japan in the 21st century under the IPCC A1B scenario

Reports indicate that leaf onset (leaf flush) of deciduous trees in cool‐temperate ecosystems is occurring earlier in the spring in response to global warming. In this study, we created two types of phenology models, one driven only by warmth (spring warming [SW] model) and another driven by both warmth and winter chilling (parallel chill [PC] model), to predict such phenomena in the Japanese Islands at high spatial resolution (500 m). We calibrated these models using leaf onset dates derived from satellite data (Terra/MODIS) and in situ temperature data derived from a dense network of ground stations Automated Meteorological Data Acquisition System. We ran the model using future climate predictions created by the Japanese Meteorological Agency's MRI‐AGCM3.1S model. In comparison to the first decade of the 2000s, our results predict that the date of leaf onset in the 2030s will advance by an average of 12 days under the SW model and 7 days under the PC model throughout the study area. The date of onset in the 2090s will advance by 26 days under the SW model and by 15 days under the PC model. The greatest impact will occur on Hokkaido (the northernmost island) and in the central mountains.     

Why is the world green? The interactions of top–down and bottom–up processes in terrestrial vegetation ecology

A classic question in plant ecology is “why is the world green?” That is, if plants are food for animals why do not animals eat all the available food – changing a ‘green world’ into a ‘brown world’. We first reviewed this question in 2009 and now revisit our arguments in the light of new data and new thinking. Here we argue that (1) the top–down bottom–up dichotomy is probably too simple for understanding a complex system – such as vegetation – rich in feedback processes. (2) Nevertheless it appears that bottom–up processes are generally more important for maintaining the presence of some sort of vegetation while top–down control process are generally more important in determining the type of vegetation at a site. (3) Although this review mainly takes a qualitative and experimental approach to the question, we also argue that simple well-known mathematical models from population ecology can be very informative in thinking about the types of explanations for the green world phenomenon, and demonstrating that it is rarely a simple choice between one form of control or another.     

How seed traits predict floating times: a biophysical process model for hydrochorous seed transport behaviour in fluvial systems

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Incorporating spatial autocorrelation in rarefaction methods: Implications for ecologists and conservation biologists

Recently, methods for constructing Spatially Explicit Rarefaction (SER) curves have been introduced in the scientific literature to describe the relation between the recorded species richness and sampling effort and taking into account for the spatial autocorrelation in the data. Despite these methodological advances, the use of SERs has not become routine and ecologists continue to use rarefaction methods that are not spatially explicit. Using two study cases from Italian vegetation surveys, we demonstrate that classic rarefaction methods that do not account for spatial structure can produce inaccurate results. Furthermore, our goal in this paper is to demonstrate how SERs can overcome the problem of spatial autocorrelation in the analysis of plant or animal communities. Our analyses demonstrate that using a spatially-explicit method for constructing rarefaction curves can substantially alter estimates of relative species richness. For both analyzed data sets, we found that the rank ordering of standardized species richness estimates was reversed between the two methods. We strongly advise the use of Spatially Explicit Rarefaction methods when analyzing biodiversity: the inclusion of spatial autocorrelation into rarefaction analyses can substantially alter conclusions and change the way we might prioritize or manage nature reserves.     

Spatial and temporal changes in species composition of submersed aquatic vegetation reveal effects of river restoration

We examined temporal changes in spatial patterns of submersed aquatic vegetation (SAV) in response to the restoration of geomorphic habitat in Navigation Pool 8 of the Upper Mississippi River from 1998 to 2016. The frequency of occurrence and species composition of SAV at sampling sites were spatially interpolated for each year to create annual maps. Linear models were fitted to temporal changes in SAV within each map pixel. The frequency of occurrence of SAV (across all species) increased over time in much of the impounded region of the pool, including areas near restored islands. However, impounded areas maintained a relatively consistent species composition over time, with species known to be tolerant of higher flow velocities (>0.10 m/second) and wind fetch distances (>1,000 m) (e.g. Vallisneria americana) being most abundant. In contrast, areas protected by newly constructed islands transitioned from V. americana to species found in other protected backwater habitats and known to be intolerant of high flow velocities and wind fetch distances (e.g. Ceratophyllum demersum). The results suggest that previously reported improvements in water clarity may have improved growing conditions for all SAV species, especially in the lower impounded portion of the pool, while island restoration created more backwater‐like habitats and facilitated changes in species composition. Assessing changes in SAV occurrence alone offers only a partial view of local‐scale river restoration (e.g. island building), while analyses of species composition are likely to be more indicative of the types of changes (i.e. reduced flow velocity and wind fetch) associated with restoring geomorphic habitat.     

