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Biodiversity monitoring is essential to inform the state of wildlife populations, and the impacts of environmental change, conservation intervention, and sustainable development policies and actions. We review the current state of bird monitoring across Africa using public questionnaires and semi-structured interviews. We received 87 questionnaire responses from 46 (of 54) countries and, additionally, 24 in-depth interviews were carried out. Multiple data collection methods were reported with total counts of individuals being most frequent, but all-species surveys, essential for quantifying ecosystem health, were restricted to bird atlases and Common Bird Monitoring (CBM) projects in Kenya, Uganda, and Botswana. Data collection relied largely on volunteers, but their motivation, recruitment, training, and retention is a continuing challenge. The most sustainable programmes were driven by clear policy objectives (e.g. waterbird monitoring under the Ramsar Convention or the Convention on the Conservation of Migratory Species), monitoring of individual groups (e.g. raptors, vultures, bustards), specific threatened species, and where clear national priorities had been set within government agencies. Use of monitoring data by governments in country biodiversity reports or National Biodiversity Species Action Plans (NBSAPs) varied widely and, for many countries, simply did not exist. A lack of skilled analysts and a comprehensive approach to data curation and ownership were identified as major limitations. A more strategic approach to funding and monitoring is needed, whereby smaller funders collaborate to reduce costs associated with applying for small amounts of money, and bird (and biodiversity) monitoring is explicitly integrated with sustainable development goals to exploit broader funding streams.
The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity.
We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case–control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection.
In UKB, reductions in network efficiency were observed in MDD cases globally (d = −0.076, pFDR = 0.033), across all tiers (d = −0.069 to −0.079, pFDR = 0.020), and in hubs (d = −0.080 to −0.113, pFDR = 0.013–0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample.
Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.
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