Increased Dust Deposition in New Zealand Related to Twentieth Century Australian Land Use

Mineral aerosols (dust) generated in the dryland regions of Australia have the potential to reach New Zealand through atmospheric transport. Although a large portion of dust in New Zealand originates in Australia, little is known about how dust deposition has varied over time in New Zealand or what may have caused this variation. We used geochemical dust proxies to examine the recent history of dust deposition to two alpine lakes in Kahurangi National Park, South Island, New Zealand. Geochemical indicators suggest that dust deposition began to increase around 1900, with the greatest deposition rates occurring from ~1920 to ~1990. In subsequent decades, dust deposition rates to New Zealand lakes appear to have declined. This rise and fall of dust deposition recorded in New Zealand lakes is consistent with dust records from the Antarctic Ice Sheet, Eastern Australia, and incidents of low visibility due to dust events recorded at Australian climate stations. The dust deposition rate over time also follows the temporal pattern of land use in south and central Australia over the time scale of the twentieth century suggesting a causal linkage. It is possible, and perhaps likely, that drought cycles also affected both emissions and transport pathways but over shorter time periods this was difficult to discern at the temporal resolution of these lake sediment cores. The increase in dust deposition to the high‐elevation regions of New Zealand likely has implications for the biogeochemistry of alpine lakes in the Tasman Mountains

Predicting tree distributions in an East African biodiversity hotspot : model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix. (C) 2008 Elsevier B.V. All rights reserved

GLOWORM-PARA: a flexible framework to simulate the population dynamics of the parasitic phase of gastrointestinal nematodes infecting grazing livestock

Gastrointestinal (GI) nematodes are a significant threat to the economic and environmental sustainability of keeping livestock, as adequate control becomes increasingly difficult due to the development of anthelmintic resistance (AR) in some systems and climate-driven changes to infection dynamics. To mitigate any negative impacts of climate on GI nematode epidemiology and slow AR development, there is a need to develop effective, targeted control strategies that minimise the unnecessary use of anthelmintic drugs and incorporate alternative strategies such as vaccination and evasive grazing. However, the impacts climate and GI nematode epidemiology may have on the optimal control strategy are generally not considered, due to lack of available evidence to drive recommendations. Parasite transmission models can support control strategy evaluation to target field trials, thus reducing the resources and lead-time required to develop evidence-based control recommendations incorporating climate stochasticity. GI nematode population dynamics arising from natural infections have been difficult to replicate and model applications have often focussed on the free-living stages. A flexible framework is presented for the parasitic phase of GI nematodes, GLOWORM-PARA, which complements an existing model of the free-living stages, GLOWORM-FL. Longitudinal parasitological data for two species that are of major economic importance in cattle, Ostertagia ostertagi and Cooperia oncophora, were obtained from seven cattle farms in Belgium for model validation. The framework replicated the observed seasonal dynamics of infection in cattle on these farms and overall, there was no evidence of systematic under- or over-prediction of faecal egg counts (FECs). However, the model under-predicted the FECs observed on one farm with very young calves, highlighting potential areas of uncertainty that may need further investigation if the model is to be applied to young livestock. The model could be used to drive further research into alternative parasite control strategies such as vaccine development and novel treatment approaches, and to understand GI nematode epidemiology under changing climate and host management

Restricted by borders: trade-offs in transboundary conservation planning for large river systems

Effective conservation of freshwater biodiversity requires accounting for connectivity and the propagation of threats along river networks. With this in mind, the selection of areas to conserve freshwater biodiversity is challenging when rivers cross multiple jurisdictional boundaries. We used systematic conservation planning to identify priority conservation areas for freshwater fish conservation in Hungary (Central Europe). We evaluated the importance of transboundary rivers to achieve conservation goals by systematically deleting some rivers from the prioritization procedure in Marxan and assessing the trade-offs between complexity of conservation recommendations (e.g., conservation areas located exclusively within Hungary vs. transboundary) and cost (area required). We found that including the segments of the largest transboundary rivers (i.e. Danube, Tisza) in the area selection procedure yielded smaller total area compared with the scenarios which considered only smaller national and transboundary rivers. However, analyses which did not consider these large river segments still showed that fish diversity in Hungary can be effectively protected within the country’s borders in a relatively small total area (less than 20 % of the country’s size). Since the protection of large river segments is an unfeasible task, we suggest that transboundary cooperation should focus on the protection of highland riverine habitats (especially Dráva and Ipoly Rivers) and their valuable fish fauna, in addition to the protection of smaller national rivers and streams. Our approach highlights the necessity of examining different options for selecting priority areas for conservation in countries where transboundary river systems form the major part of water resources.Full Tex

