Biotic homogenization and changes in species diversity across human-modified ecosystems

Changing land use and the spread of ‘winning’ native or exotic plants are expected to lead to biotic homogenization (BH), in which previously distinct plant communities become progressively more similar. In parallel, many ecosystems have recently seen increases in local species (α-) diversity, yet γ-diversity has continued to decline at larger scales. Using national ecological surveillance data for Great Britain, we quantify relationships between change in α-diversity and between-habitat homogenizations at two levels of organization: species composition and plant functional traits. Across Britain both increases and decreases in α-diversity were observed in small random sampling plots (10–200 m(2)) located within a national random sample of 1 km square regions. As α-diversity declined (spatially in 1978 or temporally between 1978 and 1998), plant communities became functionally more similar, but species-compositional similarity declined. Thus, different communities converged on a narrower range of winning trait syndromes, but species identities remained historically contingent, differentiating a mosaic of residual species-poor habitat patches within each 1 km square. The reverse trends in β-diversity occurred where α-diversity increased. When impacted by the same type and intensity of environmental change, directions of change in α-diversity are likely to depend upon differences in starting productivity and disturbance. This is one reason why local diversity change and BH across habitats are not likely to be consistently coupled

Potential use of gene drive modified insects against disease vectors, agricultural pests and invasive species poses new challenges for risk assessment.

Potential future application of engineered gene drives (GDs), which bias their own inheritance and can spread genetic modifications in wild target populations, has sparked both enthusiasm and concern. Engineered GDs in insects could potentially be used to address long-standing challenges in control of disease vectors, agricultural pests and invasive species, or help to rescue endangered species, and thus provide important public benefits. However, there are concerns that the deliberate environmental release of GD modified insects may pose different or new harms to animal and human health and the wider environment, and raise novel challenges for risk assessment. Risk assessors, risk managers, developers, potential applicants and other stakeholders at many levels are currently discussing whether there is a need to develop new or additional risk assessment guidance for the environmental release of GD modified organisms, including insects. Developing new or additional guidance that is useful and practical is a challenge, especially at an international level, as risk assessors, risk managers and many other stakeholders have different, often contrasting, opinions and perspectives toward the environmental release of GD modified organisms, and on the adequacy of current risk assessment frameworks for such organisms. Here, we offer recommendations to overcome some of the challenges associated with the potential future development of new or additional risk assessment guidance for GD modified insects and provide considerations on areas where further risk assessment guidance may be required

Adequacy and sufficiency evaluation of existing EFSA guidelines for the molecular characterisation, environmental risk assessment and post‐market environmental monitoring of genetically modified insects containing engineered gene drives

Advances in molecular and synthetic biology are enabling the engineering of gene drives in insects for disease vector/pest control. Engineered gene drives (that bias their own inheritance) can be designed either to suppress interbreeding target populations or modify them with a new genotype. Depending on the engineered gene drive system, theoretically, a genetic modification of interest could spread through target populations and persist indefinitely, or be restricted in its spread or persistence. While research on engineered gene drives and their applications in insects is advancing at a fast pace, it will take several years for technological developments to move to practical applications for deliberate release into the environment. Some gene drive modified insects (GDMIs) have been tested experimentally in the laboratory, but none has been assessed in small-scale confined field trials or in open release trials as yet. There is concern that the deliberate release of GDMIs in the environment may have possible irreversible and unintended consequences. As a proactive measure, the European Food Safety Authority (EFSA) has been requested by the European Commission to review whether its previously published guidelines for the risk assessment of genetically modified animals (EFSA, 2012 and 2013), including insects (GMIs), are adequate and sufficient for GDMIs, primarily disease vectors, agricultural pests and invasive species, for deliberate release into the environment. Under this mandate, EFSA was not requested to develop risk assessment guidelines for GDMIs. In this Scientific Opinion, the Panel on Genetically Modified Organisms (GMO) concludes that EFSA's guidelines are adequate, but insufficient for the molecular characterisation (MC), environmental risk assessment (ERA) and post-market environmental monitoring (PMEM) of GDMIs. While the MC,ERA and PMEM of GDMIs can build on the existing risk assessment framework for GMIs that do not contain engineered gene drives, there are specific areas where further guidance is needed for GDMI

Effects of farming system and landscape (% arable) on activity density of hunting spiders where system effect is significant.

<p>Organic: solid lines, filled points; conventional: dotted line, open points. Plots in left hand panel: lines in plots are linear regressions plotted on log scale by system, points are farms. Plots in right hand panel: lines in plots are linear regression (solid line) and reference line (dotted at system difference = zero), points are farm pairs. Right hand panel extracts system effect from left hand panel, O-C difference on arithmetic scale.</p

Organic Farming: Biodiversity Impacts Can Depend on Dispersal Characteristics and Landscape Context - Fig 4

<p>(a) Hunting spider activity density and field plant species richness (before harvest). Organic farms shown as solid points. Upward trend in conventional farms (open points, model averaged parameter estimate 0.10, SE = 0.05, P = 0.04), no trend among organic farms (model averaged parameter estimate 0.02, SE = 0.02, P = 0.29). (b) Hunting spider species density and field plant species richness (before harvest). Organic farms shown as solid points. Upward trend in conventional farms (open points, model averaged parameter estimate 0.05, SE = 0.03, P = 0.06), no trend among organic farms (model averaged parameter estimate 0.005, SE = 0.01, P = 0.66).</p

Model averaged parameter estimates (back transformed) for farming system and landscape effects (extent of arable land in landscape).

<p>The responses were square root transformed mean species counts per trap (Species Density, SD), and log mean numbers per trap. Effect size = organic/conventional ratio (LCI = Lower Confidence Interval, UCI = Upper Confidence Interval). Confidence intervals not encompassing zero are emboldened.</p

Effects of farming system and landscape on activity density of web-building spiders, no system effect, and one example plotted.

<p>Organic: solid lines, filled points; conventional: dotted line, open points. Plot in left hand panel: line in plot is linear regression plotted on log scale by system, points are farms. Plot in right hand panel: line in plot is linear regression (solid line) and reference line (dotted at system difference = zero), points are farm pairs. Right hand panel extracts system effect from left hand panel, O-C difference on arithmetic scale.</p

Model selection outputs for responses with significant system effect or system-landscape interaction.

<p>Top 3 models in descending order of Akaike weight given for each such response variable. Predictor variables: 1- arabkm1z (arable in 1 km x 1 km square around farm), 2- arabkm3z (arable in 3 km x 3 km square around farm), 3- calyear (calendar year, 2002,2003), 4- system (organic, conventional), 5- woodkm1z (woodland in 1 km x 1 km square around farm), 6- woodkm3z (woodland in 3 km x 3 km square around farm). Interaction terms: 7- arabkm1z:system, 8- arabkm3z:system, 9- system:woodkm1z, 10- system:woodkm3z. In the presence of system-landscape interaction, landscape models are given for each system separately. Model averaged parameter estimates given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135921#pone.0135921.s001" target="_blank">S1 Table</a>.</p

Effect of farming system and landscape on activity density of carabid beetles.

<p>Organic: solid lines, filled points; conventional: dotted line, open points. Plots in left hand panel: lines in plots are linear regressions plotted on log scale by system, points are farms. Plots in right hand panel: lines in plots are linear regression (solid line) and reference line (dotted at system difference = zero), points are farm pairs. Right hand panel extracts system effect from left hand panel.</p