Effects of Extreme Weather Events on Plant Productivity and Tissue Die-Back are Modified by Community Composition

Extreme weather events are expected to increase in frequency and magnitude due to climate change. Their effects on vegetation are widely unknown. Here, experimental grassland and heath communities in Central Europe were exposed either to a simulated single drought or to a prolonged heavy rainfall event. The magnitude of manipulations imitated the local 100-year weather extreme according to extreme value statistics. Overall productivity of both plant communities remained stable in the face of drought and heavy rainfall, despite significant effects on tissue die-back. Grassland communities were more resistant against the extreme weather events than heath communities. Furthermore, effects of extreme weather events on community tissue die-back were modified by functional diversity, even though conclusiveness in this part is limited by the fact that only one species composition was available per diversity level within this case study. More diverse grassland communities exhibited less tissue die-back than less complex grassland communities. On the other side, more diverse heath communities were more vulnerable to extreme weather events compared to less complex heath communities. Furthermore, legumes did not effectively contribute to the buffering against extreme weather events in both vegetation types. Tissue die-back proved a strong stress response in plant communities exposed to 100-year extreme weather events, even though one important ecosystem function, namely productivity, remained surprisingly stable in this experiment. Theories and concepts on biodiversity and ecosystem functioning (insurance hypothesis, redundancy hypothesis) may have to be revisited when extreme weather conditions are considered.     

Dispersal without errors: symmetrical ears tune into the right frequency for survival

Vertebrate animals localize sounds by comparing differences in the acoustic signal between the two ears and, accordingly, ear structures such as the otoliths of fishes are expected to develop symmetrically. Sound recently emerged as a leading candidate cue for reef fish larvae navigating from open waters back to the reef. Clearly, the integrity of the auditory organ has a direct bearing on what and how fish larvae hear. Yet, the link between otolith symmetry and effective navigation has never been investigated in fishes. We tested whether otolith asymmetry influenced the ability of returning larvae to detect and successfully recruit to favourable reef habitats. Our results suggest that larvae with asymmetrical otoliths not only encountered greater difficulties in detecting suitable settlement habitats, but may also suffer significantly higher rates of mortality. Further, we found that otolith asymmetries arising early in the embryonic stage were not corrected by any compensational growth mechanism during the larval stage. Because these errors persist and phenotypic selection penalizes asymmetrical individuals, asymmetry is likely to play an important role in shaping wild fish populations.     

Application of wavelet-based neural network on DNA microarray data

The advantage of using DNA microarray data when investigating human cancer gene expressions is its ability to generate enormous amount of information from a single assay in order to speed up the scientific evaluation process. The number of variables from the gene expression data coupled with comparably much less number of samples creates new challenges to scientists and statisticians. In particular, the problems include enormous degree of collinearity among genes expressions, likely violation of model assumptions as well as high level of noise with potential outliers. To deal with these problems, we propose a block wavelet shrinkage principal component (BWSPCA) analysis method to optimize the information during the noise reduction process. This paper firstly uses the National Cancer Institute database (NC160) as an illustration and shows a significant improvement in dimension reduction. Secondly we combine BWSPCA with an artificial neural network-based gene minimization strategy to establish a Block Wavelet-based Neural Network model in a robust and accurate cancer classification process (BWNN). Our extensive experiments on six public cancer datasets have shown that the method of BWNN for tumor classification performed well, especially on some difficult instances with large-class (more than two) expression data. This proposed method is extremely useful for data denoising and is competitiveness with respect to other methods such as BagBoost, RandomForest (RanFor), Support Vector Machines (SVM), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN).     

Variability in the mating calls of the Lusitanian toadfish Halobatrachus didactylus: cues for potential individual recognition

The mating sounds (boatwhistles) of nesting batrachoidid Halobatrachus didactylus males were recorded in the Tagus Estuary from piers. Thirteen males with 16 boatwhistles per fish were analysed for 20 acoustic features. All variables showed larger between‐male than within‐male variation and differed significantly among individuals. Discriminant function analyses (DFA) considering seven of these variables assigned 90–100% of boatwhistles to the correct individual, depending on the number of males and number of sounds per male included in the model. The acoustic features that consistently best discriminated individuals were the dominant frequency of the middle tonal segment of the boatwhistle (P₂) and dominant frequency modulation, followed by P₂ pulse period, amplitude modulation and sound duration. These results suggest the possibility of individual recognition based on acoustic cues.     