Characterising and Predicting Benthic Biodiversity for Conservation Planning in Deepwater Environments

Understanding patterns of biodiversity in deep sea systems is increasingly important because human activities are extending further into these areas. However, obtaining data is difficult, limiting the ability of science to inform management decisions. We have used three different methods of quantifying biodiversity to describe patterns of biodiversity in an area that includes two marine reserves in deep water off southern Australia. We used biological data collected during a recent survey, combined with extensive physical data to model, predict and map three different attributes of biodiversity: distributions of common species, beta diversity and rank abundance distributions (RAD). The distribution of each of eight common species was unique, although all the species respond to a depth-correlated physical gradient. Changes in composition (beta diversity) were large, even between sites with very similar environmental conditions. Composition at any one site was highly uncertain, and the suite of species changed dramatically both across and down slope. In contrast, the distributions of the RAD components of biodiversity (community abundance, richness, and evenness) were relatively smooth across the study area, suggesting that assemblage structure (i.e. the distribution of abundances of species) is limited, irrespective of species composition. Seamounts had similar biodiversity based on metrics of species presence, beta diversity, total abundance, richness and evenness to the adjacent continental slope in the same depth ranges. These analyses suggest that conservation objectives need to clearly identify which aspects of biodiversity are valued, and employ an appropriate suite of methods to address these aspects, to ensure that conservation goals are met

Are We Predicting the Actual or Apparent Distribution of Temperate Marine Fishes?

Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change – particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km2 study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions

Developing Literacy Learning Model Based on Multi Literacy, Integrated, and Differentiated Concept at Primary School

The main issue addressed in this research is the low writing skills of primary school students. One of the reasons for this condition is that the existing model of writing literacy learning is not appropriate. The purpose of this study is to explain MID-based literacy teaching model and the impact of the model in increasing primary school students\u27 writing skills. This study used combined methods of exploratory type. The samples were elementary school students coming from six schools with three different characteristics. Based on the data analysis, it can be concluded that the implementation of MID-based literacy learning model has proven to signi cantly contribute to the improvement of students\u27 writing skills. Taking place in all sample schools, the improvement may suggest that the model ts not only to students with high- ability but also those with low-ability. Therefore, the MID-based literacy learning model is needed to improve the ability to write various text types appropriately

Patterns of deep-sea genetic connectivity in the New Zealand region : implications for management of benthic ecosystems

© The Author(s), 2012. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in PLoS One 7 (2012): e49474, doi:10.1371/journal.pone.0049474.Patterns of genetic connectivity are increasingly considered in the design of marine protected areas (MPAs) in both shallow and deep water. In the New Zealand Exclusive Economic Zone (EEZ), deep-sea communities at upper bathyal depths (<2000 m) are vulnerable to anthropogenic disturbance from fishing and potential mining operations. Currently, patterns of genetic connectivity among deep-sea populations throughout New Zealand’s EEZ are not well understood. Using the mitochondrial Cytochrome Oxidase I and 16S rRNA genes as genetic markers, this study aimed to elucidate patterns of genetic connectivity among populations of two common benthic invertebrates with contrasting life history strategies. Populations of the squat lobster Munida gracilis and the polychaete Hyalinoecia longibranchiata were sampled from continental slope, seamount, and offshore rise habitats on the Chatham Rise, Hikurangi Margin, and Challenger Plateau. For the polychaete, significant population structure was detected among distinct populations on the Chatham Rise, the Hikurangi Margin, and the Challenger Plateau. Significant genetic differences existed between slope and seamount populations on the Hikurangi Margin, as did evidence of population differentiation between the northeast and southwest parts of the Chatham Rise. In contrast, no significant population structure was detected across the study area for the squat lobster. Patterns of genetic connectivity in Hyalinoecia longibranchiata are likely influenced by a number of factors including current regimes that operate on varying spatial and temporal scales to produce potential barriers to dispersal. The striking difference in population structure between species can be attributed to differences in life history strategies. The results of this study are discussed in the context of existing conservation areas that are intended to manage anthropogenic threats to deep-sea benthic communities in the New Zealand region.This work was funded in part by a Fulbright Fellowship administered by Fulbright New Zealand and the U.S. Department of State, awarded in 2011 to EKB. Funding and support for research expedition was provided by Land Information New Zealand, New Zealand Ministry of Fisheries, NIWA, Census of Marine Life on Seamounts (CenSeam), and the Foundation for Research, Science and Technology. Other research funding was provided by the New Zealand Ministry of Science and Innovation project “Impacts of resource use on vulnerable deep-sea communities” (FRST contract CO1X0906), the National Science Foundation (OCE-0647612), and the Deep Ocean Exploration Institute (Fellowship support to TMS)