A picture is worth a thousand words: using digital tools to visualize marine invertebrate diversity data along the coasts of Mozambique and São Tomé & Príncipe

The amount of biological data available in online repositories is increasing at an exponential rate. However, data on marine invertebrate biodiversity resources are still sparse and scattered in these countries. Online repositories are useful instruments for biodiversity research, as they provide a fast access to data from different sources. The use of interactive platforms comprising web mapping are becoming more important not only for the scientific community, but also for conservation managers, decision-makers and the general public as they allow data presentation in simple and understandable visual schemes. The main goal of this study was to create an interactive online digital map (MARINBIODIV Atlas), through the collection of data from various sources, to visualize marine invertebrate occurrences and distribution across different habitats, namely mangroves, seagrasses, corals and other coastal areas, in Mozambique and São Tomé and Príncipe. The acquired biodiversity data were managed and structured to be displayed as spatial data and to be disseminated using the geographic information system ArcGIS, where data can be accessed, filtered and mapped. The ArcGIS web mapping design tools were used to produce interactive maps to visualize marine invertebrate diversity information along the coasts of Mozambique and São Tomé and Príncipe, through different habitats, offering the foundation for analysing species incidence and allocation information. Understanding the spatial occurrences and distribution of marine invertebrates in both countries can provide a valuable baseline, regarding information and trends on their coastal marine biodiversity.

Summer weed species incidence in Western Australia varies between seasons

Agronomic surveys of summer weed species are necessary to identify future research directions for optimal weed control, but usually focus on agricultural fields in a single season. To survey all species in the absence of weed control measures and determine species variability between seasons, a survey of 133 sites was conducted on roadsides adjoining agricultural fields throughout the Western Australian grainbelt in early 2015 and repeated in 2016 and 2017. The survey identified 144 species, but only 19 species were evident at more than 10% of sites. The most common species were weeping lovegrass [Eragrostis curvula (Schrad.) Nees], fleabane (Erigeron sp.), windmillgrass (Chloris truncata R. Br.), and wild radish (Raphanus raphanistrum L). The survey highlighted that weed species incidence varied between years. For example, C. truncata incidence was 30% in 2015 and 55% in 2016, while stinkgrass [Eragrostis cilianensis (All.) Vignolo ex Janch.] ranged from 20% in 2015 to 50% of sites in 2017. Conversely, density of individual species on the roadside was usually low, and density remained consistent between years. The survey highlighted multiple weed species that will require further research to optimize management programs. Raphanus raphanistrum and wild oat (Avena fatua L.) in particular are an issue for growers, as these species are highly detrimental winter weeds, and the survey demonstrates that they can also be common summer weeds. Control of these species with nonselective herbicides in summer as well as winter is likely to exacerbate the development of herbicide resistance.

Inferring global species richness from megatransect data and undetected species estimates

Ratio-like approaches for estimating global species richness have been criticised for their unjustified extrapolation from regional to global patterns. Here we explore the use of cumulative percentages of ‘new’ (i.e., not formally described) species over large geographic areas (‘megatransects’) as a means to overcome this problem. In addition, we take into account undetected species and illustrate these combined methods by applying them to a family of spiders (Pholcidae) that currently contains some 1,700 described species. The raw global cumulative percentage of new species (‘new’ as of the end of 2008, when 1,001 species were formally described) is 75.1%, and is relatively constant across large biogeographic regions. Undetected species are estimated using the Chao2 estimator based on species incidence data (date by species and locality by species matrices). The estimated percentage of new species based on the date by species matrices is 76.0% with an estimated standard error (s.e.) of 2.6%. This leads to an estimated global species richness of about 4,200 with a 95% confidence interval of (3,300, 5,000). The corresponding values based on locality by species matrices are 84.2% (s.e. 3.0%) and 6,300 with a 95% confidence interval of (4,000, 8,600). Our results suggest that the currently known 1,700 species of Pholcidae may represent no more than about 25–40% of the total species richness. The impact of further biasing factors like geography, species size and distribution, cryptic species, and model assumptions needs to be explored.

Can the metacommunity data matrix predict changes in species incidence and abundance?

Metacommunity matrices contain data on species incidence or abundance across sites, compactly portraying community composition and how it varies over sites. We constructed models based on an initial metacommunity matrix of either species incidence or abundance to test whether such data suffice to predict subsequent changes in incidence or abundance at each site. We then tested these models against extensive empirical data on vascular plant incidence and abundance collected from 156 forested sites in both the 1950s and 2000s. Predictions from these models parallel observed changes in species incidence and abundance in two distinctly different forest metacommunities and differ greatly from null model predictions. The abundance model shows greater power than the incidence model reflecting its higher information content. Predictions were more accurate for the more diverse forests of southern Wisconsin which are changing faster in response to succession and fragmentation. Simulations demonstrate that these results are fairly robust to variation in sampling intensity. These models, based only on the metacommunity matrix, do not require data on site conditions or species' characteristics. They thus provide a useful baseline for assessing more complex models incorporating data on species' functional traits, local site conditions, or landscape context. They may also prove useful to conservation biologists seeking to predict local population declines and extinction risks.