Land use intensification affects soil microbial populations, functional diversity and related suppressiveness of cucumber Fusarium wilt in China’s Yangtze River Delta

The extent of soil microbial diversity in agricultural soils is critical to the maintenance of soil health and quality. The aim of this study was to investigate the influence of land use intensification on soil microbial diversity and thus the level of soil suppressiveness of cucumber Fusarium wilt. We examined three typical microbial populations, Bacillus spp., Pseudomonas spp. and Fuasarium oxysporum, and bacterial functional diversity in soils from three different land use types in China’s Yangtze River Delta, and related those to suppressiveness of cucumber Fusarium wilt. The land use types were a traditional rice wheat (or rape) rotation land, an open field vegetable land, and a polytunnel greenhouse vegetable land that had been transformed from the above two land use types since 1995. Results generated from the field soils showed similar counts for Bacillus spp. (log 5.87–6.01 CFU g⁻¹ dw soil) among the three soils of different land use types, significantly lower counts for Pseudomonas spp. (log 5.44 CFU g⁻¹ dw soil) in the polytunnel greenhouse vegetable land whilst significantly lower counts for Fusarium oxysporum (log 3.21 CFU g⁻¹ dw soil) in the traditional rice wheat (or rape) rotation land. A significant lower dehydrogenase activity (33.56 mg TPF kg⁻¹ dw day⁻¹) was observed in the polytunnel greenhouse vegetable land. Community level physiological profiles (CLPP) of the bacterial communities in soils showed that the average well color development (AWCD) and three functional diversity indices of Shannon index (H′), Simpson index (D) and McIntosh index (U) at 96 h incubation in BIOLOG Eco Micro plates were significantly lower in the polytunnel greenhouse vegetable land than in both the traditional rice wheat (or rape) rotation land and the open field vegetable land. A further greenhouse experiment with the air-dried and sieved soils displayed significantly lower plant growth parameters of 10-old cucumber seedlings as well as significantly lower biomass and total fresh fruit yield at the end of harvesting at day 70 in the polytunnel greenhouse vegetable soil sources. The percentages of Fusarium wilt plant death were greatly increased in the polytunnel greenhouse vegetable plants, irrespective of being inoculated with or without Fusarium oxysporum f. sp. cucumerinum. Our results could provide a better understanding of the effects of land use intensification on soil microbial population and functional diversity as well as the level of soil suppressiveness of cucumber Fusarium wilt.     

Designing better frog call recognition models

Advances in bioacoustic technology, such as the use of automatic recording devices, allow wildlife monitoring at large spatial scales. However, such technology can produce enormous amounts of audio data that must be processed and analyzed. One potential solution to this problem is the use of automated sound recognition tools, but we lack a general framework for developing and validating these tools. Recognizers are computer models of an animal sound assembled from “training data” (i.e., actual samples of vocalizations). The settings of variables used to create recognizers can impact performance, and the use of different settings can result in large differences in error rates that can be exploited for different monitoring objectives. We used Song Scope (Wildlife Acoustics Inc.) to build recognizers and vocalizations of the wood frog (Lithobates sylvaticus) to test how different settings and amounts of training data influence recognizer performance. Performance was evaluated using precision (the probability of a recognizer match being a true match) and sensitivity (the proportion of vocalizations detected) based on a receiver operating characteristic (ROC) curve‐determined score threshold. Evaluations were conducted using recordings not used to build the recognizer. Wood frog recognizer performance was sensitive to setting changes in four out of nine variables, and small improvements were achieved by using additional training data from different sites and from the same recording, but not from different recordings from the same site. Overall, the effect of changes to variable settings was much greater than the effect of increasing training data. Additionally, by testing the performance of the recognizer on vocalizations not used to build the recognizer, we discovered that Type I error rates appear idiosyncratic and do not recommend extrapolation from training to new data, whereas Type II errors showed more consistency and extrapolation can be justified. Optimizing variable settings on independent recordings led to a better match between recognizer performance and monitoring objectives. We provide general recommendations for application of this methodology with other species and make some suggestions for improvements.     