Composition and ecological drivers of the kwongan scrub and woodlands in the northern Swan Coastal Plain,Western Australia

The nature of community patterns and environmental drivers in kwongan mediterranean‐type shrubland on nutrient‐poor soils occurring in Western Australia remain poorly examined. We aimed to determine whether (i) classification of the kwongan vegetation of the northern Swan Coastal Plain would be ecologically informative and (ii) which environmental drivers underpin the plant community patterns. The study area was positioned on the northern Swan Coastal Plain, locality of Cooljarloo (30°39′ S, 115°22′ E), situated 170 km north of Perth, Western Australia. Compositional (518 species × 337 relevés) and environmental data set (29 variables × 87 relevés) describing time since last fire, soil chemical and physical properties, and terrain characteristics were analysed using classification and ordination techniques. OptimClass assisted in the selection of a robust data transformation, resemblance function and clustering algorithm to identify the vegetation patterns. Major ecological drivers of the vegetation patterns were detected using distance‐based redundancy analysis (db‐RDA). Classification revealed major groupings of Wet Heath and Banksia Woodland distinguishable by the high prevalence of myrtyoid and proteoid taxa, respectively. On floristic‐sociological grounds, we recognised four Wet Heath and two Banksia Woodland communities. The Wet Heath was constrained to areas of higher litter depth (db‐RDA axis 1: 9%). Soil chemical and physical properties explained the highest proportion (17%) of the compositional variance, while the terrain‐ and fire‐related variables explained 2% and <0.001%, respectively. While fire explained little compositional variance overall, a separate db‐RDA analysis found that it may play an important pattern‐structuring role within Banksia Woodlands. Fine‐scale compositional patterns correspond only to a small extent to environmental data; the substantial unexplained variance may be due to slow‐acting neutral and stochastic processes.     

Urbanization and Mismatch with Protected Areas Place the Conservation of a Threatened Species at Risk

In this work, we report the predicted distribution of the threatened fluminense swallowtail butterfly, Parides ascanius (Cramer 1775), and correlate it to the presence of urban and protected areas within its range. The distribution was modeled using a genetic algorithm. The predicted distribution of the fluminense swallowtail shows high agreement within Rio de Janeiro state, in a near-continuous strip of 2,038,253 ha along the coastal lowlands, 17.8 percent of which is within urban areas. Only 8.7 percent (178,187 ha) of the remaining (nonurban) predicted model overlapped at least partially with protected areas (19 in all). Almost half of these protected areas also overlapped with urban areas, resulting in an additional loss of 58,751 ha. In seven of 19 protected areas, the distribution of P. ascanius was predicted by less than 50 percent of the models; five of the remaining protected areas are less restrictive reserves. Despite the wide distribution predicted by the models, only two of the observed occurrence points matched the predicted distribution within protected areas. Modeling threatened species distribution is a useful tool for highlighting gaps in networks of protected areas and should aid in planning to fill these gaps. However, in several developing countries with high biodiversity, there is insufficient basic biological information for many threatened species. In these cases, prospecting field studies are urgently needed.     

Response of Amazonian forests to mid-Holocene drought: A model-data comparison

There is a major concern for the fate of Amazonia over the coming century in the face of anthropogenic climate change. A key area of uncertainty is the scale of rainforest dieback to be expected under a future, drier climate. In this study, we use the middle Holocene (ca. 6000 years before present) as an approximate analogue for a drier future, given that palaeoclimate data show much of Amazonia was significantly drier than present at this time. Here, we use an ensemble of climate and vegetation models to explore the sensitivity of Amazonian biomes to mid-Holocene climate change. For this, we employ three dynamic vegetation models (JULES, IBIS, and SDGVM) forced by the bias-corrected mid-Holocene climate simulations from seven models that participated in the Palaeoclimate Modelling Intercomparison Project 3 (PMIP3). These model outputs are compared with a multi-proxy palaeoecological dataset to gain a better understanding of where in Amazonia we have most confidence in the mid-Holocene vegetation simulations. A robust feature of all simulations and palaeodata is that the central Amazonian rainforest biome is unaffected by mid-Holocene drought. Greater divergence in mid-Holocene simulations exists in ecotonal eastern and southern Amazonia. Vegetation models driven with climate models that simulate a drier mid-Holocene (100–150 mm per year decrease) better capture the observed (palaeodata) tropical forest dieback in these areas. Based on the relationship between simulated rainfall decrease and vegetation change, we find indications that in southern Amazonia the rate of tropical forest dieback was ~125,000 km² per 100 mm rainfall decrease in the mid-Holocene. This provides a baseline sensitivity of tropical forests to drought for this region (without human-driven changes to greenhouse gases, fire, and deforestation). We highlight the need for more palaeoecological and palaeoclimate data across lowland Amazonia to constrain model responses.