Distributions and interactions between indicators with the strongest relationship to differences in perceived management effectiveness in Kenya.

Density plots demonstrating the relative proportion of individuals responding that they believed management was either not effective (left) or effective (right) across different values of: (A) the top three most important indicators (WAMR: Women’s Access to Marine Resources; TM: Trained in Management; WNG: Women’s Nature Groups); (B-D) two-way interactions between the top three indicators; and (E) three-way interactions between all indicators (x-axis: (NE) not effective; (E) effective). Numbers on either side of density plots represent the total number of individuals for each feature or combination of features that believed management was effective (right side) or not effective (left side).</p

Parameter values used in iterative optimization of classification models and final values used for each parameter for each country-specific model.

Parameter values used in iterative optimization of classification models and final values used for each parameter for each country-specific model.</p

Relative importance and distribution of responses for indicators most strongly associated with differences in perceived management effectiveness in Tanzania.

(A) Relative importance of features to differences in perceived effectiveness. The length of solid bars along the x-axis represents the average feature importance for each variable across 100 model iterations, and the color of bars indicates relationships between increases in each metric and more positive perceptions of management effectiveness (blue: positive; red: negative; gray: variable). Error bars represent the standard deviation of importance across model runs. (B) Density plots of individual responses for the top 10 most important metrics, separated by individuals that believed management was effective (yellow) and not effective (purple). Brown areas represent overlapping values between individuals that believed management was effective and not effective.</p

Distributions and interactions between indicators with the strongest relationship to differences in perceived management effectiveness in Tanzania.

Density plots demonstrating the relative proportion of individuals responding that they believed management was either not effective (left) or effective (right) across different values of: (A) the top three most important indicators (IFL: Influence on Governance; IFO: Frequency of Information from Governing Bodies; EDU: Education Level); (B-D) two-way interactions between the top three indicators; and (E) three-way interactions between all indicators (x-axis: (NE) not effective; (E) effective). Numbers on either side of density plots represent the total number of individuals for each feature or combination of features that believed management was effective (right side) or not effective (left side).</p

Gender-specific number of surveys (N) from each site in Kenya and Tanzania that were included in analyses.

The “% Effective” column represents the number of female or male respondents from each site that indicated that they believed management was effective.</p

Predictive metrics used in xgboost classification models for Kenya and Tanzania.

Predictive metrics used in xgboost classification models for Kenya and Tanzania.</p

Location of household surveys in Kenya and Tanzania.

Yellow circles indicate locations where household survey data was collected.</p

Schematic overview of the research approach utilized in this study.

Schematic overview of the research approach utilized in this study.</p

Relative importance and distribution of responses for indicators most strongly associated with differences in perceived management effectiveness in Kenya.

(A) Relative importance of features to differences in perceived effectiveness. The length of solid bars along the x-axis represents the average feature importance for each variable across 100 model iterations, and the color of bars indicates relationships between increases in each metric and more positive perceptions of management effectiveness (blue: positive; red: negative; gray: variable). Error bars represent the standard deviation of importance across model runs. (B) Density plots of individual responses for the top 10 most important metrics, separated by individuals that believed management was effective (yellow) and not effective (purple). Brown areas represent overlapping values between individuals that believed management was effective and not effective.</p

Marine protected and conserved areas in the time of covid

The intersection of potential global targets and commitments for ocean conservation with the COVID-19 pandemic in 2020 has resulted in an opportunity to rethink the future of marine area-based conservation tools, particularly for marine protected and conserved areas (MPCAs). As MPCAs continue to provide essential ecological, social and economic services, current approaches to establishing and managing these areas require an understanding of the factors that drive the pressures they face. We briefly review their status pre-pandemic and provide an overview of the impacts of COVID-19 informed primarily by 15 case studies. Impacts are of two kinds: those affecting livelihoods and well-being of local communities and stakeholders that depend on the MPCA; and those which affect management and governance of the MPCA itself. Responses from managers and communities have addressed: the management of resources; income and food security; monitoring and enforcement; seafood supply chains; and communication amongst managers, community members and other stakeholders. Finally, we discuss innovative approaches and tools for scaling and transformational change, emphasising synergies between management for conservation and management for sustainable livelihoods, and how these relate to the principles of equity and resilience.</p