川西亚高山森林凋落物不同分解阶段碳氮磷化学计量特征及种间差异

为了了解亚高山森林凋落物在不同分解阶段的化学计量特征,采用空间代替时间的方法,以自然状态下凋落物的3个层次--新鲜凋落物层（L）、发酵层（F）、腐殖质层（H））模拟凋落物分解的不同阶段,对川西亚高山不同林分类型（岷江冷杉天然林、粗枝云杉人工林、白桦天然林、杜鹃矮曲林）凋落物的碳氮磷（C、N、P）及可溶性碳氮磷（DOC、SN、SP）含量进行研究。结果表明：林分类型及分解阶段将显著影响凋落物分解过程中的碳氮磷含量及其化学计量比。亚高山森林凋落物可溶性有机碳、水溶性磷含量均随着分解过程的不断进行而降低,分解初期快速淋溶,而分解中后期释放变缓。4种林分比较而言,水溶性碳氮磷含量表现为：白桦 > 杜鹃 > 冷杉 > 云杉,阔叶树种凋落物的可溶性碳氮磷普遍高于针叶树种,尤其在分解初期。针叶树种凋落物SN在分解过程中呈现释放模式,而阔叶树种SN呈现先富集后释放模式。凋落物C含量随着分解的不断进行而降低,冷杉、白桦及杜鹃N含量呈现先富集后释放的趋势,分解阶段对云杉与白桦各层P含量影响不显著,但冷杉却呈现先降低后升高的现象,而杜鹃则是在分解后期P含量显著降低。从总体来看,亚高山森林凋落物C/P和N/P均显著小于全球平均水平,凋落物C/N、C/P、N/P、DOC/C、SN/N、SP/P均随着分解的不断进行呈现降低的趋势。分解初期白桦和杜鹃DOC/C显著降低,而冷杉则在分解后期显著降低。冷杉N/P先升高后降低,杜鹃N/P随着分解的不断进行呈现升高趋势。这些研究结果为深入理解亚高山森林生态系统的凋落物分解进程和养分循环提供了依据。     

浙江天台山不同林型土壤环境的微生物区系和细菌生理群的多样性

研究了天台山8种土壤环境的微生物区系,细菌生理群分布、组成和多样性。结果表明：黄山松林、竹林和云锦杜鹃林土壤中细菌、真菌和放线菌数量较多,而柳杉林土壤中较少。微生物数量与土壤有机质、全氮、全磷含量以及土壤凋落物的关系较大。每种土壤环境的细菌、真菌和放线菌占微生物总量的比例为：细菌数量最多,放线菌居中,真菌较少。土壤细菌生理群在天台山8种土壤环境中的分布有较大的差异。好气纤维素分解菌、好气固氮菌、氨化细菌、有机磷分解菌、无机磷分解菌在8种土壤环境中均占有较大的比例,是每种土壤环境的优势菌群,而反硝化细菌和反硫化细菌在每种土壤环境中占有的数量比例均相对较小,处于次要地位。七子花林、竹林、云锦杜鹃林和日本花柏林土壤细菌生理群的Simpson指数和Shannon-Wiener指数均较小,柳杉林、茶园、金钱松林、黄山松林土壤细菌生理群的Simpson指数和Shannon-Wiener指数相对较大。     

Malfunctions

A persistent boast of the historical approach to functions is that functional properties are normative. The claim is that a token trait retains its functional status even when it is defective, diseased, or damaged and consequently unable to perform the relevant task. This is because historical functional categories are defined in terms of some sort of historical success -- success in natural selection, typically -- which imposes a norm upon the performance of descendent tokens. Descendents thus are supposed to perform the associated task even when they cannot. The conceit, then, is that malfunctions are explicable in terms of historical success. The aim of this paper is to challenge this conceit. My thesis is that the historical approach to functions lacks the resources with which to account for the possibility of malfunctions. If functional types are defined in terms of historical success, then tokens that lack the defining property due to defect, and tokens that have lost the defining property due to disease or damage, are excluded from the functional category. Historically based malfunctions, in consequence, are impossible. The historical approach is no better than its non-historical competitors in accounting for the presumed normativity of functional properties.